Books on the topic 'Predictive risk modeling'

This chapter provides a nontechnical review of new statistical methodology for longitudinal data analysis that has been published in statistical journals in recent years. The methodology has applications in four important areas: (1) conducting variable selection among many highly correlated risk factors when the outcome measure is zero-inflated count; (2) characterizing developmental trajectories of symptomatology using regression splines; (3) modeling the longitudinal association between risk factors and substance use outcomes as time-varying effects; and (4) testing measurement reactivity and predictive validity using daily process data. The excellent statistical properties of the methods introduced have been supported by simulation studies. The applications in alcohol and substance abuse research have also been demonstrated by graphs on real longitudinal data.

This book, “Forest Paths” for short, started as a detailed guide for the construction of predictive models for credit and other risk assessment, for use in big-bank retail lending. It became a textbook covering credit processes (from marketing through to fraud), bureau and rating agencies, and various tools. Included are detailed histories (economics, statistics, social science}, which much referencing. It is unique in the field, with chatpers’-end questions. The primary target market is corporate and academic, but much would be of interest to a broader audience. There are eight modules: 1) an introduction to credit risk assessment and predictive modelling; 2) micro-histories of credit, credit intelligence, credit scoring, plus industrial revolutions, economic ups and downs, and both personal registration and identification; 4) mathematical and statistical tools used to develop and assess predictive models; 5) project management and data assembly; 6) data preparation from sampling to reject inference; 7) model training through to implementation; and 8) appendices, including an extensive glossary, bibliography, and index. Although the focus is credit risk, especially in the retail consumer and small-business segments, many concepts are common across disciplines as diverse as psychology, biology, engineering, and computer science, whether academic research or practical use. It also covers issues relating to the use of machine learning for credit risk assessment. Most of the focus is on traditional modelling techniques, but the increasing use of machine learning is recognised, as are its limitations. It is hoped that the contents will inform both camps.

In this chapter, the author defines and provides examples of several key terms used in public health and healthcare modeling research. The chapter begins by clarifying the differences between key terms used to describe rates of disease (incidence, prevalence, and mortality) as well as the performance characteristics of tests used to detect disease (sensitivity, specificity, positive predictive value, and negative predictive value), prevent or treat disease (odds ratios, relative risks), understand studies (case-control, cohort, and randomized controlled trials), and avoid common study problems (bias, confounding). Understanding these key terms and how they are used in research studies allows the reader to correctly interpret study results.

The incidence infectious disease is inherently dynamic in time and space. Mathematical models that account for the dynamic processes that give rise to fluctuations in disease incidence are powerful tools in disease management and control. We describe the use of dynamic models for surveillance, evaluation and prediction of disease control efforts in low-income countries. Dynamic models can help to anticipate trends owing to intrinsic (e.g., herd immunity) or extrinsic (e.g., seasonality) forces that may confound efforts to isolate the impact of specific interventions. Infectious disease dynamics are frequently nonlinear, meaning that future outcomes are difficult to predict through simple extrapolation of present conditions. Thus, dynamic models can help to explore the potential consequences of proposed interventions. These projections can alert managers to the potential for unintended consequences of control and help to define effect sizes for the design of conventional studies of the impact of interventions.

INHALT Electrical Agricultural Machines Structuring of electrified agricultural machine systems – Diversity of solutions and analysis methods .....1 GridCON2 – Development of a Cable Drum Vehicle Concept to Power 1MW Fully Electric Agricultural Swarms ..... 11 GridCON Swarm – Development of a Grid Connected Fully Autonomous Agricultural Production System ..... 17 Fully electric Tractor with 1000 kWh battery capacity ..... 23 Soil and Modelling The Integration of a Scientific Soil Compaction Risk Indicator (TERRANIMO) into a Holistic Tractor and Implement Optimization System (CEMOS) .....29 Identification of draft force characteristics for a tillage tine with variable geometry ..... 37 Calibration of soil models within the Discrete Element Method (DEM) ..... 45 Automation and Optimization of Working Speed and Depth in Agricultural Soil Tillage with a Model Predictive Control based on Machine Learning ..... 55 Synchronising machine adjustments of combine harvesters for higher fleet performance ..... 65 A generic approach to bridge the gap between route optimization and motion planning for specific guidance points o...

Uncertainty in Wastewater Treatment Design and Operation aims to facilitate the transition of the wastewater profession to the probabilistic use of simulators with the associated benefits of being better able to take advantage of opportunities and manage risk. There is a paradigm shift taking place in the design and operation of treatment plants in the water industry. The market is currently in transition to use modelling and simulation while still using conventional heuristic guidelines (safety factors). Key reasons for transition include: wastewater treatment simulation software advancements; stricter effluent requirements that cannot be designed for using traditional approaches, and increased pressure for more efficient designs (including energy efficiency, greenhouse gas emission control). There is increasing consensus among wastewater professionals that the performance of plants and the predictive power of their models (degree of uncertainty) is a critical component of plant design and operation. However, models and simulators used by designers and operators do not incorporate methods for the evaluation of uncertainty associated with each design. Thus, engineers often combine safety factors with simulation results in an arbitrary way based on designer ‘experience’. Furthermore, there is not an accepted methodology (outside modelling) that translates uncertainty to assumed opportunity or risk and how it is distributed among consultants/contractors and owners. Uncertainty in Wastewater Treatment Design and Operation documents how uncertainty, opportunity and risk are currently handled in the wastewater treatment practice by consultants, utilities and regulators. The book provides a useful set of terms and definitions relating to uncertainty and promotes an understanding of the issues and terms involved. It identifies the sources of uncertainty in different project phases and presents a critical review of the available methods. Real-world examples are selected to illustrate where and when sources of uncertainty are introduced and how models are implemented and used in design projects and in operational optimisation. Uncertainty in Wastewater Treatment Design and Operation defines the developments required to provide improved procedures and tools to implement uncertainty and risk evaluations in projects. It is a vital reference for utilities, regulators, consultants, and trained management dealing with certainty, opportunity and risk in wastewater treatment. ISBN: 9781780401027 (Paperback) ISBN: 9781780401034 (eBook) ISBN: 9781789062601 (ePub)

General relativity explains much more than the spacetime around static spherical masses.We briefly assess general relativity in the larger context of physical theories, then explore various general relativistic effects that have no Newtonian analog. First, source massmotion gives rise to gravitomagnetic effects on test particles.These effects also depend on the velocity of the test particle, which has substantial implications for orbits around black holes to be further explored in Chapter 20. Second, any changes in the sourcemass ripple outward as gravitational waves, and we tell the century‐long story from the prediction of gravitational waves to their first direct detection in 2015. Third, the deflection of light by galaxies and clusters of galaxies allows us to map the amount and distribution of mass in the universe in astonishing detail. Finally, general relativity enables modeling the universe as a whole, and we explore the resulting Big Bang cosmology.

Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.

Guidelines for Open Pit Slope Design is a comprehensive account of the open pit slope design process. Created as an outcome of the Large Open Pit (LOP) project, an international research and technology transfer project on rock slope stability in open pit mines, this book provides an up-to-date compendium of knowledge of the slope design processes that should be followed and the tools that are available to aid slope design practitioners.
 This book links innovative mining geomechanics research into the strength of closely jointed rock masses with the most recent advances in numerical modelling, creating more effective ways for predicting rock slope stability and reliability in open pit mines. It sets out the key elements of slope design, the required levels of effort and the acceptance criteria that are needed to satisfy best practice with respect to pit slope investigation, design, implementation and performance monitoring.
 Guidelines for Open Pit Slope Design comprises 14 chapters that directly follow the life of mine sequence from project commencement through to closure. It includes: information on gathering all of the field data that is required to create a 3D model of the geotechnical conditions at a mine site; how data is collated and used to design the walls of the open pit; how the design is implemented; up-to-date procedures for wall control and performance assessment, including limits blasting, scaling, slope support and slope monitoring; and how formal risk management procedures can be applied to each stage of the process.
 This book will assist in meeting stakeholder requirements for pit slopes that are stable, in regards to safety, ore recovery and financial return, for the required life of the mine.

Presented reasearch aimed to develop and analyse the suitability of the CART models for prediction of the extent and probability of occurrence of damage to outer soil layers caused by timber harvesting performed under varied conditions. Having employed these models, the author identified certain methods of logging works and conditions, under which they should be performed to minimise the risk of damaging forest soils. The analyses presented in this work covered the condition of soils upon completion of logging works, which was investigated in 48 stands located in central and south-eastern Poland. In the stands selected for these studies a few felling treatments were carried out, including early thinning, late thinning and final felling. Logging works were performed with use of the most popular technologies in Poland. Trees were cut down with chainsaws and timber was extracted by means of various skidding methods: with horses, semi-suspended skidding with the use of cable yarding systems, farm tractors equipped with cable winches or tractors of a skidder type, and forwarding employing farm tractors with trailers loaded mechanically by cranes or manually. The analyses also included mechanised forest operation with the use of a harvester and a forwarder. The information about the extent of damage to soil, in a form of wheel-ruts and furrows, gathered in the course of soil condition inventory served for construction of regression tree models using the CART method (Classification and Regression Trees), based on which the area, depth and the volume of soil damage under analysis, wheel-ruts and furrows, were determined, and the total degree of all soil disturbances was assessed. The CART classification trees were used for modelling the probability of occurrence of wheel-ruts and furrows, or any other type of soil damage. Qualitative independent variables assumed by the author for developing the models included several characteristics describing the conditions under which the logging works were performed, mensuration data of the stands and the treatments conducted there. These characteristics covered in particular: the season of the year when logging works were performed, the system of timber harvesting employed, the manner of timber skidding, the means engaged in the process of timber harvesting and skidding, habitat type, crown closure, and cutting category. Moreover, the author took into consideration an impact of the quantitative independent variables on the extent and probability of occurrence of soil disturbance. These variables included the following: the measuring row number specifying a distance between the particular soil damage and communication tracks, the age of a stand, the soil moisture content, the intensity of a particular cutting treatment expressed by units of harvested timber volume per one hectare of the stand, and the mean angle of terrain inclination. The CART models developed in these studies not only allowed the author to identify the conditions, under which the soil damage of a given degree is most likely to emerge, or determine the probability of its occurrence, but also, thanks to a graphical presentation of the nature and strength of relationships between the variables employed in the model construction, they facilitated a recognition of rules and relationships between these variables and the area, depth, volume and probability of occurrence of forest soil damage of a particular type. Moreover, the CART trees served for developing the so-called decision-making rules, which are especially useful in organising logging works. These rules allow the organisers of timber harvest to plan the management-related actions and operations with the use of available technical means and under conditions enabling their execution in such manner as to minimise the harm to forest soils. Furthermore, employing the CART trees for modelling soil disturbance made it possible to evaluate particular independent variables in terms of their impact on the values of dependent variables describing the recorded disturbance to outer soil layers. Thanks to this the author was able to identify, amongst the variables used in modelling the properties of soil damage, these particular ones that had the greatest impact on values of these properties, and determine the strength of this impact. Detailed results depended on the form of soil disturbance and the particular characteristics subject to analysis, however the variables with the strongest influence on the extent and probability of occurrence of soil damage, under the conditions encountered in the investigated stands, enclosed the following: the season of the year when logging works were performed, the volume-based cutting intensity of the felling treatments conducted, technical means used for completion of logging works, the soil moisture content during timber harvest, the manner of timber skidding, dragged, semi-suspended or forwarding, and finally a distance between the soil damage and transportation ducts. The CART models proved to be very useful in designing timber harvesting technologies that could minimise the risk of forest soil damage in terms of both, the extent of factual disturbance and the probability of its occurrence. Another valuable advantage of this kind of modelling is an opportunity to evaluate an impact of particular variables on the extent and probability of occurrence of damage to outer soil layers. This allows the investigator to identify, amongst all of the variables describing timber harvesting processes, those crucial ones, from which any optimisation process should start, in order to minimise the negative impact of forest management practices on soil condition.