The Himalayan region, characterized by its steep terrain and geological complexities, stands vulnerable to the persistent threat of landslides, particularly exacerbated during the monsoon season. The susceptibility to landslides in this region arises from a convergence of factors, including the region's high seismic activity, diverse geological formations, intense monsoonal precipitation, and rapidly changing climate patterns. The impact of landslides extends beyond immediate infrastructure damage, often leading to loss of lives, disruption of livelihoods, and severe environmental degradation. This chapter embarks on a comprehensive exploration of the pivotal role of artificial intelligence and machine learning in revolutionizing the prediction and mitigation of landslides in the Himalayas. It delves into the intricate challenges posed by the region's geological diversity and environmental dynamics, offering insights into AI-driven strategies to enhance predictive accuracy, implement early warning systems, and devise effective mitigation measures. The chapter commences with an overview of the Himalayas, delineating the geological complexities and the profound influence of climatic conditions on landslide occurrences. It elucidates the critical challenges hindering traditional landslide prediction methods, such as inadequate data quality and sparsity, underscoring the dire need for advanced predictive techniques. A meticulous review of existing methods, encompassing both conventional approaches and the utilization of remote sensing technologies and geographic information systems (GIS), sets the stage for introducing AI-based solutions. The chapter unfolds the nuances of machine learning approaches tailored for landslide prediction, spotlighting the selection of pertinent features, the application of supervised and unsupervised learning models, and the integration of real-time environmental data. A pivotal focus lies on AI-driven early warning systems that amalgamate historical data, sensor networks, and predictive models to facilitate timely alerts and risk assessments. Moreover, the chapter elucidates how AI empowers hazard zonation mapping, aiding in the identification of high-risk areas and adaptive planning for resilient infrastructure development. The narrative is enriched with insightful studies and practical implementations showcasing the efficacy of AI-based models in landslide prediction and mitigation within the Himalayan terrain. Lessons gleaned from these studies illuminate both successes and challenges, providing invaluable insights for the field. This chapter endeavours to unravel the transformative potential of AI and machine learning in confronting the formidable challenge of landslides in the Himalayas. It underscores the significance of these technological advancements in fostering resilience, safeguarding lives, and fortifying the region's infrastructure against the omnipresent threat of landslides.
