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.