Real-Time Landslide Dataset of Mawiongrim, Meghalaya, India

This paper presents a bi-directional Long ShortTerm Memory (LSTM) model for the detection of landslides. Previous uses of machine learning 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 bi-directional 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 dataset 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

本文提出了一种双向长短期记忆（LSTM）模型，用于滑坡的检测。在此领域先前应用机器学习技术已充分展示了其潜在的普遍适用性，这要求实施一种适宜的算法。滑坡作为一种自然灾害，能够在受影响区域造成严重的破坏和扰乱。早期检测是减轻滑坡影响的关键，因此开发准确且高效的模型显得尤为重要。本研究选取的区域位于印度梅加拉亚邦的Mawiongrim，该地区是一个活跃的滑坡区。所提出的模型采用双向LSTM来捕捉从该区域长期实时监测系统中收集的输入数据的时序模式。为了评估预测的有效性，该模型使用由多种与滑坡相关的特征组成的训练集进行训练，这些特征包括地形、降雨、水文和土壤属性。结果表明，所提出的模型在检测滑坡方面具有较高的准确性，并且相较于其他模型具有最低的错误值。此外，该模型还能够提供实时预警系统，使其成为早期滑坡检测的有用工具。研究还突出了对于基质吸力和地下水位预测模型的研究，这些模型对于确定边坡稳定性至关重要。