Effectively leveraging landslide spatial location information is crucial for improving the accuracy of deep learning in predicting landslide susceptibility and exploring the impacts of predisposing factors. Current single deep learning models for landslide susceptibility assessment require enhancements in both prediction accuracy and robustness. Inclusion of non-interrelated positional information among samples leads to reduced prediction accuracy and challenges in quantifying landslide risk covariates. This study proposes a landslide susceptibility assessment method that integrates ensemble learning with geographically weighted concepts. Using a stacking method, a 1D convolutional neural network (1D-CNN), a recurrent neural network (RNN), and a long short-term memory (LSTM) network were combined to form the CRNN-LSTM ensemble model. Additionally, we constructed a deep learning geographically weighted regression (GW-DNN) model based on the deep learning principles and geographically weighted regression to quantify the impacts of landslide-predisposing factors.The experimental results show that the CRNN-LSTM model achieved AUC values of 0.977 and 0.961 on the training and validation sets, significantly outperforming the individual classifiers (AUC of 0.944 and 0.940 for the 1D-CNN model, 0.950 and 0.948 for the RNN model, and 0.956 and 0.952 for the LSTM model). Additionally, the GW-DNN model achieved R2 coefficients of 0.876 and 0.860 during the training and validation phases. These findings indicate that our proposed method not only highly accurately predicts landslide susceptibility but also provides a precise quantitative assessment of the impact of landslide-predisposing factors at specific spatial points (landslide units) in high-risk areas. These findings offer valuable technical support for landslide disaster prevention and mitigation.

中文翻译：

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一种预测滑坡易发性和评估诱发因素的方法


有效利用滑坡空间位置信息对于提高深度学习预测滑坡易感性和探索诱发因素影响的准确性至关重要。当前用于滑坡敏感性评估的单一深度学习模型需要提高预测准确性和稳健性。在样本中包含不相关的位置信息会导致预测准确性降低，并导致量化滑坡风险协变量的挑战。本研究提出了一种滑坡易感性评估方法，该方法将集成学习与地理加权概念相结合。采用堆叠方法，将一维卷积神经网络 （1D-CNN） 、递归神经网络 （RNN） 和长短期记忆 （LSTM） 网络相结合，形成 CRNN-LSTM 集成模型。此外，我们基于深度学习原理和地理加权回归构建了深度学习地理加权回归 （GW-DNN） 模型，以量化滑坡诱发因素的影响。实验结果表明，CRNN-LSTM 模型在训练集和验证集上实现了 0.977 和 0.961 的 AUC 值，显著优于单个分类器（1D-CNN 模型的 AUC 分别为 0.944 和 0.940，RNN 模型的 AUC 为 0.950 和 0.948，LSTM 模型的 AUC 为 0.956 和 0.952）。此外，GW-DNN 模型在训练和验证阶段实现了 0.876 和 0.860 的 R2 系数。这些发现表明，我们提出的方法不仅高度准确地预测了滑坡易发性，而且对高风险区域特定空间点（滑坡单元）滑坡易感因素的影响进行了精确的定量评估。 研究结果为滑坡灾害防治提供了宝贵的技术支持。