Advanced Sensor-Based Landslide and Earthquake Detection and Alert System Utilizing Machine Learning and Computer Vision Technologies

Abstract

—This paper presents a comprehensive analysis of the transformative role of the Internet of Things (IoT) and Machine Learning (ML) in advancing landslide monitoring and prediction for enhanced disaster resilience. Landslides, a prevalent natural hazard, pose substantial risks to life, infrastructure, and socio economic stability, particularly in geographically vulnerable regions. The inherent complexity of landslides, triggered by a confluence of geological, hydrological, and meteorological factors, necessitates advanced monitoring and prediction techniques to mitigate their devastating impacts. Traditional monitoring ap proaches, often constrained by limited spatial coverage, data resolution, and realtime analysis capabilities, struggle to provide timely and accurate warnings. The emergence of IoT and ML offers a paradigm shift in landslide monitoring and prediction, enabling real-time data acquisition, sophisticated analysis, and proactive risk management. IoT-enabled sensor networks, com prising diverse sensors strategically deployed across landslide prone areas, provide continuous data streams on critical param eters such as rainfall intensity and duration, soil moisture content, pore-water pressure, ground vibrations (microseismic activity), and slope deformation. These sensors, often low-cost, low-power, and wirelessly interconnected, transmit data to edge computing devices or cloud-based platforms for real-time processing and analysis. ML algorithms, trained on historical landslide data and associated parameters, play a pivotal role in deciphering complex patterns and anomalies within these large datasets. The sources demonstrate the effectiveness of various ML models, including Random Forest, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Convolutional Neural Networks (CNN), in landslide susceptibility mapping, hazard assessment, and early warning system development.