Hierarchical Modelling For The Environmental Sciences
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Hierarchical Modelling for the Environmental Sciences
New statistical tools are changing the ways in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences. Models have developed rapidly, and there is now a requirement for a clear exposition of the methodology through to application for a range of environmental challenges.
Artificial Intelligence-Driven Models for Environmental Management
Author: Shrikaant Kulkarni
language: en
Publisher: John Wiley & Sons
Release Date: 2025-07-01
Step-by-step guidelines for the development of artificial neural network-based environmental pollution models Artificial Intelligence-Driven Models for Environmental Management delves into the application of AI across a plethora of areas in environmental management, including climate forecasting, natural resource optimization, waste management, and biodiversity conservation. This book shows how AI can help in monitoring, predicting, and mitigating environmental impacts with tremendous accuracy and speed by leveraging machine learning, deep learning, and other data-driven models. The methodologies explored in this volume reflect a synthesis of computational intelligence, data science, and ecological expertise, underscoring how AI-driven systems have been making strides in managing and preserving our planet’s natural resources. The text is structured to guide readers through numerous AI models and their practical environmental management applications, showcasing theoretical foundations as well as case studies. This book also addresses the challenges and ethical considerations related to deploying AI in ecological contexts, underscoring the importance of transparency, inclusivity, and alignment with sustainability goals. Sample topics discussed in Artificial Intelligence-Driven Models for Environmental Management include: Tools and methods for monitoring and predicting environmental pollutants faster and more accurately AI technology for the protection of water supplies from contamination to produce healthier foods Use of AI for the evaluation of the impacts of environmental pollution on human health AI and waste management technologies for sustainable agriculture and soil management The role of AI in environmental research and sustainability and key social and economic aspects of natural resource management through AI Artificial Intelligence-Driven Models for Environmental Management is a timely, forward-thinking resource for a diverse readership, including researchers, policymakers, environmental scientists, and AI practitioners.
Models for Ecological Data
Author: James S. Clark
language: en
Publisher: Princeton University Press
Release Date: 2020-10-06
The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data. Consistent treatment from classical to modern Bayes Underlying distribution theory to algorithm development Many examples and applications Does not assume statistical background Extensive supporting appendixes Lab manual in R is available separately