Bidirectional LSTM Model for Accurate and Real-Time Landslide Detection: A Case Study in Mawiongrim, Meghalaya, India

This article presents a bidirectional long short-term memory (LSTM) model for the detection of landslides. Previous uses of machine learning (ML) in this setting have demonstrated its general potential, which necessitates the implementation of a suitable algorithm. Landslides are natural disasters that can cause significant destruction and disruption in the affected areas. Early detection is the key to minimizing the impact of landslides, so it is important to develop accurate and efficient models. An area selected for this study is located in Mawiongrim, Meghalaya, India, which is an active landslide zone. The proposed model uses a bidirectional LSTM to capture the temporal patterns of the input data collected from a long-term real-time monitoring system set up in the area. To evaluate the effectiveness of the predictions, the model is trained using a data set composed of various landslide-related characteristics, such as topography, rainfall, hydrological, and soil properties. The results show that the suggested model is capable of detecting landslides with greater accuracy and the lowest error value relative to other models. Additionally, the model is also able to provide a real-time warning system, making it a viable tool for early landslide detection. The research also highlights the prediction models for matric suction and groundwater level, which are crucial in determining slope stability.