Early Landslide Detection Using Wireless Sensor Network

This paper aims at detecting and warning of possible landslides well in advance, preventing a significant threat to both human life and infrastructure. Hence the development of effective early detection systems is no longer an option but a necessity. Here, we performed a comprehensive study on implementing a machine learning (ML) based early landslide detection system. The primary objective is to investigate the feasibility and accuracy of an ML algorithm in predicting landslides before they occur and providing a warning using a system of wireless sensors. We considered a dataset comprising of diverse geological and meteorological features, compiled to train and evaluate the ML algorithm. Here, we also explored the application of a supervised learning algorithm, to classify regions susceptible to landslides based on historical data and update its dataset based on data from landslides which it itself predicts, to be used for future reference. Additionally, the research investigates the integration of remote sensing data, including ground-based sensors, to provide real-time inputs periodically, such as the moisture level of the soil nearby as well as any change in the inclination of the land, for the ML models. The findings contribute to the growing body of knowledge in landslide detection and offer insights into the practical implementation of ML algorithms for early warning systems. The results highlight the potentials of ML to significantly enhance the accuracy and timeliness of landslide predictions, ultimately contributing to improved disaster preparedness and risk reduction strategies.