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<p>This study evaluates four models across two modeling paradigms to develop debris flow susceptibility maps for Chaharmahal and Bakhtiari Province, Iran, a semi-arid, mountainous region spanning 1,655,300&#xa0;ha prone to debris flow hazards. The models include Multi-Layer Perceptron (MLP) and Self-Organizing Map (SOM) (Neural Networks, NN) and Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) (Deep Learning, DL). A debris flow inventory of 713 points (288 debris flow, 425 non-debris flow) was compiled, and collinearity analysis on 25 environmental variables reduced to 19 variables (e.g., flow accumulation, slope degree, rainfall) after excluding highly correlated factors (e.g., topographic roughness index, average rainfall). Models were trained on 465 points and validated on 248 points, with susceptibility maps delineated into five zones (Very Low, Low, Moderate, High, Very High) using the natural break algorithm. Results show MLP and CNN emerged as the most accurate supervised models, achieving AUC values of 0.91 and 0.90, respectively, with RMSE values of 0.35 and 0.35, indicating high discriminative ability and low prediction error. LSTM achieved moderate performance (AUC: 0.86), while SOM, employed as an unsupervised clustering method for exploratory analysis, showed limited predictive capability (AUC: 0.70). For MLP, susceptibility zones were distributed as Very Low (81.4%), Low (6.4%), Moderate (2.1%), High (2.2%), and Very High (7.9%). CNN allocated 68.6% to Very Low, 16.4% to Low, 6.2% to Moderate, 4.3% to High, and 4.5% to Very High. The SHAP analysis underscored topographic wetness index (0.11), surface sand content (0.07), slope length (0.06), flow accumulation (0.06), and depth to bedrock (0.05) as primary predictors, illustrating the significant roles of hydrological, topographic, and soil-related factors in driving debris flow events.</p>

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Debris flow susceptibility mapping using neural networks and deep learning with SHAP-based interpretation in Chaharmahal and Bakhtiari Province, a semi-arid mountainous region of Iran

  • 期刊论文

  • 发表日期：

    2026年7月2日

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  • Saleh Yousefi,
  • Sara Mardanian,
  • Fumitoshi Imaizumi,
  • Christopher Gomez

摘要

This study evaluates four models across two modeling paradigms to develop debris flow susceptibility maps for Chaharmahal and Bakhtiari Province, Iran, a semi-arid, mountainous region spanning 1,655,300 ha prone to debris flow hazards. The models include Multi-Layer Perceptron (MLP) and Self-Organizing Map (SOM) (Neural Networks, NN) and Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) (Deep Learning, DL). A debris flow inventory of 713 points (288 debris flow, 425 non-debris flow) was compiled, and collinearity analysis on 25 environmental variables reduced to 19 variables (e.g., flow accumulation, slope degree, rainfall) after excluding highly correlated factors (e.g., topographic roughness index, average rainfall). Models were trained on 465 points and validated on 248 points, with susceptibility maps delineated into five zones (Very Low, Low, Moderate, High, Very High) using the natural break algorithm. Results show MLP and CNN emerged as the most accurate supervised models, achieving AUC values of 0.91 and 0.90, respectively, with RMSE values of 0.35 and 0.35, indicating high discriminative ability and low prediction error. LSTM achieved moderate performance (AUC: 0.86), while SOM, employed as an unsupervised clustering method for exploratory analysis, showed limited predictive capability (AUC: 0.70). For MLP, susceptibility zones were distributed as Very Low (81.4%), Low (6.4%), Moderate (2.1%), High (2.2%), and Very High (7.9%). CNN allocated 68.6% to Very Low, 16.4% to Low, 6.2% to Moderate, 4.3% to High, and 4.5% to Very High. The SHAP analysis underscored topographic wetness index (0.11), surface sand content (0.07), slope length (0.06), flow accumulation (0.06), and depth to bedrock (0.05) as primary predictors, illustrating the significant roles of hydrological, topographic, and soil-related factors in driving debris flow events.