Academic literature on the topic 'Future studies; prediction; planning'
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Journal articles on the topic "Future studies; prediction; planning"
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Adeleye Yusuff Adewuyi, Kayode Blessing Adebayo, Damilola Adebayo, et al. "Application of big data analytics to forecast future waste trends and inform sustainable planning." World Journal of Advanced Research and Reviews 23, no. 1 (2024): 2469–78. http://dx.doi.org/10.30574/wjarr.2024.23.1.2229.
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Urbanization and industrialization have caused a substantial rise in garbage production, creating substantial environmental, economic, and social difficulties. Precise prediction of future waste patterns is essential for sustainable waste management and strategic planning. Big Data Analytics (BDA) presents a potential method for examining large quantities of waste-related data, revealing trends, and offering practical insights. This review article examines the utilization of Big Data Analytics (BDA) in predicting future waste patterns, emphasizing its capacity to provide valuable insights for sustainable planning and decision-making. The study examines several techniques, tools, and case studies, and explores the obstacles and future prospects in this new discipline.
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Adeleye, Yusuff Adewuyi, Blessing Adebayo Kayode, Adebayo Damilola, et al. "Application of big data analytics to forecast future waste trends and inform sustainable planning." World Journal of Advanced Research and Reviews 23, no. 1 (2024): 2469–79. https://doi.org/10.5281/zenodo.14811297.
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Urbanization and industrialization have caused a substantial rise in garbage production, creating substantial environmental, economic, and social difficulties. Precise prediction of future waste patterns is essential for sustainable waste management and strategic planning. Big Data Analytics (BDA) presents a potential method for examining large quantities of waste-related data, revealing trends, and offering practical insights. This review article examines the utilization of Big Data Analytics (BDA) in predicting future waste patterns, emphasizing its capacity to provide valuable insights for sustainable planning and decision-making. The study examines several techniques, tools, and case studies, and explores the obstacles and future prospects in this new discipline.
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Ma, Jungmok. "Data driven TRL Transition Predictions for Early Technology Development in Defence." Defence Science Journal 71, no. 6 (2021): 777–83. http://dx.doi.org/10.14429/dsj.71.16771.
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This paper proposes the framework of TRL (Technology Readiness Level) transition predictions for early technology development in defense. Though predicting future TRLs is an important planning tool, it has been studied less actively than the other critical issues on TRL, and previous studies mostly have resorted to domain experts. The proposed framework is data-driven and utilises both explanatory and predictive modelling techniques. As a case study, the proposed framework is applied to real technology development data from DTiMS (Defense Technology InforMation Service) which is identified as a key resource. The result of explanatory modelling shows that the two predictor variables, TRL before R&D and project cost, are statistically significant for future TRLs. Also, popular predictive models are fitted and compared with various performance indices using 10-fold cross validation. The two selected predictive models are linear regression and support vector machine models with the lowest prediction errors.
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Chrispell, John C., Eleanor W. Jenkins, Kathleen R. Kavanagh, and Matthew D. Parno. "Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework." Modelling 2, no. 4 (2021): 753–75. http://dx.doi.org/10.3390/modelling2040040.
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Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather patterns may be. The effort described in this work is geared towards a flexible mathematical and software framework for simulating the impact of meteorological variability on future crop yield. Our framework is data driven and can easily be applied to any location with suitable historical observations. This will enable site-specific studies that are needed for rigorous risk assessments and climate adaptation planning. The framework combines a physics-based model of crop yield with stochastic process models for meteorological inputs. Combined with techniques from uncertainty quantification, global sensitivity analysis, and machine learning, this hybrid statistical–physical framework allows studying the potential impacts of meteorological uncertainty on future agricultural yields and identify the environmental variables that contribute the most to prediction uncertainty. To highlight the utility of our general approach, we studied the predicted yields of multiple crops in multiple scenarios constructed from historical data. Using global sensitivity analysis, we then identified the key environmental factors contributing to uncertainty in these scenarios’ predictions.
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Wang, Renhe. "IoT in Urban Traffic Prediction Development Case Studies and Future Trends." ITM Web of Conferences 70 (2025): 01007. https://doi.org/10.1051/itmconf/20257001007.
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The problem of urban traffic has become quite serious in recent years. This problem seriously affects the daily travel of urban residents and urban safety and brings great challenges to the sustainable development of the transportation system. This paper first briefly summarizes the development process of using the Internet of Things (IoT) to calculate future road traffic. As early as 1999, Kevin Ashton proposed to apply the Internet of Things to traffic, but the application in the traffic field is not mature, such as the early ETC (electronic road pricing system). With the rise of wireless communication technology, vehicle GPS navigation systems began to appear, which can provide real-time traffic information and route planning. Nowadays, various sensors, radio frequency identification devices (RFID), cameras, and other devices are widely used, making real-time data collection and analysis possible. Then it analyzes the current actual successful cases of using the IOT to calculate future road traffic, discusses in detail the promotion of traffic and the advantages of using the Internet of Things compared with the previous traffic, and gives the corresponding data. Finally, the future research direction and development trends are put forward. First, edge computing is introduced into the IOT, and edge computing is mixed with cloud computing to obtain a prediction system with better performance and lower robustness. The second is to optimize the existing network transport layer protocol. Finally, this paper makes a summary.
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Johnson, David R., and Nathan B. Geldner. "Contemporary Decision Methods for Agricultural, Environmental, and Resource Management and Policy." Annual Review of Resource Economics 11, no. 1 (2019): 19–41. http://dx.doi.org/10.1146/annurev-resource-100518-094020.
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Traditional top-down methods for resource management ask first what future conditions will be, then identify the best action(s) to take in response to that prediction. Even when acknowledging uncertainty about the future, standard approaches ( a) characterize uncertainties probabilistically, then optimize objectives in expectation, and/or ( b) develop a small number of representative scenarios to explore variation in outcomes under different policy responses. This leaves planners vulnerable to surprise if future conditions diverge from predictions. In this review, we describe contemporary approaches to decision support that address deep uncertainty about future external forcings, system responses, and stakeholder preferences for different outcomes. Many of these methods are motivated by climate change adaptation, infra-structure planning, or natural resources management, and they provide dramatic improvements in the robustness of management strategies. We outline various methods conceptually and describe how they have been applied in a range of landmark real-world planning studies.
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Knyazeva, Elena N. "The Evolution of Future Science: from the Art of Prediction to Sustainability Science." Chelovek 35, no. 4 (2024): 62–80. http://dx.doi.org/10.31857/s0236200724040046.
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The article shows how methods for studying the future developed, starting from ancient times, when seers and oracles were revered, through utopias, conjectures and projects of the future in the era of modern times and the Enlightenment, to the emergence and development of the modern Futures Studies and Foresight proper, starting from the middle of the 20th century to the present day. The instruments of futures studies have evolved from individually nuanced techniques and the art of guessing the future to science-based methods for assessing possible, multiple and preferable futures. The fundamental differences between prognostic science and foresight are revealed. While prognostic science deals with foreseeing the most likely and unambiguous future, foresight is based on methods of scenario planning and roadmaps building for strategic management and achieving preferred images of the future. Modern foresight instruments are based on their understanding of the fundamental openness of the future, the existence of development alternatives and the construction of scenarios for future development. The most promising and proven in practice basis for futures research and foresight is the modern science of complex systems, since it is on its basis that it is possible to recognize development trends, strategic vision of remote goals and develop methods for soft and effective management of complex technological and social systems. It is this that has become the evidence-based scientific foundation for the implementation of sustainable development goals and for creating sustainable futures.
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Ki, Injong, Hasup Song, Jihyeok Ryu, and Jongpil Jeong. "Production Improvement Rate with Time Series Data on Standard Time at Manufacturing Sites." Applied Sciences 13, no. 19 (2023): 10937. http://dx.doi.org/10.3390/app131910937.
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Amid the changes brought about by the 4th Industrial Revolution, numerous studies have been undertaken to develop smart factories, with a strong emphasis on knowledge-based manufacturing through smart factory construction. Advances in manufacturing data collection, fusion, and mining technologies have significantly bolstered the utilization of knowledge-based manufacturing. Data mining technology is widely employed for facility maintenance and failure prediction. Smart factory operations are pursuing automation and autonomization. Automation of production planning is also essential to achieve automation and autonomy in factory operations, from planning to execution. With the advancement of data mining technology, it is possible to automate production planning for the production planning and prediction of future production through information based on current conditions based on the past. The baseline information generated based on the current situation is suitable for automating short-term operational planning. If we generate time series reference information based on data from the past to the present, we can also automate long-term operation planning. By measuring the results of productivity improvements in mass-produced products from the past to the present and extrapolating them to future products, time series baseline information on production time is generated. If the baseline information is used for long-term planning, it can be used to predict future production capacity and facility shortages. This study presents a methodology and utilization method for calculating the rate of change in production time, which can be applied to production plan prediction and equipment investment capacity forecasting in future factory operations, using historical time series production time data.
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Kim, Youjung, and Galen Newman. "Advancing scenario planning through integrating urban growth prediction with future flood risk models." Computers, Environment and Urban Systems 82 (July 2020): 101498. http://dx.doi.org/10.1016/j.compenvurbsys.2020.101498.
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Kim, Hyeongjun, Hoon Cho, and Doojin Ryu. "Corporate Default Predictions Using Machine Learning: Literature Review." Sustainability 12, no. 16 (2020): 6325. http://dx.doi.org/10.3390/su12166325.
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Corporate default predictions play an essential role in each sector of the economy, as highlighted by the global financial crisis and the increase in credit risk. This study reviews the corporate default prediction literature from the perspectives of financial engineering and machine learning. We define three generations of statistical models: discriminant analyses, binary response models, and hazard models. In addition, we introduce three representative machine learning methodologies: support vector machines, decision trees, and artificial neural network algorithms. For both the statistical models and machine learning methodologies, we identify the key studies used in corporate default prediction. By comparing these methods with findings from the interdisciplinary literature, our review suggests some new tasks in the field of machine learning for predicting corporate defaults. First, a corporate default prediction model should be a multi-period model in which future outcomes are affected by past decisions. Second, the stock price and the corporate value determined by the stock market are important factors to use in default predictions. Finally, a corporate default prediction model should be able to suggest the cause of default.
Dissertations / Theses on the topic "Future studies; prediction; planning"
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Emig, Anne (Anne Nicole). "The future of manufacturing in Somerville." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/67224.
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Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 66-67).<br>As traditional industrial uses in the U.S. have declined, Somerville, Massachusetts has similarly seen a decline in active industrial uses, together with a loss of living wage jobs. Somerville, like many New England cities, is now struggling to establish its identity in this "post-industrial" world. The City's large manufacturers have, for the most part, left the City leaving behind an abundance of old and irregular industrial building stock. Mayor Curtatone is on record as being interested in biotechnology, green, and clean energy businesses and City staff are actively seeking opportunities in these areas. At the same time, the City is increasingly becoming a regional destination for small-scale artisanal and food manufacturing. Products being manufactured locally include bicycles, guitars and chocolate. However, this sector faces many challenges such as limited growth and intense competition. Yet despite these challenges, this sector provides economic development returns by bringing new revenue into Somerville and providing lower skill residents with a higher wage than their alternatives in the retail and restaurant industry. What's more, this sector is appropriate for Somerville's land availability and building stock, and it significantly contributes to Somerville's creative brand and therefore its ability to attract more Creative Class residents and businesses. To bolster this sector, Somerville should embark on sector-specific strategy to strengthen the existing consumer goods sector. The cornerstone of this effort will be the creation of an umbrella organization that will provide technical assistance, marketing, and financial assistance to local manufacturers. To support this work, Somerville will also make infrastructure investments and targeted land use policies. With these policies in place, Somerville will create jobs in Somerville for Somerville residents, extract more value from the existing land, and strengthen its "brand" as a "City of Makers." What can other cities learn about manufacturing as an economic development activity from Somerville? When considering a manufacturing strategy, a city must consider: 1) its role within the region; 2) land availability; 3) existing building stock, and; 4) existing and nascent industry networks. Sound economic development strategies should not only attempt to foster profitable uses (tax revenues), but also uses that leverage a city's relative strengths and are aligned with their larger community and economic development goals including a range of jobs, quality of life, and perhaps most importantly, creating a strong identity and pride of place.<br>by Anne Emig.<br>M.C.P.
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Rojas, Francisca M. 1976. "Cyberpunk visions of the future city." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/69760.
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Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2000.<br>Includes bibliographical references (leaves 112-116).<br>As a future-oriented practice, urban design compels us to imagine, anticipate, design and plan our cities of tomorrow. In fact, 20* century urban planning has generated a number of influential visions of urban futures - from Howard, Le Corbusier, and Wright, to Fuller, Archigram, and Soleri. Yet for more than twenty years, urban planning has exhibited a conspicuous lack of critical projection about the future of urban life and form. This present lack of futurist vision is particularly remarkable when considering the rapid advancements in information technology (IT) that have begun to affect the nature of a wide range of interactions at the various scales of urban life. Of particular interest to urban planning and design is whether or not, and how, IT contributes to the transformation of social and spatial relationships. The future consequences of how IT and other postmodern forces are changing cities have been explored by a subset of science fiction dubbed, Cyberpunk. Writers of cyberpunk fiction have extrapolated the present urban condition to expose a cautionary dystopian vision of cities and urban life in the near-future. Generally, cyberpunks envision the technologically- enhanced future city as an anarchic physical environment of exclusion, sprawl, surveillance, degradation, dematerialization, submission, and resistance. This study examines the images of a potential urban future, with particular attention placed upon the future of urban form, through detailed readings of cyberpunk fiction and film. While a far cry from the communitarian utopias of the previous century that have served as models for the cities we live in today, the cyberpunk vision of the future city does present urban planning and design with cautionary tales from which the profession may begin to examine its current practices and inform its designs for an electronically mediated future.<br>by Francisca M. Rojas.<br>M.C.P.
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Howell, Dwight D. (Dwight Dee). "Army installations of the future : urban + shrinkage + landscape." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97343.
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Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2015.<br>Vita. Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 125-132).<br>The US Army has set a course to transition to a future force that is adaptive, modern, and at the forefront of change. This strategic vision lacks a refined installation strategy to meet the needs of the future force. In a period of troop reductions, declining budgets, and increased facility vacancy rates the Army is required to shrink its installations. This thesis explores how to shrink Army installations through change, policy, and design. A set of changes is proposed that focus on eliminating housing, revising security standards, increasing privatization, and growth in Enhanced Use Leasing. Current Army planning strategies based on New Urbanist principles do not address how to shrink installations. Four theories are analyzed to develop a framework for designing the future of Army installations. Parameters are established to test the results of the design. The framework is applied to develop a design proposal for Fort Belvoir, VA. The framework generated a successful design of Fort Belvoir, VA based on the establish parameters. The framework and design process is transferable to all Army installations in the United States. Army planners can apply the process and framework as a tool to generate solutions to shrink Army installations.<br>by Dwight D. Howell.<br>M.C.P.
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Hunting, Jesse L. "Industrializing housing through factory production : future or fantasy?" Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/49700.
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Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2009.<br>Includes bibliographical references (p. 81-83).<br>The purpose of this thesis is to review and assess the state of factory-built housing in the U.S., and to propose a business plan for a new approach. The thesis addresses the question: What would be a viable factory-based home building strategy for the Mid-Atlantic region of the U.S.? The thesis begins with research on the history and early ethos of the factory-built housing industry in conjunction with an analysis of the factory-built housing industry's current image, advantages, and challenges. To assess the image of the industry, a variety of common perceptions regarding the industry were explored. To isolate the specific advantages and challenges facing the industry, the thesis compares the relative and normalized costs of factory-built homes to site-built homes in addition to macro issues, like building regulations. The thesis also provides a synthesis of the research in the form of a business strategy. The business strategy takes the strengths identified in the initial research and couples them with a viable and forward looking development strategy suited to Pennsylvania's housing market. Key among the proposal's recommendations are using factory production to build secondary homes, like granny flats or summer cottages and marketing the homes as a community. The example given in the business plan includes developing infill retirement communities in small Pennsylvania towns. Other examples could include developing small vacation communities.<br>by Jesse L. Hunting.<br>M.C.P.
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Vandermillen, James P. (James Patrick). "Rediscovering downtown--strategies for the future of Providence." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/74402.
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Moinian, Mahtab Maxene. "Future Earth Catalog : urban design in climate change." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118240.
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Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>What is the agency of urban planning and design in climate change? This project explores new ways of engaging with the environmental narrative of our time. I present Future Earth Catalog as six representations of the same body of research, spanning a spectrum from academic thesis to media object, and catalog to playbook. The project departs from convention, both in process and product, in order to place learning and practice into an unfamiliar territory and form a new dynamic with climate change. This is a work in progress, to see the full project please visit www.futureearthcatalog.com.<br>by Mahtab Maxene Moinian.<br>M.C.P.
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Kim, Myung-hwan. "A past adapts to the future : an old Japanese city will adapt to the future with preserving its past." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/67388.
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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture; and, (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1990.<br>Includes bibliographical references (leaves 151-157).<br>Over the last quarter of a century, historic preservation has become a major component of urban revitalization in the US. Along with the cultural and economic benefits, the restoration of historic structures and environments has also produced a distinct improvement in the quality of life in several American cities. Japan, on the other hand, has only recently begun to recognize the potential contribution of historic preservation to urban revitalization; and, in general, its recent revitalization efforts, especially in several small cities, have not been well formulated or well executed. It is proposed here that small, declining Japanese cities can be revitalized, both economically and culturally, by making effective use of their historic resources. The concept of historic preservation as an urban revitalization tool is first discussed with emphasis on several major theories of preservation in an urban setting. Next, actual instances in which historic preservation was successfully integrated into urban revitalization programs are analyzed. Finally, a revitalization strategy for Otaru, a small declining city in northern Japan, is formulated based on the Urban Cultural Park concept implemented in the city of Lowell, Massachusetts.<br>by Myung-hwan Kim.<br>M.C.P.<br>M.S.
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Rolley, Stephanie. "Suburban urban patterns : the future form of suburban growth corridors." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/77324.
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Lieberman, Todd O. 1978. "Mallville : mixing uses in the shopping center of the future." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28795.
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Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2004.<br>Includes bibliographical references (p. 109-113).<br>(cont.) product allows municipalities an alternative strategy for large scale development than simply consenting to the construction of Wal-Marts and power centers. The alignment of private and public interests in the development of mixed-use projects create the potential for an attractive (and rare) win-win situation for developers and cities, alike, and the potential for lasting economic and social value.<br>Changes in consumer preference, supply and public policy are producing momentum for the introduction of new real estate products into suburbia, calling into question the homogeneous propagation of entrenched forms. Suburbs need to be viewed as what they are in some places: underutilized real estate, underbuilt or emerging neighborhoods-ripe for future morphological alterations and untapped economic opportunities. Capitalizing on the potential to improve underutilized land, public willingness to create a sense of place in the suburbs, and the market demand for lifestyle centers and urban housing, developers have created a new product type that recreates the main street feel of a city--in short "Mallville." These mixed-use products have the potential to achieve returns and product differentiation for developers and property owners in an increasingly competitive retail sector, while simultaneously providing municipalities with social benefit. The point of this thesis is not to prognosticate the demise of the shopping mall like some industry critics contend and the website http://dcadmalls.com has made famous, because in reality well-situated, properly managed malls are still very viable and among the strongest performing asset classes in the real estate universe. Instead, this thesis explores the introduction of mixed-use and the development of town centers in suburbia and the incipient evolution of a new product types within the shopping center universe. Embracing innovative new uses such as hotels, museums, city halls and amphitheaters, urban style housing, open space, streetscape, these projects seek to craft new town centers for homogeneous suburban and low-density urban communities, instilling a "sense of place" where, hitherto, none had existed. The mixed-use<br>by Todd O. Lieberman.<br>M.C.P.
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Gooding, Gates. "Assessing the future : the impacts of development on Steamboat Springs, Colorado." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/59724.
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Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 60-62).<br>The residents of Steamboat Springs, Colorado are continually faced with new propositions for accommodating population growth, but do not possess the proper tools for comparing the potential impacts of different options. In this thesis, I construct four alternative futures for Steamboat Springs, based on realistic potential growth trajectories. Using a visual survey technique and geospatial impact modeling, I then test the impacts of each scenario on five concerns that the community in Steamboat Springs identified as being most important.<br>by Gates Gooding.<br>M.C.P.
Books on the topic "Future studies; prediction; planning"
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Kharbanda, Om Prakash. Corporate failure: Prediction, panacea, and prevention. McGraw-Hill, 1985.
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Ohio. Governor (1991- : Voinovich). Ohio 2000/Ohio first: A practical vision for Ohio's future. Office of the Governor, 1994.
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Alfredo, Bianchini, and Istituto veneto di scienze, lettere ed arti., eds. A future for Venice?: Considerations 40 years after the 1966 flood. U. Allemandi, 2006.
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Svenson, Ola. Time orientation, planning horizons and responsibility into the future. Statens Kärnbränslenämnd, 1988.
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S, Himes Joseph, ed. The South moves into its future: Studies in the analysis and prediction of social change. University of Alabama Press, 1991.
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McCoy, Doris Lee. America's new future: 100 new answers : a glimpse of the future by 100 American decision makers. Morgan James Pub., 2008.
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E, Cawthorne Jon, and Pearson Debra, eds. Navigating the future with scenario planning: A guidebook for librarians. Association of College and Research Libraries, a division of the American Library Association, 2015.
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Mohsen, Mostafavi. A Sustainable Future for Exuma : Environmental Management, Design, and Planning: Engagement Report. Edited by Harvard University. Graduate School of Design, Bahamas National Trust, and Bahamas. Harvard University Graduate School of Design, 2014.
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Hawks, Richard. Your town: Designing its future : a rural community design workshop and follow-up case studies. National Trust for Historic Preservation, 1997.
Find full textBook chapters on the topic "Future studies; prediction; planning"
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Becht, Lukas. "Potentials and Risks of Futurology: Lessons from Late Socialist Poland." In Perspectives on Public Policy in Societal-Environmental Crises. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94137-6_5.
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AbstractIn the decades after 1945, the future gained unprecedented prominence as an object of scientific anticipation and state planning in both capitalist and socialist countries of the Cold War world. In Poland, future studies or futurology emerged in the course of the 1960s in reaction to Western intellectual trends, the post-stalinist political Thaw, as well as the domestic socio-economic situation. The Polish futurology turned out to be one of the most productive, institutionally and personally stable research collectives when compared to other socialist countries. This research community generated various approaches to the problem of how to anticipate the unknown future. This chapter examines three of them: making the future an object of knowledge; subjecting it to conscious (political) control; imagining alternatives to the status quo. Re-examining these historical examples of anticipatory knowledge provides a mirror to discuss our current efforts at predicting and controlling the future.
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Phillips, Fred. "Prediction and Planning." In Science, Technology and Innovation Studies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26165-8_5.
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Gentilal, Nichal, Ariel Naveh, Tal Marciano, et al. "The Impact of Scalp’s Temperature in the Predicted LMiPD in the Tumor During TTFields Treatment for Glioblastoma Multiforme." In Brain and Human Body Modelling 2021. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15451-5_1.
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AbstractTumor Treating Fields (TTFields) is a cancer treatment technique used for glioblastoma multiforme (GBM). It consists in the application of an electric field (EF) in two perpendicular directions alternately by placing transducer arrays on the patient’s scalp. In-vitro studies showed that the higher the electric field in the tumor, the better are the outcomes of the therapy. Therefore, these arrays are strategically placed in positions that can optimize the EF, based on the results of computational simulations. However, due to the required daily usage of this technique, at least 18 hours per day, the temperature of head tissues increases inevitably. To ensure patient’s safety, the temperature of the scalp is monitored and kept around 39.5 °C by changing the injected current, which consequently changes the EF in the tumor. In this work, we studied the impact that accounting for the temperature of the scalp might have in the choice of which layout should be used during TTFields planning. We used both a simplified and a realistic head model in our studies. We solved Laplace’s equation for the electric potential and Pennes’ equation for the temperature distribution using COMSOL Multiphysics. The electric field in the tumor was evaluated using the local minimum power density (LMiPD) both when the temperature of the scalp was considered in treatment planning and when it was not. We concluded that the values of the LMiPD significantly decrease when the temperature is considered. Furthermore, layouts in which two pairs of different arrays are very close to each other lead to the appearance of a common temperature hotspot, and consequently to the most significant variations in the predicted LMiPD values. In future, TTFields treatment planning studies, considering the temperature of the scalp might be beneficial to improve the predictions of treatment effectiveness.
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Piorr, H. P., H. R. Bork, and K. O. Wenkel. "Future Land Use Planning in Northeast German Agro-landscapes." In Ecological Studies. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04504-6_23.
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Eniola, Victor, Kafayat Adeyemi, Mohammed Adamu, et al. "Daily Streamflow Forecasting Using an Enhanced LSTM Neural Network Model." In Modelling the Energy Transition. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-69031-0_8.
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Abstract Oil and gas consumption for power generation has caused irreversible damage to humanity. To address the attendant effects of fossil fuel utilization, renewable energy is a good alternative. International organizations give support to countries in their transition to a green energy future. This implies that the use of renewable energy is widely supported. It is therefore recommended to utilize renewable energy as it is environmentally friendly. One such type of renewables is water energy. Water cycle has streamflow $$\left( {f_{s} } \right)$$ f s as its central component. Having reliable information about future $$f_{s}$$ f s data is essential in hydrological research, as it can help water managers to plan hydropower generation. To also ensure preparedness and mitigation of floods and drought as well as hydropower production planning and management, precise $$f_{s}$$ f s prediction is considered essential. Several modelling methods have been used lately to forecast $$f_{s}$$ f s , namely: physical methods, data-driven approaches such as shallow artificial neural networks (ANNs), and hybrid techniques. Nevertheless, they may not approximate complex relationships as accurate as deep learning techniques. In this study, an innovative deep learning technique based on long short-term memory (LSTM) neural network adapted with data preprocessing algorithm (DPA) is proposed for seasonal $$f_{s}$$ f s forecasting. Considering recent studies on $$f_{s}$$ f s forecasting, one can avow that researchers have been able to employ lag value predictors for future $$f_{s}$$ f s extrapolation, although deep learning techniques can offer good potentials for $$f_{s}$$ f s prediction with complex physical relationship. However, to the best knowledge of the authors, only very few studies have applied LSTM neural network for streamflow forecasting. In addition, there have been attempts to estimate river $$f_{s}$$ f s in Nigeria using some traditional methods though, but the effect of seasonal variation on $$f_{s}$$ f s forecasting has never been investigated in Nigeria. This is the maiden research in Nigeria that considers seasonal variation in LSTM neural network model-based $$f_{s}$$ f s forecasting. Accordingly, the novelty and key contribution of our state-of-the-art research is the development and implementation of a low-cost intelligent deep learning model based on the LSTM neural network enhanced with DPA for day-ahead $$f_{s}$$ f s forecasting. To further demonstrate the $$f_{s}$$ f s modelling capability of our technique, we have examined the performances of two different baseline approaches namely, the linear regression (LinReg) model and the adaptive linear element neural network (ADALINE-NN) model. The results of $$f_{s}$$ f s simulation indicated that the proposed LSTM neural network model has the capability to handle varnishing or exploding gradient conundrum. It is highly robust and steady with better accuracy when configured for 24-hour ahead $$f_{s}$$ f s forecasting. The LSTM neural network model outpaced both baseline approaches as model comparisons showed that it has the highest extrapolative accuracy. It presents the lowest RMSE and MAPE and the best NSME and CoC of [2.73, 1.28] m3/s, [11.16, 8.16] %, [0.91, 0.83], and [0.97, 0.95] for the rainy and dry season respectively. As the results of the LSTM neural network approach are observed to be more stable in general, it can be established that the proposed model is a practical daily $$f_{s}$$ f s forecasting technique for both the rainy and dry season.
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Saaty, Thomas L., and Luis G. Vargas. "Planning the Future of the Social Security System in the United States: An Example in Control." In Prediction, Projection and Forecasting. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7952-0_11.
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White, David, Drew Collins, and Mark Howden. "Drought in Australia: Prediction, Monitoring, Management, and Policy." In Drought Assessment, Management, and Planning: Theory and Case Studies. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3224-8_12.
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Kirby, Peadar, and Tadhg O’Mahony. "Planning Future Pathways: Implications and Outcomes of Scenario Studies." In The Political Economy of the Low-Carbon Transition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62554-6_5.
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Sitta, Fawaz Hazza Abu. "Reinventing Education Strategic Planning and Policymaking Through the Lenses of Futures Thinking." In Gulf Studies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9667-0_7.
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Abstract Educational Institutes plan their strategies and policies on the assumption that tomorrow mirrors today and yesterday, relying heavily on historical data to forecast the future. This approach ignores weak signals and wild cards, limiting their responsiveness to disruptive innovations. Furthermore, these entities operate under the notion of a certain future, with a single strategic plan and no consideration of alternative scenarios. Conversely, Strategic Futures Thinking employs diverse techniques and methods to anticipate plausible discontinuities and surprises. This chapter discusses the core differences between Futures Thinking and Strategic Planning, emphasising that integrating them enhances agility and resilience.
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Li, Kerang, and Xianchao Lin. "Drought in China: Present Impacts and Future Needs." In Drought Assessment, Management, and Planning: Theory and Case Studies. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3224-8_15.
Full textConference papers on the topic "Future studies; prediction; planning"
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G M, Harshitha, Puneeth Kumar, and Rakshitha M. "AI-Driven Financial Insights for Personal Budget Planning: A Smart Approach to Future Expense Prediction." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914721.
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Adams, Graham. "Methodology for Threat Assessment and Mitigation Planning for Pipeline Integrity." In CORROSION 2016. NACE International, 2016. https://doi.org/10.5006/c2016-07573.
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Abstract Engineering Assessments represent one of the most holistic and comprehensive evaluations of a pipeline’s integrity and maintenance history. As per regulations and industry standards, engineering assessments are required to support operational changes to pipelines, including reactivation of a discontinued pipeline, significant increases to operating pressure, or changes in service fluid. In addition, engineering assessments are often included in operational audits – both internal and external – to evaluate the effectiveness of a pipeline’s integrity management program and can provide a basis for planning future inspection and risk mitigation activities. When properly executed, an engineering assessment will validate existing threat and hazard mitigation and will also identify unmanaged threats and areas where little information exists, facilitating improvement to integrity management. Engineering assessments are complex, multidisciplinary reports that require careful planning to ensure that all potential threats to a pipeline’s integrity have been considered and assessed in accordance with industry standards and requirements. This paper describes an in-depth methodology for carrying out engineering assessments on pipelines. It will outline industry best-practices for evaluating threats and provide criteria for planning future integrity management activities based on the assessment’s findings. Several case studies are also presented to highlight principles covered.
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Nasonov, M. A. "Improving the efficiency of preventive maintenance planning in an industrial company." In IV All-Russian (National) Scientific Conference "Achievements of Science and Technology". Krasoyarsk Science & Technology City Hall, 2025. https://doi.org/10.47813/dnit.4.2025.3005.
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The article highlights the relevance of improving the efficiency of preventive maintenance planning in an industrial company. It reviews studies on existing planning methods, identifying their limitations and shortcomings in the context of a dynamic production process. The article provides a characterization of the challenges faced by the studied enterprise, particularly those related to the inefficiencies in maintenance planning. The task of enhancing preventive maintenance planning is set, encompassing organizational, methodological, and instrumental aspects. The article examines solutions based on the implementation of a centralized maintenance management system, dynamic planning, resource allocation optimization, and automated planning processes. A detailed description of the organizational, methodological, and instrumental aspects is provided, along with an analysis of the key characteristics of the applied methods and technologies. The obtained results demonstrate a reduction in unplanned equipment downtime, shorter repair execution times, improved failure prediction accuracy, and increased automation of maintenance processes. The discussion explores future development directions, including further integration of analytical tools and the expansion of artificial intelligence applications for failure prediction and maintenance optimization. The article concludes that a comprehensive approach to preventive maintenance planning, integrating organizational, methodological, and instrumental solutions, significantly improves maintenance efficiency, reduces unplanned downtime, and enhances equipment reliability, thereby ensuring the sustainable development of the enterprise.
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Li, Xiaozhi, Yuhai Wang, and Xingkun Li. "Hierarchical Control Strategy of Predictive Energy Management for Hybrid Commercial Vehicle Based on ADAS Map." In WCX SAE World Congress Experience. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0543.
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<div class="section abstract"><div class="htmlview paragraph">Considering the change of vehicle future power demand in the process of energy distribution can improve the fuel saving effect of hybrid system. However, current studies are mostly based on historical information to predict the future power demand, where it is difficult to guarantee the accuracy of prediction. To tackle this problem, this paper combines hybrid energy management with predictive cruise control, proposing a hierarchical control strategy of predictive energy management (PEM) that includes two layers of algorithms for speed planning and energy distribution. In the interest of decreasing the energy consumed by power components and ensuring transportation timeliness, the upper-level introduces a predictive cruise control algorithm while considering vehicle weight and road slope, planning the future vehicle speed during long-distance driving. The lower-level calculates the future power demand based on the results of speed planning, and a dynamic programming method is utilized to determine the global optimal power distribution rules for the current road and driving condition with the goal of optimal engine fuel consumption. The comparison of simulation and vehicle test results indicates that under the various high-speed cruising conditions with little change in speed range and road slope, the predictive energy management strategy has a significant improvement in fuel saving compared with the rule-based energy management strategy.</div></div>
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Regalla, Srinivasa Prakash, Apoorva Kaushal, and Sarvesh Khetan. "Machine Learning (ML) Based Prediction of Defects in Extrusion-Type Additively Manufactured Parts." In International Conference on Future Technologies in Manufacturing, Automation, Design and Energy. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-8efgqy.
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Additive manufacturing (AM) is gradually occupying a unique place as a viable industrial manufacturing technology for parts with complex geometry and difficult-to-machine materials. The capability of AM to apply to such parts in various sectors of industry, including automotive, aerospace, and medical devices, stems from the layer-by-layer building process and manufacturing directly from computer-aided design (CAD) models. All AM processes share a common set of steps and characteristics but are distinct in the type and form of raw material, energy source, and supply to the target location to build the layers. Based on these differences, the AM processes have been classified into different types: material extrusion-based processes, powder bed fusion (PBF) processes, direct energy deposition (DED) processes, and photo-polymerization processes. All the AM processes induce certain defects in the parts, making the final mechanical properties inferior. A capability to edict the mechanical properties of the parts by identifying defects in layers can help in designing parts and planning for AM to reduce those defects. The present work investigates the feasibility of applying a machine learning (ML) based technique to identify typical AM defects. The idea of transfer learning of a pre-trained network, namely, AlexNet, with a limited-size dataset of defect images generated from the layer images has been studied. A novel yet simple and effective experimental setup has been devised to capture high-resolution images of the layers of the part in a fused deposition modeling (FDM) machine. From these layer images, a total two hundred defect images have been captured and converted into an input image dataset for transfer learning. After suitable modification of the pre-trained network, the training and validation gave an accuracy of about 62%. Two different techniques of hyper-parameter tuning were then conducted, through which the accuracy of training improved to more than 95%. Based on the success achieved, further possible tracks of future research have been suggested.
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Kessy, Dillon, Jose Ignacio Sierra Castro, Jose Chirinos, Giorgio De Paola, and Maria Jose Lopez Perez-Valiente. "How Artificial Intelligence can Guide Marcellus Development." In SPE Eastern Regional Meeting. SPE, 2021. http://dx.doi.org/10.2118/201786-ms.
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Abstract The application of Artificial Intelligence for planning has received increased attention in the energy industry in the past few years, particularly for the increased production efficiency requirements and environmental standards. The objective of this paper is to show the successful integration of production, completion, subsurface and spatial data using machine-learning algorithms to predict production performance for future development wells. The internal Marcellus Business Unit (MBU) well database, populated with data of 500+ historical wells, has been used in this study. Production data, treated as timeseries, has been processed using functional Principal Component Analysis (PCA) to allow removal of outliers and mode detection. Utilizing this data, a suite of machine-learning algorithms has been applied to reconstruct gas production from available and target well data. Uncertainty quantification has been provided for production curves to identify the quality of prediction. During the study, the sensitivity analysis on input variables has been performed iteratively to screen and rank the most important variables for prediction. The workflow, Unconventional Reservoir Assistant (URA), has been implemented in a proprietary cloud-based platform providing the necessary means for data upload, integration, pre-processing, and finally model training and deployment. This allows the user to focus on the evaluation of model output quality, data filter and workspace generation for continuous model testing and improvement. The full well dataset, split into trained and tested data, has been used for prediction as a preliminary guide to where the most prolific areas of development are located. Results were ranked based on production expected by pad and based on normalized performance. The information was then used to compare with type curves and original development order. In parallel, economic evaluation of break-even was performed to rank all future pads. Consequently, integration of the model prediction and breakeven ranking were used to generate the final development order for the MBU. The URA tool has shown preliminary success in predicting production performance for the pilot development area. Multiple case studies have been run achieving blind test results with high accuracy for historical prediction. Results show some dependency of predictor variable ranking on the field development area, providing insight on how subsurface may affect well dynamic behavior. This paper describes how the integration of URA can complement the development workflow for unconventional reservoirs and be used to predict performance based on complex data integration. The methodology used is superior to standard machine learning models providing only production indicators, as it gives the user the possibility to evaluate economics and completion design sensitivity for future well activities. The methodology can be further extended as a proxy model for well schedule optimization in planning or for better insight into well refrac selection.
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Li, Shen, and Feargal Brennan. "Inspection Planning Optimisation for Offshore Energy Structures Using Digital Twin." In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-122381.
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Abstract Inspection and maintenance are integral elements within the structural integrity management of offshore energy structures. This allows the identification and remediation of progressive structural degradation such as fatigue cracks, ensuring fitness-for-service of critical offshore assets throughout their service lifetime. In view of optimising the offshore retrieval/intervention and eventually reducing the life-cycle costs, risk-based inspection and maintenance planning is highly relevant. However, conducting a comprehensive risk assessment requires gaining a thorough knowledge of the as-built design and actual operational profile whereby accurately predicting its performance. Although design specifications and assumptions can serve as guidelines, a high degree of uncertainty may arise due to the disparity between the actual operational profile and the design assumptions. This paper studies the application of digital twin approach whose virtual monitoring feature coupled with fatigue load updating algorithm enables the derivation of more realistic loading spectrum. Probabilistic fracture mechanics-based model is utilised to simulate the fatigue crack growth. An optimisation of future inspections is performed with an objective to minimise the life cycle expected total cost due to fatigue failure, inspection and repair. For demonstration purpose, a case study using an offshore wind support structure is presented to illustrate this digital twin-enabled proposal. Furthermore, areas open future research in this field are discussed.
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Hanamuro, Takahiro, Ken-Ichi Yasue, Yoko Saito-Kokubu, Koichi Asamori, Tsuneari Ishimaru, and Koji Umeda. "Current R & D Activities in the Study on Geosphere Stability." In ASME 2010 13th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2010. http://dx.doi.org/10.1115/icem2010-40018.
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The Japanese islands are located in a tectonically active zone. The scientific base is required for assessing the geosphere stability for long-term isolation of radioactive waste in Japan. JAEA is promoting the establishment of investigation method for geotectonic events affecting geosphere stability and prediction model for the future changes of geological environments, that is necessary for site selection and safety assessment of the HLW geological disposal. For seismicity and faulting, detection techniques for active faults without topographic surface expression, such as using helium isotope ratios in hot spring gases or detection of hydrogen gas, and studies on the assessment of fault evolution have been developed. For volcanism and geothermal activity, heat sources for anomalous geothermal activity in non-volcanic regions are considered. Detection techniques for high-temperature fluids and magma deep underground, using geophysical and geochemical approaches, were constructed. For uplift, denudation and climatic/sea-level changes, a methodology to predict the future topographic change was developed. Also, for dating techniques as an essential part to proceed on these studies, C-14 and Be-10 dating using AMS and (U-Th)/He dating using QMS and ICP-MS have been developed. We are planning the establishment of assessment methods for geosphere stability including assessment of the activity of faults encountered in underground excavations, development of long-term prediction model of volcanism and hydrothermal activities, and hydrogeological analyses considering topographic change.
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Sapač, Klaudija, Simon Rusjan, Nejc Bezak, and Mojca Šraj. "ANALYSIS OF LOW-FLOW CONDITIONS IN A HETEROGENEOUS KARST CATCHMENT AS A BASIS FOR FUTURE PLANNING OF WATER RESOURCE MANAGEMENT." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.20.
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Understanding and prediction of low-flow conditions are fundamental for efficient water resources planning and management as well as for identification of water-related environmental problems. This is problematic especially in view of water use in economic sectors (e.g., tourism) where water-use peaks usually coincide with low-flow conditions in the summer time. In our study, we evaluated various low-flow characteristics at 11 water stations in the non-homogenous Ljubljanica river catchment in Slovenia. Approximately 90% of the catchment is covered by karst with a diverse subsurface, consisting of numerous karst caves. The streams in the remaining part of the catchment have mainly torrential characteristics. Based on daily discharge data we calculated and analyzed values of 5 low-flow indices. In addition, by analyzing hydrograph recession curves, recession constants were determined to assess the catchment’s responsiveness to the absence of precipitation. By using various calculation criteria, we analyzed the influence of individual criteria on the values of low-flow recession constants. Recession curves are widely used in different fields of hydrology, for example in hydrological models, baseflow studies, for low-flow forecasting, and in assessing groundwater storages which are crucial in view of assessing water availability for planning water resources management. Moreover, in the study we also investigated the possible impact of projected climate change (scenario RCP4.5) on low-flow conditions in two sub-catchments of the Ljubljanica river catchment. For the evaluation we used the lumped conceptual hydrological model implemented in the R package airGR. For periods 2011-2040, 2041-2070, and 2071-2100 low-flow conditions were evaluated based on flow duration curves compared with the 1981-2010 period. The lowest discharges at all water stations in the Ljubljanica river catchment occur mostly during the summer months. Our results for the future show that we can expect a decrease of the lowest low-flows in the first two 30-year periods, while in the last one low-flows could increase by approx. 15%. However, the uncertainty/variability of the results is very high and as such should be taken into account when interpreting and using the results. This study demonstrates that evaluation of several low-flow characteristics is needed for a comprehensive and holistic overview of low-flow dynamics. In non-homogeneous catchments with a high karstic influence, the hydrogeological conditions of rivers should also be taken into account in order to adequately interpret the results of low-flow analyses. This proved to be important even in case of neighboring water stations.
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Krusche, Krupali. "Tackling Climate Change: Integrated Planning of Mumbai’s Eastern Waterfront." In 2022 AIA/ACSA Intersections Research Conference. ACSA Press, 2022. http://dx.doi.org/10.35483/acsa.aia.inter.22.13.
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The Intergovernmental Panel on Climate Change (IPCC) AR-6 report from the United Nations spelled out how urbanization has pushed up intense rainfall in cities across South Asia using several scientific pieces of evidence generated on Indian cities. The report builds on an analysis by NASA predicting that several Asian cities on or near the coastline would have to withstand significant sea level rise by 2100. The Eastern Waterfront of Mumbai city is a property mainly under the Mumbai Port Authority (MPA), a large holding for the Indian Navy and other Government activities. This area is predominantly used as a port and has been opened for new development in recent months. As the city grows, this land will play a vital role in the future development of the city. Based on the current first estimates made by the National Aeronautics and Space Administration (NASA) and Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado studies, this area indicates fast deterioration by the liquefaction of soil. Therefore, there is a dire need to study the area in greater detail and create a comprehensive understanding of the impact of sea level rise before new infrastructure is developed that brings more vulnerable people to this site. This paper presents an expanding innovative solutions design exercise and supports a study focusing on the issue and a mitigation strategy through green infrastructure solutions. It will involve the expertise of the University of Notre Dame, the University of Colorado, NASA, and Xylem Water Solutions & Water Technologies. The area’s urban development will house a few hundred people once developed to reduce the impact of coastal flooding and sea level rise.
Reports on the topic "Future studies; prediction; planning"
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Pasupuleti, Murali Krishna. Sustainable and Affordable Global Housing via AI and Advanced Materials. National Education Services, 2025. https://doi.org/10.62311/nesx/rr425.
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Abstract: The global housing crisis demands innovative solutions that balance affordability, sustainability, and technological advancement. This research report explores the transformative role of Artificial Intelligence (AI) and advanced materials in addressing housing shortages, reducing environmental impact, and improving construction efficiency. AI-driven generative design, smart automation, and predictive analytics optimize resource allocation and enhance structural safety, while advanced materials such as 3D-printed composites, self-healing concrete, aerogels, and bio-based materials revolutionize durability and cost-effectiveness. The study examines the economic feasibility, policy frameworks, and global case studies demonstrating the real-world application of these technologies. Through a multidisciplinary approach, this report provides insights into how AI and sustainable materials can drive the future of affordable housing, ensuring scalability, resilience, and environmental responsibility. Keywords: AI-driven housing, advanced materials, sustainable construction, affordable housing, generative design, 3D-printed homes, self-healing concrete, smart automation, predictive analytics, energy-efficient housing, climate-resilient architecture, green building materials, urban planning, housing policy, robotics in construction, digital twins, AI in real estate.
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Leis. L51866 Field Studies to Support SCC Life Prediction Model. Pipeline Research Council International, Inc. (PRCI), 1997. http://dx.doi.org/10.55274/r0010357.
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One objective of this project was to gather and analyze SCC field data on lines being retested for use in assessing the validity of current or future SCC models. The scope of this initial study was limited to colonies of SCC in one valve section of a pipeline that runs from Texas to the northeast of the United States. This valve section had an early history of high pH SCC. The susceptibility since has been controlled through hydrotesting and modifications to the gas compression to meet upstream demand while reducing the discharge temperature. In addition to collecting data to validate models of SCC, data were also developed to evaluate the suitability of a hand-held tool to measure the depth of SCC, because such results can be critical in the use of models in making serviceability and maintenance decisions.
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Ginis, Isaac, Deborah Crowley, Peter Stempel, and Amanda Babson. The impact of sea level rise during nor?easters in New England: Acadia National Park, Boston Harbor Islands, Boston National Historical Park, and Cape Cod National Seashore. National Park Service, 2024. http://dx.doi.org/10.36967/2304306.
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This study examines the potential impact of sea level rise (SLR) caused by climate change on the effects of extratropical cyclones, also known as nor?easters, in four New England coastal parks: Acadia National Park (ACAD), Boston Harbor Islands National Recreation Area (BOHA), Boston National Historical Park (BOST) and Cape Cod National Seashore (CACO). A multi-method approach is employed, including a literature review, observational data analysis, coupled hydrodynamic-wave numerical modeling, 3D visualizations, and communication of findings. The literature review examines previous studies of nor?easters and associated storm surges in New England and SLR projections across the study domain due to climate change. The observational data analysis evaluates the characteristics of nor?easters and their effects, providing a basis for validating the model. Numerical modeling is performed using the Advanced Circulation (ADCIRC) model, coupled with the Simulating Waves in the Nearshore (SWAN) model to simulate storm surges and waves. The model was validated against available observations and demonstrated its ability to simulate water levels, inland inundation, and wave heights in the study area with high accuracy. The validated model was used to simulate three powerful nor?easters (April 2007, January 2018, and March 2018) and each storm was simulated for three sea levels, (1) a baseline mean sea level representative of the year 2020, as well as with a (2) 1 ft of SLR and (3) 1 m of SLR. Analysis of the model output was used to assess the vulnerability of the parks to nor?easters by examining peak impacts in the park areas. Additional simulations were conducted to evaluate the role of waves in predicting peak water levels and the impact of inlet configurations on storm surges within coastal embayments behind the barrier beach systems in the southern Cape Cod region. The project developed maps, three-dimensional visualizations, and an interpretive film to assist the parks in planning for resource management, maintenance, emergency management, visitor access, safety, education, and outreach. These tools provide a better understanding of the potential impacts of nor?easters and SLR and enable the parks to better prepare for future storms.
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Bhurtyal, Sanjeev, Hieu Bui, Sarah Hernandez, et al. Prediction of waterborne freight activity with Automatic Identification System using machine learning. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49794.
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This paper addresses latency issues related to publicly available port-level commodity tonnage reports. Predicting commodity tonnage at the port-level, near real time vessel tracking data is used with historical WCS with a machine learning model. Commodity throughput is derived from WCS data which is released publicly approximately two years after collection. This latency presents a challenge for short-term planning and other operational uses. This study leverages near real time vessel tracking data from the AIS data set. LSTM, TCN, and TFT machine learning models are developed using the features extracted from AIS and the historical WCS data. The output of the model is the prediction of the quarterly volume of commodities at port terminals for four quarters in the future. Uncategorized and Categorized models were developed. The uncategorized outperformed the categorized based on the Mean Absolute Percentage Error. The uncategorized LSTM model has the highest accuracy. Results show the model has higher accuracy for port terminals that handle a specific type of vessel, compared to the port terminals handling more than one vessel type. The application of the model enables port authorities and stakeholders to make short-term capacity expansion and infrastructure investment decisions based on commodity volume.
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Izulain, Ane, Mari Jose Aranguren, and James Wilson. Futures studies in governments: international models and their potential in the Basque Country. Edited by Patricia Canto. Universidad de Deusto, 2025. https://doi.org/10.18543/hwys5620.
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In the current environment of high levels of uncertainty, volatility, rapid change, interdependence and hyperconnectivity, traditional planning approaches are insufficient, and futures domain is emerging as a growing discipline to support governments and policymakers in building a more desirable future. This notebook brings together six international best practices in the integration of foresight in governments, and proposes a framework for reflection for those public institutions that want to implement and mainstream this transdisciplinary approach in their apparatus.
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Cushing, Janet, Shawn Komlos, Tomma Barnes, Charles Theiling, and Elizabeth Murray. Incorporating ecosystem goods and services (EGS) into US Army Corps of Engineers (USACE) project planning : a retrospective analysis. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49519.
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Ecosystem goods and services (EGS) have been promoted as a way to effectively examine trade-offs and improve communication of project- related environmental outcomes in terms of human well-being. Notably, EGS provide a construct that seems capable of enhancing the capacity to communicate with stakeholders about how ecosystem restoration and rehabilitation activities can affect them—and in ways that are more meaningful to the public than the habitat metrics currently employed. The concept of EGS is not new to the US Army Corps of Engineers (USACE) Civil Works Program. This document presents a review of past attempts to apply EGS assessment techniques in the context of USACE project planning and then identifies obstacles met in those efforts that could be avoided in the future. This report is not intended to showcase approaches to consider EGS in planning studies. Rather, this paper uses case studies to illustrate the challenges of considering ecosystem services in the context of planning studies. These challenges will need to be addressed in any future applications of EGS assessments to USACE Civil Works Program decision-making.
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Cartwright, Nancy, Lucy Charlton, Matt Juden, Tamlyn Munslow, and Richard Beadon Williams. Making predictions of programme success more reliable. Centre for Excellence and Development Impact and Learning (CEDIL), 2020. http://dx.doi.org/10.51744/cmwp1.
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This paper provides an account of how a ‘causal–process–tracing theory of change’ can be very helpful for programme prediction, planning and evaluation. The paper uses case studies as running examples to illustrate how this type of detailed theory of change would be built.
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Aventin, Áine, Martin Robinson, Jennifer Hanratty, et al. Involving men and boys in family planning is effective in increasing contraceptive use. Centre for Excellence and Development Impact and Learning (CEDIL), 2023. http://dx.doi.org/10.51744/ceb6.
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Involving men and boys as both users and supporters of family planning is now considered essential for maternal and child health. It is recognised that men are the primary decisionmakers on family size in many countries and may control or inhibit women’s use of family planning. Men may also have unmet needs in relation to family planning. Evidence on how to engage men and boys to meet family planning needs is therefore important. It is equally important that family planning programmes and services engage men and boys in ways that support women’s and girls’ choices, as well as men’s own family planning needs. This brief shares learning from a systematic review of programme evaluation studies is about how to enhance future programming with men and boys to better meet the needs for family planning for women and men in low- and middle-income countries.
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Upadhyaya, Shrini, Dan Wolf, William J. Chancellor, Itzhak Shmulevich, and Amos Hadas. Traction-Soil Compaction Tradeoffs as a Function of Dynamic Soil-Tire Interation Due to Varying Soil and Loading Conditions. United States Department of Agriculture, 1995. http://dx.doi.org/10.32747/1995.7612832.bard.
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The objectives of this study were to investigate soil-pneumatic tire interaction and develop traction-soil compaction prediction model. We have developed an inverse solution technique that employs a response surface methodology to determine engineering properties of soil in-situ. This technique is useful in obtaining actual properties of soil in-situ for use in traction and soil compaction studies rather than using the values obtained in the laboratory by employing remolded and/or disturbed soil samples. We have conducted extensive field tests i the U.S. to develop semi-empirical traction prediction equation for radial ply tires. A user friendly traction-soil compaction program was developed to predict tractive ability of radial ply tires using several different techniques and to estimate soil compaction induced by these tires. A traction prediction model that incorporates strain rate effects on the tractive ability of tires was developed in Israel. A mobile single wheel tester and an in-situ soil test device were developed i Israel to significantly enhance the ability of Israeli investigators to conduct traction-soil compaction research. This project has resulted in close cooperation between UCD, Technion, and ARO, which will be instrumental in future collaboration.
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Shadurdyyev, G. Analysis of sets of factors affecting the variable flow of the Amu Darya River to create a seasonal prognostic model. Kazakh-German University, 2022. http://dx.doi.org/10.29258/dkucrswp/2022/53-72.eng.
Full textAbstract:
The Amu Darya River is a transboundary river whose flow of the river in high-water years reaches up to 108 km3 and in low-water years up to 47 km3 and these are huge fluctuations in the water flow of the river for Tajikistan, Kyrgyzstan, Uzbekistan, Turkmenistan, and Afghanistan, that share water among themselves. The point to consider is that the downstream countries Turkmenistan and Uzbekistan (and possibly Afghanistan in the future) use a lot of water for irrigation, and therefore these countries are the ones most in need of an accurate forecast of the volume of water for the upcoming season. An accurate forecast of the volume of water on the seasonal scale is necessary for better planning of the structure of crops, and subsequently water use in the irrigation of crops. An acceptable solution to this challenge is the construction of an empirical time series model that will be used to predict the seasonal flows of the Amu Darya River to improve the planning and management of water resources in downstream countries. This article considers three important discharge time series in the larger Amu Darya Basin. These include the Kerki Gauge on the Amu Darya, Darband Gauge on Vaksh River and Khorog Gauge on Gunt River. Long-term time series from these stations are available for the study of the development and implementation of time-series based models for the prediction of discharge in the basin. At this stage, we attempt to demonstrate a proof-of-concept which can in a second step convince stakeholders to share such type of discharge data operationally for more effective water allocation between sectors and countries. All our work was carried out with the quantitative tools R/RStudio and QGIS. It can serve as a stepping stone for more complex forecasting models in the future.