Volume 43 Issue 1
Jan.  2024
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RAO Shanshan, LENG Xiaopeng. Debris flow susceptibility evaluation of Liangshan Prefecture based on the RSIV-RF model[J]. Bulletin of Geological Science and Technology, 2024, 43(1): 275-287. doi: 10.19509/j.cnki.dzkq.tb20220267
Citation: RAO Shanshan, LENG Xiaopeng. Debris flow susceptibility evaluation of Liangshan Prefecture based on the RSIV-RF model[J]. Bulletin of Geological Science and Technology, 2024, 43(1): 275-287. doi: 10.19509/j.cnki.dzkq.tb20220267

Debris flow susceptibility evaluation of Liangshan Prefecture based on the RSIV-RF model

doi: 10.19509/j.cnki.dzkq.tb20220267
More Information
  • Author Bio:

    RAO Shanshan, E-mail: 943378644@qq.com

  • Corresponding author: LENG Xiaopeng, E-mail: lengxiaopeng@cdut.edu.cn
  • Received Date: 13 Jun 2022
  • Accepted Date: 26 Aug 2022
  • Rev Recd Date: 24 Aug 2022
  • Objective

    In employing the random forest (RF) model for debris flow susceptibility assessment, challenges arose, including subjectivity in classifying continuous factors and the low accuracy of randomly selected nondebris flow samples. Taking Liangshan Yi Autonomous Prefecture in southwestern Sichuan Province as the study area, a random forest based on statistical prior model sampling was proposed to evaluate the debris flow susceptibility in the study area.

    Methods

    Continuous factors are classified by the relative changes in cumulative disaster frequency and other curves. Rough set theory (RS) and the information value method (Ⅳ) were used to calculate the weighted information values, delimit the extremely low- and low-prone areas and selecting the negative sample data. The optimal number of trees n_estimators and the number of feature splits max_features for the RF model were determined from the out-of-bag error (OOB) change curves. Subsequently, a weighted information random forest (RSIV-RF) model was constructed to predict the vulnerability of debris flow in Liangshan Prefecture. Furthermore, a comparative analysis with the RF model randomly selecting non-debris flow samples revealed the superior performance of the RSIV-RF model.

    Results

    The results show that the accuracy of the RSIV-RF model in the training set and the test set is 0.89 and 0.83, respectively, and the AUC value of the corresponding ROC curve is 0.920 and 0.895, respectively, which are higher than that of the RF model alone. The assessment map of debris flow susceptibility drawn by RSIV-RF is consistent with the distribution of historical disasters. The areas with high and higher susceptibility levels account for 18.625% of the study area, including 78.57% of debris flow points.

    Conclusion

    The results of the performance evaluation and susceptibility statistics show that RSIV-RF can solve the problem of inaccurate sampling of nondebris samples in a single model, and its prediction accuracy of debris flow susceptibility is higher. It has good adaptability in the study of debris flow susceptibility evaluation in Liangshan Prefecture.

     

  • The authors declare that no competing interests exist.
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