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Journal articles on the topic 'Future studies; prediction; planning'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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

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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

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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

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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. Thi

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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 co

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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 appr

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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 fundame

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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

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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 m

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Shrinivas, Jagtap, Sai Manoj Pasupuleti Venkata, and Goud Myadaboyina Srinidhi. "Reinforcement Learning for Supply Chain Optimization: AI-Driven Demand Forecasting and Logistics Planning." Global Journal of Engineering and Technology [GJET] 4, no. 2 (2025): 07–09. https://doi.org/10.5281/zenodo.14964042.

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<em>Supply chain optimization is essential for enhancing efficiency, reducing costs, and improving customer satisfaction. This paper explores the application of reinforcement learning (RL) in supply chain management, particularly in demand forecasting and logistics planning. We discuss RL frameworks, methodologies, and advantages over traditional methods. Empirical studies demonstrate how RL-based models dynamically adapt to uncertainties, improving demand prediction accuracy and logistics efficiency. The paper also outlines challenges and future research directions. Furthermore, we present re

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Anan, Muhammad, Khalid Kanaan, Driss Benhaddou, et al. "Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data." Energies 17, no. 24 (2024): 6451. https://doi.org/10.3390/en17246451.

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Accurate energy consumption prediction in commercial buildings is a challenging research task. Energy prediction plays a crucial role in energy efficiency, management, planning, sustainability, risk management, diagnosis, and demand response. Although many studies have been conducted on building energy predictions, the impact of occupancy on energy prediction models for office-type commercial buildings remains insufficiently explored, despite its potential to improve energy efficiency by 20%. This study investigates energy prediction using a Long Short-Term Memory (LSTM) model that incorporate

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Kou, Jing, Jinjie Wang, Jianli Ding, and Xiangyu Ge. "Spatial Simulation and Prediction of Land Use/Land Cover in the Transnational Ili-Balkhash Basin." Remote Sensing 15, no. 12 (2023): 3059. http://dx.doi.org/10.3390/rs15123059.

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Exploring the future trends of land use/land cover (LULC) changes is significant for the sustainable development of a region. The simulation and prediction of LULC in a large-scale basin in an arid zone can help the future land management planning and rational allocation of resources in this ecologically fragile region. Using the whole Ili-Balkhash Basin as the study area, the patch-generating land use simulation (PLUS) model and a combination of PLUS and Markov predictions (PLUS–Markov) were used to simulate and predict land use in 2020 based on the assessment of the accuracy of LULC classifi

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Virtriana, Riantini, Akhmad Riqqi, Tania Septi Anggraini, et al. "Development of Spatial Model for Food Security Prediction Using Remote Sensing Data in West Java, Indonesia." ISPRS International Journal of Geo-Information 11, no. 5 (2022): 284. http://dx.doi.org/10.3390/ijgi11050284.

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The food crisis is a problem that the world will face. The availability of growing areas that continues to decrease with the increase in food demand will result in a food crisis in the future. Good planning is needed to deal with future food crises. The absence of studies on the development of spatial models in estimating an area’s future food status has made planning for handling the food crisis suboptimal. This study aims to predict food security by integrating the availability of paddy fields with environmental factors to determine the food status in West Java Province. Food status modeling

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Shankar, Lakshmi, and Krishnamoorthy Arasu. "Deep Learning Techniques for Air Quality Prediction: A Focus on PM2.5 and Periodicity." Migration Letters 20, S13 (2023): 468–84. http://dx.doi.org/10.59670/ml.v20is13.6477.

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The rapid increase in traffic, urbanization, and industrial expansion has all contributed to a decrease in air quality, which has a vital impact on both the long-term feasibility of the environment and the health of humans, particularly in industrialized nations. Numerous studies have explored using machine learning for air quality forecasting to reduce pollution. While shallow machine learning architectures offer less accurate forecasts, deep learning, a recent advancement in computational intelligence, has immense potential in predicting air quality. Deep learning frameworks can identify int

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Al Ahmed, R. T., and E. Y. Al Najjar. "Prediction of the Nutritional Gap of Table Eggs in Iraq for (2025-2040) Measurement Study." IOP Conference Series: Earth and Environmental Science 1449, no. 1 (2025): 012182. https://doi.org/10.1088/1755-1315/1449/1/012182.

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Abstract One of the most important objectives of the economic quantitative studies is conducting the prediction as it is a vital responsibility of the decision makers in the planning process so that future economic policies can be drawn accurately. The good planning process depends on the degree of accuracy of the future predictions so as to decrease the risk degree in terms of making future decisions., there are many scientific methods used for generating the economic predictions; most important of which is (Box-Jenkins model) (ARIMA) Model. This method was used to predict the food gap of the

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Wang, Yiwei, Yuhao Zhang, Yanbing Zhou, and Wenjin Tang. "Road Condition Prediction Based on ARIMA Algorithm." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 403–10. http://dx.doi.org/10.54097/zwypra61.

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This study investigates a road condition prediction method based on autoregressive integral moving average model (ARIMA) and its application in road traffic management and planning. With the acceleration of urbanization and the increasing demand for transportation, effective road condition prediction is crucial for traffic management and planning. This paper first introduces the challenges faced in the field of road transportation. Then, the principles and applications of the ARIMA algorithm in road condition prediction are elaborated, focusing on its advantages in capturing trends and cyclica

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"The Evolving Role Of Artificial Intelligence In Neurosurgical Planning And Outcome Prediction: A MetaAnalysis." IOSR Journal of Dental and Medical Sciences 23, no. 11 (2024): 39–41. http://dx.doi.org/10.9790/0853-2311023941.

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Artificial Intelligence (AI) is transforming various medical fields, including neurosurgery. This review explores the evolving role of AI in neurosurgical planning and outcome prediction, with a focus on its applications in preoperative imaging, intraoperative navigation, and postoperative outcome forecasting. A meta-analysis of recent studies shows that AI enhances precision, reduces errors, and offers personalized patient care. However, significant challenges such as interpretability and integration into clinical practice remain. This article discussescurrent developments, limitations, and f

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Liu, Jincheng, Chengyu Yue, Chenyang Pei, Xuejian Li, and Qingfeng Zhang. "Prediction of Regional Forest Biomass Using Machine Learning: A Case Study of Beijing, China." Forests 14, no. 5 (2023): 1008. http://dx.doi.org/10.3390/f14051008.

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Dynamic changes in forest biomass are closely related to the carbon cycle, climate change, forest productivity and biodiversity. However, most previous studies mainly focused on the calculation of current forest biomass, and only a few studies attempted to predict future dynamic changes in forest biomass which obtained uncertain results. Therefore, this study comprehensively considered the effects of multi-stage continuous survey data of forest permanent sample plots, site condition factors and corresponding meteorological factors using Beijing as an example. The geographic detector method was

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Roumpani, Flora. "Procedural Cities as Active Simulators for Planning." Urban Planning 7, no. 2 (2022): 321–29. http://dx.doi.org/10.17645/up.v7i2.5209.

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Modelling a 3D city poses an interesting challenge. To create a virtual city, a road pattern has to be designed and a large number of buildings need to be generated. Every urban place has a road network, often a superimposed pattern plan that serves a population density and buildings which follow statutory rules. This patterned behaviour of the city is why it is possible to develop rules or “computational instructions,” to generate city models. In this article, we are going to discuss how to use procedural modelling and CityEngine, a rule-based application commonly used in the movie industry a

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Abdel-Mooty, Moustafa Naiem, Wael El-Dakhakhni, and Paulin Coulibaly. "Data-Driven Community Flood Resilience Prediction." Water 14, no. 13 (2022): 2120. http://dx.doi.org/10.3390/w14132120.

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Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towar

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Tsai, Wen-Chang, Chih-Ming Hong, Chia-Sheng Tu, Whei-Min Lin, and Chiung-Hsing Chen. "A Review of Modern Wind Power Generation Forecasting Technologies." Sustainability 15, no. 14 (2023): 10757. http://dx.doi.org/10.3390/su151410757.

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction

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Bieck, Richard, Katharina Heuermann, Markus Pirlich, Juliane Neumann, and Thomas Neumuth. "Language-based translation and prediction of surgical navigation steps for endoscopic wayfinding assistance in minimally invasive surgery." International Journal of Computer Assisted Radiology and Surgery 15, no. 12 (2020): 2089–100. http://dx.doi.org/10.1007/s11548-020-02264-2.

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Abstract Purpose In the context of aviation and automotive navigation technology, assistance functions are associated with predictive planning and wayfinding tasks. In endoscopic minimally invasive surgery, however, assistance so far relies primarily on image-based localization and classification. We show that navigation workflows can be described and used for the prediction of navigation steps. Methods A natural description vocabulary for observable anatomical landmarks in endoscopic images was defined to create 3850 navigation workflow sentences from 22 annotated functional endoscopic sinus

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Bokde, Neeraj, Andrés Feijóo, Daniel Villanueva, and Kishore Kulat. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction." Energies 12, no. 2 (2019): 254. http://dx.doi.org/10.3390/en12020254.

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Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of

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Beneciuk, Jason M., Mark D. Bishop, and Steven Z. George. "Clinical Prediction Rules for Physical Therapy Interventions: A Systematic Review." Physical Therapy 89, no. 2 (2009): 114–24. http://dx.doi.org/10.2522/ptj.20080239.

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Background and Purpose Clinical prediction rules (CPRs) involving physical therapy interventions have been published recently. The quality of the studies used to develop the CPRs was not previously considered, a fact that has potential implications for clinical applications and future research. The purpose of this systematic review was to determine the quality of published CPRs developed for physical therapy interventions. Methods Relevant databases were searched up to June 2008. Studies were included in this review if the explicit purpose was to develop a CPR for conditions commonly treated b

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Wang, Haiqiao, Li Shang, Decai Tang, and Zhijiang Li. "Research Themes, Evolution Trends, and Future Challenges in China’s Carbon Emission Studies." Sustainability 16, no. 5 (2024): 2080. http://dx.doi.org/10.3390/su16052080.

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A profound analysis of China’s research achievements in the realm of carbon emissions holds the potential to furnish insightful references for analogous endeavors and inquiries in other nations. Employing the CiteSpace tool, this paper identifies five major focal points in Chinese scholars’ research on carbon emissions: carbon emission computation and prediction, influencing factors of carbon emissions, carbon footprint, carbon emission efficiency, and differential analysis of carbon emissions. Subsequently, this article systematically scrutinizes and dissects the outcomes of Chinese scholars’

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Kim, Youjung, Galen Newman, and Burak Güneralp. "A Review of Driving Factors, Scenarios, and Topics in Urban Land Change Models." Land 9, no. 8 (2020): 246. http://dx.doi.org/10.3390/land9080246.

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Due to the increase in future uncertainty caused by rapid environmental, societal, and technological change, exploring multiple scenarios has become increasingly important in urban planning. Land Change Modeling (LCM) enables planners to have the ability to mold uncertain future land changes into more determined conditions via scenarios. This paper reviews the literature on urban LCM and identifies driving factors, scenario themes/types, and topics. The results show that: (1) in total, 113 driving factors have been used in previous LCM studies including natural, built environment, and socio-ec

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Sulistyo, Totok, Sari Kusumayudha, Agung Cahyadi, and Reza Fajar. "Future research topic prospect dealing with “flood severity” term: A systematic literature review." Journal of the Geographical Institute Jovan Cvijic, SASA, no. 00 (2025): 6. https://doi.org/10.2298/ijgi240903006s.

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Most recent flood prediction studies focus on the probability and frequency of a flood at a specific location or flood vulnerability prediction. However, their results often lack flood magnitude or severity information. Therefore, severity levels are highly imperative for further research in floods, such as their mapping and prediction. This study has involved various stages, such as developing the literature selection protocol in obtaining the expected papers, searching the literature by protocol implementations, and results interpretation. The search results were 537 articles; the selected r

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Gold, Michael A. "PET in Cervical Cancer — Implications for `Staging,' Treatment Planning, Assessment of Prognosis, and Prediction of Response." Journal of the National Comprehensive Cancer Network 6, no. 1 (2008): 37–45. http://dx.doi.org/10.6004/jnccn.2008.0004.

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The use of functional imaging techniques such as fluorodeoxyglucose-positron emission tomography and positron emission tomography–computed tomography to manage patients with cervical cancer is constantly increasing. Current roles include pretreatment staging and diagnosis of recurrent disease. Reports have also shown its ability to predict survival based on pre- and posttherapy scans. These techniques are not fool-proof, however, and reports of both false-negative and false-positive scans document some limitations. Future studies must further elucidate their exact roles in the management of th

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Shimizu, Keita, Tadashi Yamada, and Tomohito J. Yamada. "Uncertainty Evaluation in Hydrological Frequency Analysis Based on Confidence Interval and Prediction Interval." Water 12, no. 9 (2020): 2554. http://dx.doi.org/10.3390/w12092554.

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The shortage of extreme rainfall data gives substantial uncertainty to design rainfalls and causes predictions for torrential rainfall to deviate strongly from adopted probability distributions used in river planning. These torrential rainfalls are treated as outliers which existing studies do not evaluate. However, probability limit method test which its acceptance region expresses with high accuracy the range where observed ith order statistics could realize. Confidence interval which quantifies uncertainty of adopted distributions can be constructed by assuming that these critical values in

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Wu, Zhenjing, Min Qi, Weiling Zhang, et al. "The Electricity Load Prediction Model for Residential Buildings: A Critical Review of Output Types, Prediction Methods and Driving Factors." Buildings 15, no. 6 (2025): 925. https://doi.org/10.3390/buildings15060925.

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An electrification revolution in the Chinese building energy field has been promoted by China’s carbon peak and carbon neutrality goals. An accurate electricity load prediction was essential to resolve the conflict between substations which was caused by the current increase in energy demand, on both the generation and consumption sides. This review provided an in-depth study of prediction models for residential building electricity load and inspected various output types, prediction methods and driving factors. The prediction types were divided into three categories: (i) time scale, (ii) geog

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Drozdowska, Bogna A., Kris McGill, Michael McKay, Roisin Bartlam, Peter Langhorne, and Terence J. Quinn. "Prognostic rules for predicting cognitive syndromes following stroke: A systematic review." European Stroke Journal 6, no. 1 (2021): 18–27. http://dx.doi.org/10.1177/2396987321997045.

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Purpose Stroke survivors are at high risk of developing cognitive syndromes, such as delirium and dementia. Accurate prediction of future cognitive outcomes may aid timely diagnosis, intervention planning, and stratification in clinical trials. We aimed to identify, describe and appraise existing multivariable prognostic rules for prediction of post-stroke cognitive status. Method We systematically searched four electronic databases from inception to November 2019 for publications describing a method to estimate individual probability of developing a cognitive syndrome following stroke. We ext

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Mosselman, Erik. "Studies on River Training." Water 12, no. 11 (2020): 3100. http://dx.doi.org/10.3390/w12113100.

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This editorial regards a Special Issue of Water on river training. It introduces five papers in a framework of history, fundamentals, case studies and future. Four papers result from decades of experience with innovation, planning, design and implementation of river training works on rivers in Colombia, the Rhine branches in the Netherlands and the Brahmaputra-Jamuna River in Bangladesh. A fifth paper reviews the state-of-the-art in predicting and influencing the formation and behavior of river bars. The editorial argues that the future lies in more flexible river training, using a mix of inno

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Kulisz, Monika, and Justyna Kujawska. "Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling." Sustainability 12, no. 23 (2020): 10088. http://dx.doi.org/10.3390/su122310088.

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Planning is a crucial component of short- and long-term municipal waste management. Establishing the relationships between the factors that determine the amount of waste generated by municipalities and forecasting the waste management needs plays a fundamental role in the development of effective planning strategies and implementation of sustainable development. Artificial Neural Network employed for verifying the forecasts pertaining to the amount of rainfall in Poland were presented in the studies. The proposed models included selected explanatory indices in order to reflect the impact of so

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Asmara, Y. P., and Tedi Kurniawan. "Corrosion Prediction for Corrosion Rate of Carbon Steel in Oil and Gas Environment: A Review." Indonesian Journal of Science and Technology 3, no. 1 (2018): 64. http://dx.doi.org/10.17509/ijost.v3i1.10808.

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Corrosion predictions are essential for corrosion and material engineers. It is used to prepare pre-Front End Engineering Design (pre-FEED). FEED guides to select appropriate materials, planning test schedule, work over management, and estimate future repair for cost analyses. Corrosion predictions also calculate life of pipeline and equipment systems during operational stages. As oil and gas environments are corrosive for carbon steel, it is important to account the corrosion rate of carbon steels in those environmental conditions. There are many existing corrosion predictions software, which

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Guo, Chuangle, and Wei Shang. "Tourist Demand Prediction Model Based on Improved Fruit Fly Algorithm." Security and Communication Networks 2021 (June 5, 2021): 1–11. http://dx.doi.org/10.1155/2021/3411797.

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To accurately predict the development and change trend of the future, tourism market can effectively improve the planning and purpose of tourism development. In order to improve the accuracy of tourist demand prediction, this paper studies the tourist demand prediction model based on improved fruit fly algorithm. Aiming at the optimization defects of the traditional fruit fly optimization algorithm (FOA), the model introduces two concepts of sensitivity and pheromone, improves the optimization strategy and position replacement of fruit fly, improves the diversity of fruit fly population, modif

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Liu, Xiaoyi. "Prediction of Olympic Medal Counts Based on Multilevel Negative Binomial Regression Model and Bayesian Methods." Highlights in Science, Engineering and Technology 140 (May 23, 2025): 422–28. https://doi.org/10.54097/nqed0206.

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The aim of this study is to accurately predict the number of medals for each country in the 2028 Summer Olympics in Los Angeles by constructing a multilevel negative binomial regression model combined with a Bayesian approach. This study focuses on predicting the medal count for each country in the 2028 Summer Olympics in Los Angeles by constructing a multilevel negative binomial regression model. The model integrates factors such as historical medal data, economic and demographic indicators, and the host country effect. A Bayesian approach, specifically the Markov Chain Monte Carlo (MCMC) met

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Dong, Shuyun, Wayne J. Stephenson, Sarah Wakes, Zhongyuan Chen, and Jianzhong Ge. "Mesoscale simulation of typhoon-generated storm surge: methodology and Shanghai case study." Natural Hazards and Earth System Sciences 22, no. 3 (2022): 931–45. http://dx.doi.org/10.5194/nhess-22-931-2022.

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Abstract. The increasing vulnerability of coastal megacities to storm surge inundation means both infrastructure and populations are subject to significant threat. Planning for further urban development should include consideration of the changing circumstances in coastal cities to ensure a sustainable future. A sustainable urban plan relies on sound preparedness and prediction of future climate change and multiple natural hazards. In light of these needs for urban planning, this paper develops a general method to simulate typhoon-generated storm surge at the mesoscale (1–100 km in length). Me

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Gupta, Soma, Satarupa Mohanty, and Dayal Kumar Behera. "AI-based Yield Prediction: A Thorough Review." Indian Journal Of Science And Technology 18, no. 10 (2025): 822–38. https://doi.org/10.17485/ijst/v18i10.175.

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Background: Traditional farming practices often rely on conventional methods and anecdotal knowledge, but accurate crop yield prediction is crucial for strategic decision-making in agriculture, including import-export strategies and financial planning. Machine learning, a subset of artificial intelligence, offers a data-driven approach that can improve yield prediction by considering multiple factors. ML models can be either explanatory, analyzing past events, or predictive, forecasting future outcomes. Effective feature selection and data preprocessing are essential for accurate ML-based yiel

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Hudays, Ali, Nicholas K. Schiltz, Mohammed Alrashidi, et al. "Machine Learning Models for Predicting Nurse Turnover and Turnover Intention: A Systematic Review." Saudi Journal of Nursing and Health Care 8, no. 06 (2025): 148–62. https://doi.org/10.36348/sjnhc.2025.v08i06.003.

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Early prediction of nurses’ turnover and turnover intention is essential to enhancing staff retention, ensuring job satisfaction, and maintaining the quality of patient care. This systematic review evaluated studies that used machine learning techniques to predict either actual nurse turnover or turnover intention, with the goal of identifying key predictive variables and assessing model performance. A comprehensive search was conducted across PubMed, CINAHL, Cochrane Library, PsycINFO, and Google Scholar, following PRISMA guidelines. Out of 596 records screened, eight studies met the inclusio

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Shaikh, Mustaq Ahmad. "Advancing Groundwater Resource Management through Artificial Intelligence: Future Directions for GSDA Solapur." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03791.

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Abstract - The Groundwater Surveys and Development Agency (GSDA), Solapur plays a pivotal role in managing and conserving groundwater resources in the drought-prone region of Maharashtra. However, traditional approaches to groundwater monitoring, site selection, and recharge planning are often limited by delayed data processing and lack of predictive capabilities. This research explores the transformative potential of Artificial Intelligence (AI) in enhancing the efficiency and accuracy of groundwater management by GSDA. It discusses the integration of AI technologies—such as machine learning,

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He, Chentao, Jiangfeng Wei, Yuanyuan Song, and Jing-Jia Luo. "Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions." Water 13, no. 22 (2021): 3294. http://dx.doi.org/10.3390/w13223294.

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The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for compar

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Shepherd, J. Marshall. "A Review of Current Investigations of Urban-Induced Rainfall and Recommendations for the Future." Earth Interactions 9, no. 12 (2005): 1–27. http://dx.doi.org/10.1175/ei156.1.

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Abstract Precipitation is a key link in the global water cycle and a proxy for changing climate; therefore, proper assessment of the urban environment’s impact on precipitation (land use, aerosols, thermal properties) will be increasingly important in ongoing climate diagnostics and prediction, Global Water and Energy Cycle (GWEC) analysis and modeling, weather forecasting, freshwater resource management, urban planning–design, and land–atmosphere–ocean interface processes. These facts are particularly critical if current projections for global urban growth are accurate. The goal of this paper

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Antwi-Agyakwa, Kwesi Twum, Mawuli Kwaku Afenyo, and Donatus Bapentire Angnuureng. "Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools." Water 15, no. 3 (2023): 427. http://dx.doi.org/10.3390/w15030427.

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Flood prediction has advanced significantly in terms of technique and capacity to achieve policymakers’ objectives of accurate forecast and identification of flood-prone and impacted areas. Flood prediction tools are critical for flood hazard and risk management. However, numerous reviews on flood modelling have focused on individual models. This study presents a state-of-the-art review of flood prediction tools with a focus on analyzing the chronological growth of the research in the field of flood prediction, the evolutionary trends in flood prediction, analysing the strengths and weaknesses

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Van Remoortel, Hans, Hans Scheers, Emmy De Buck, Karen Lauwers, and Philippe Vandekerckhove. "Prediction Modeling Studies for Medical Usage Rates in Mass Gatherings: A Systematic Review." Prehospital and Disaster Medicine 34, s1 (2019): s40. http://dx.doi.org/10.1017/s1049023x19000979.

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Introduction:Mass gatherings attended by large crowds are an increasingly common feature of society. In parallel, an increased number of studies have been conducted to identify those variables that are associated with increased medical usage rates.Aim:To identify studies that developed and/or validated a statistical regression model predicting patient presentation rate (PPR) or transfer to hospital rate (TTHR) at mass gatherings.Methods:Prediction modeling studies from 6 databases were retained following systematic searching. Predictors for PPR and/or TTHR that were included in a multivariate

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Donofrio, Mary. "Predicting the Future: Delivery Room Planning of Congenital Heart Disease Diagnosed by Fetal Echocardiography." American Journal of Perinatology 35, no. 06 (2018): 549–52. http://dx.doi.org/10.1055/s-0038-1637764.

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AbstractAdvances in prenatal imaging have improved the examination of the fetal cardiovascular system. Fetal echocardiography facilitates the prenatal diagnosis of congenital heart disease (CHD) and through sequential examination, allows assessment of fetal cardiac hemodynamics, predicting the evolution of anatomical and functional cardiovascular abnormalities in utero and during the transition to a postnatal circulation at delivery. This approach allows detailed diagnosis with prenatal counseling and enables planning to define perinatal management, selecting the fetuses at a risk of postnatal

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Mwalwimba, Isaac Kadono, Bessam Kalonjeka, Vincent Msadala, et al. "Deep Learning-based Flood Risk Prediction for Climate Resilience Planning in Malawi." Journal of Atmospheric Science Research 8, no. 2 (2025): 37–50. https://doi.org/10.30564/jasr.v8i2.10377.

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Climate change resilience in Malawi faces an institutional gap because most institutions often fail to prioritize risk data when dealing with climate extremes such as floods. This unfortunate gap forces many Malawians to fend for themselves during times of climate extremes This situation is also heightened by a few studies that utilize Time Series Analysis (TSA) and Deep Learning Models (DLM) to predict climate extremes for decision-making processes. Therefore, this study focused on flood risk prediction and assessment in six selected districts of Malawi: Chikwawa, Blantyre, Phalombe, Zomba, R

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Zhang, Ruihui. "Optimization of Relevant Functions of Urban Computing in the Direction of Traffic Flow Prediction." Applied and Computational Engineering 140, no. 1 (2025): 24–30. https://doi.org/10.54254/2755-2721/2025.21272.

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As an important application direction of urban computing, traffic flow prediction plays an important role in modern traffic management, urban planning and sustainable development. In recent years, many cutting-edge studies in the field of traffic flow prediction have had a significant impact and promoted the development of practical applications in this field. This paper mainly focuses on the research results of various traffic prediction directions. According to the actual environment and the functional characteristics of the research results, the research is classified into three aspects: da

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Leivaditis, Vasileios, Eleftherios Beltsios, Athanasios Papatriantafyllou, et al. "Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future." Clinics and Practice 15, no. 1 (2025): 17. https://doi.org/10.3390/clinpract15010017.

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Background: Artificial intelligence (AI) has emerged as a transformative technology in healthcare, with its integration into cardiac surgery offering significant advancements in precision, efficiency, and patient outcomes. However, a comprehensive understanding of AI’s applications, benefits, challenges, and future directions in cardiac surgery is needed to inform its safe and effective implementation. Methods: A systematic review was conducted following PRISMA guidelines. Literature searches were performed in PubMed, Scopus, Cochrane Library, Google Scholar, and Web of Science, covering publi

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Kim, So Yeon. "GNN-surv: Discrete-Time Survival Prediction Using Graph Neural Networks." Bioengineering 10, no. 9 (2023): 1046. http://dx.doi.org/10.3390/bioengineering10091046.

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Survival prediction models play a key role in patient prognosis and personalized treatment. However, their accuracy can be improved by incorporating patient similarity networks, which uncover complex data patterns. Our study uses Graph Neural Networks (GNNs) to enhance discrete-time survival predictions (GNN-surv) by leveraging relationships in these networks. We build these networks using cancer patients’ genomic and clinical data and train various GNN models on them, integrating Logistic Hazard and PMF survival models. GNN-surv models exhibit superior performance in survival prediction acros

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