IoT-Based Landslide Monitoring and Prediction Using Machine Learning

Landslides are one of the most devastating natural disasters that result in massive human and infrastructural losses and economic inconveniences. To minimize these effects, it is essential to monitor and make early predictions. In this paper, the author introduces an IoT-based landslide monitoring and forecasting system that uses geotechnical and environmental sensors combined with machine learning algorithms. This system records the real-time data on the main parameters, which are the soil cohesion, intensity of rainfall, the angle of internal friction, the angle of slope, the slope height, and the factor of safety (FOS). These readings are sent through the IoT communication protocols to a cloud storage, pre-processed, and processed by an analytical processing platform. This paper has tested three machine learning algorithms, which include Multilinear Regression, Random Forest, and Decision Tree, to identify and forecast landslide occurrences. It also describes the system architecture, data collection process, feature engineering, and the model performance, giving a comparative analysis of the prediction accuracy of each algorithm. The proposed system integrates the IoT-based sensing with the solutions that are based on data to improve the early warning, enable informed decisions of hazard-management, and safeguard human life, infrastructure, and environment in zones of landslides.