An Ensemble Learning Based Framework for Smart Landslide Detection, Monitoring, Prediction and Warning in IoT-Cloud Environment

Abstract

Abstract Landslides occur every year during the Monsoon season in hilly areas. Every year, this natural disaster lead to several fatalities, injuries and property destruction. It is crucial to monitor landslides and promptly alert people to looming disasters in light of these injuries and fatalities. Till date no efficient technique is in practice to predict landslides. The tools that are now available monitor landslides at a very high cost and do not offer early warning or forecasts of soil movement. A innovative, low-cost Internet of Things (IoT)-based system for landslip warning, monitoring, and prediction is the major objective of this research. Its assessment, implementation, and development are described in detail.In this study, an IoT-based smart landslide detection, warning, predicting and monitoring system is proposed. The pre and post measures are considered using sensors and other hardware to deal with landslide disaster. It uses real time monitoring of the environment (landslide site) for any changes and providing appropriate output by comparing the threshold values. The proposed system is put to the test on a prototype model, which performed well in our tests. The database was updated 2.5 seconds after the landslide happened, thanks to a steady internet connection. In less than 5 seconds after the event, the thinkspeak channel is able to display a graphical depiction of the data as well as its position. Multiple readings showed an 80–85% system accuracy rate. Further, the proposed ensemble learning based risk prediction model is applied on static and dynamic data to predict the landslide for future references. The ensemble classifier model has 98.67% recall, 96.56% accuracy, 97.35% F1- value and 96.07% precision. The alert SMS are also sent to concerned authority for medical emergency/PWD department/ District administration.