Analysis and prediction of landslide deformation in water environment based on machine algorithm

Abstract

Landslides represent a significant natural hazard, especially in water-rich environments where the presence of water can drastically influence slope stability and deformation behavior. Accurate analysis and prediction of landslide deformation in such locations are critical for risk assessment and mitigation. This paper focuses on analysis and prediction of landslides in water environments through machine learning techniques by analyzing hydrological data of that geological location. The study employs the Elman Neural Network (ENN) model to create a predictive model. The ENN predicts future deformation trends based on hydrological data by identifying patterns in soil water. The performance of these models is evaluated using metrics such as accuracy, precision, and recall, ensuring robust validation against real-world data. The results show that the F1 score of the developed prediction system is 85%, which proves the effectiveness of machine learning in predicting landslide deformation based on hydrological data, and provides a reliable tool for the early warning system in landslide prone areas. The developed machine learning-based landslide risk assessment model through hydrological data not only predicts landslides but also can predict the level of groundwater and water quality, which are very helpful for emergency risk assessment and provides solutions to enhance the safety and resilience of communities in landslide-prone zones.