ICT Interventions in Landslide Prediction Runout Modelling, Mitigation and Early Warning

Landslides are one of the most destructive natural hazards, especially in mountainous regions, leading to severe loss of life, property damage, and ecosystem disruption. The complex nature of landslide prediction, influenced by geological, hydrological, topographical, and climatic factors, has long been a challenge. Consequently, Informatics based tools like run out modelling for forecasting, mitigation, and early warning have become crucial, utilizing technologies such as remote sensing, GIS, IoT, and AI/ML. This paper explores the effective application of Informatics especially its AI, ML components, in landslide risk management, with a focus on real-time data acquisition, numerical prediction models, and forecasting. Key topics discussed in this article include numerical prediction models, multi-source data integration, early warning systems (EWS) utilizing IoT sensor networks, cloud-based analytics, mobile applications for community mobilization and real-time alerts, deep learning algorithms for detection, and GIS and remote sensing for mapping susceptibility, supported by case studies. Challenges such as data quality, infrastructure constraints, and gaps in public awareness are also discussed. Stresses the role of Informatics in improving the accuracy of disaster prediction and enhancing disaster preparedness and response through timely warnings.