Machine Learning and Spatial Optimisation

Editors: Kanwarpreet Singh, Arundhati, Manmeet Kaur, Abhishek Sharma, Aditya Kumar Tiwary, Sahil Sharma

Machine Learning and Spatial Optimisation

ISBN: 979-8-89881-379-6
eISBN: 979-8-89881-378-9 (Online)

Introduction

Machine Learning and Spatial Optimisation is an exploration positioned at the intersection of environmental science, geospatial technology, and data analytics, exploring how advanced computational methods and spatial data analysis can address critical environmental challenges.

The chapters progress from foundational concepts to practical case studies in spatial data and GIS workflows to real-world applications, including air quality monitoring, water resource management, land-use analysis, biodiversity conservation, and disaster risk assessment.

With a strong focus on real-world implementation, the book bridges theory and practice by offering methodological insights, policy relevance, and data-driven strategies for sustainable environmental management.


Key Features

  • - Integration of machine learning with GIS and spatial analysis.
  • - Coverage of major environmental challenges and applications.
  • - Real-world case studies for monitoring, prediction, and planning.
  • - Focus on decision support, policy insights, and sustainability.
  • - Practical approaches to data-driven environmental management.

Target Readership :

Researchers, postgraduate students, and academics in environmental science, geoinformatics, civil engineering, and climate studies.

Foreword

The increasing severity of environmental problems facing the modern world—from the long-lasting and widespread effects of climate change and the rapid depletion of resources to the ongoing reduction in biological diversity—requires better ways of monitoring and managing the environment in more sophisticated, systematic, and holistic ways. In considering these timely issues, this book explores the essential role that spatial data can play in helping us understand and investigate them. Spatial data—the data that is somehow tied to a geographical location—are powerful tools for monitoring and assessing the environment, and therefore provide good visualizations, comparisons, and assessments of environmental patterns and processes.

This book stresses the need to develop strategies and actions to address a range of environmental issues, grounded in spatial analysis and established scientific and computational methods. From investigating some of the most serious and widespread environmental problems in the world—air pollution—to assessing the appropriate and fair management of water use, the authors in this book define interesting and useful ways that data tied to locations can show insights based on well-defined and precise frameworks in order to support evidence-based actions and policy. This shows how practitioners, researchers, and decision-makers can benefit from concrete examples. Environmental sustainability requires researchers to expand their perception of the natural world and how it can be conserved. In situations where environmental systems operate as intended, researchers and other practitioners eliminate the need to stop unsafe practices, seek to identify new pathways to environmental sustainability, and facilitate strategic alliances to operate in that manner. The case studies in this book come from diverse fields and disciplines (geography, hydrology, environmental engineering, ecology, geoscience, etc.) to identify ways to successfully utilize spatial thinking to contribute to sustainable development. The text demonstrates the necessity of collaboration, settler or stakeholder agreements, addressing issues in an ongoing manner, long-term strategic planning, and working smartly using data, data management, and technology. By identifying and sharing these ways to engage in the ongoing management of natural resources, it is hoped that the text has added value to efforts to safeguard and sustain the environment. Ultimately, it is hoped that the applications of the spatial methods and applicable models of environmental science in this text will combine to bring greater resilience, support conservation, and predictably steer communities and organizations along a healthier pathway that does not compromise the ability of other good stewards who may come after them and empowers us all to reimagine our relationship with the natural world and improve the sustainability of our resources in communion with it.

Ravi Kumar Sharma
Department of Civil Engineering
National Institute of Technology
Hamirpur, Himachal Pradesh
India