Understanding Indian Monsoon through Network Science and Machine Learning

This book presents a comprehensive overview of state-of-the-art research at the intersection of machine learning, network science, and Earth and climate sciences. This book places particular emphasis on causal and explainable machine learning approaches, alongside predictive modeling techniques, as applied to climate data. These methodologies are introduced and discussed through representative research studies published in recent years. A substantial number of the case studies focus on the Indian Monsoon, a complex and critically important climatic phenomenon that profoundly influences the lives and livelihoods of more than a billion people across South Asia. This book begins with two introductory chapters. The first chapter outlines the ongoing climate crisis and introduces Earth System Models, highlighting their key limitations and the challenges they face. It also provides a perspective on how machine learning and artificial intelligence can help address some of these challenges. The second chapter offers a concise primer on the Indian Monsoon, describing its fundamental characteristics, variability, and the major scientific questions for which AI- and ML-based approaches may offer new insights. The subsequent chapters each focus on a specific methodological theme—such as network-based analysis, causal inference, explainable machine learning, or predictive modeling—providing conceptual explanations alongside concrete examples drawn from recent research literature. Through these chapters, this book demonstrates how modern data-driven methods can be applied to longstanding problems in monsoon and climate science. This book is designed for graduate students and early-career researchers in climate and Earth sciences who seek to incorporate machine learning and data science into their research.