Landslide Early Warning Systems: Requirements and Solutions for Disaster Risk Reduction—India

Abstract Globally the prevalence of landslides has increased, impacting more than 4.8 million people between 1998 and 2017 and reported more than 18,000 casualties [UNDP]. The scenario has worsened dramatically, and it has become imperative to develop early warning systems to save human life. This demands the need for systems that could identify the potential of imminent landslides and disseminate the information related to landslide initiation in real-time. This would provide the opportunity to save lives. However, globally the research on reliable end-to-end systems for early warning of landslides is still in its nascent stage. Therefore, this paper explores in detail the requirements for developing systems for real-time monitoring, detection, and early warning of landslides. An integrated solution for building the real-time landslide monitoring and early warning system to provide community-scale disaster resilience is also proposed. This solution integrates multiple modules such as a heterogeneous sensor system, data storage and management, event detection framework, alert dissemination, and emergency communication system to address issues such as capturing dynamic variability, managing multi-scale voluminous datasets, extracting key triggering information regarding the onset of possible landslide, multilevel alert dissemination, and robust emergency communication among the stakeholders respectively. The paper also presents two case studies of real-time landslide early warning systems deployed in North-eastern Himalayas and Western Ghats of India. These case studies demonstrate the approaches utilized for risk assessment, risk analysis, risk evaluation, risk visualization, risk control, risk communication, and risk governance. The results from the deployed system in the case study areas demonstrate the capability of the IoT system to gather Spatio-temporal triggers for multiple types of landslides, detection and decision of specific scenarios, and the impact of real-time data on mitigating the imminent disaster.