Development of an Early Warning System for Landslides using IOT Sensors and Machine Learning

Landslides have a huge threat to life, infrastructure, and the environment in areas of unstable geology and heavy rainfall. Traditional monitoring techniques are usually too slow and not accurate enough for timely intervention. This has led to disastrous effects, such as the 2024 landslide in Wayanad, Kerala, which resulted in multiple fatalities and extensive damage to homes and roads. Rising rates of landslides fueled by climate change only serve to intensify the calls for a better early warning system. Most present systems depend upon manual observations and static data; these fail to take into consideration the dynamic and changing environmental states. The hardest challenge in monitoring landslides is that no one can foresee the factors which will trigger these events and their relationship to a myriad of variables involved in triggering slope instability. Natural and human-induced factors that cause landslides include rainfall, soil saturation, steepness of the slope, and seismic activity. It is almost impossible to monitor such factors in a large area and analyze the cumulative impact in real-time with conventional techniques. This project solves the above challenges by developing an automated real-time early warning system using both IoT and machine learning technologies. It will continuously monitor critical parameters of landslide occurrence and track data analyses using machine learning algorithms for very early warning and predictive purposes so that local authority people and villagers get timely messages, and so faster evacuation is carried out or preventive measures take place. Based on our project we can alert people and dave so many live maximum 90%.