Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

Abstract

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

Potential of Ensemble Learning to Improve Tree-Based Classifiers for Landslide Susceptibility Mapping

“…Landslide is a very complicated process. So far, in order to accurately evaluate and predict landslide susceptibility, scholars have been trying to explore new methods [55,56]. Because tree-based models are easy to visualize and are suitable for a small amount of sample data, scholars often apply them to LSM and use the tree visualization to understand the rules of landslide prediction [11][12][13].…”

Constructing a Large-scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

“…Deep learning, a branch of ML, has also been used for landslide mapping [20], [29]- [34]. Deep learning techniques are more efficient in terms of automatic feature engineering directly from satellite imagery.…”

Numerical Analysis on Reinforcement Range of a Closed Steel Sleeve against Collapse

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