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Land subsidence susceptibility mapping: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithms

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Abstract

Land subsidence is a worldwide threat. In arid and semiarid lands, groundwater depletion is the main factor that induce the subsidence resulting in environmental damages and socio-economic issues. To foresee and prevent the impact of land subsidence, it is necessary to develop accurate maps of the magnitude and evolution of the subsidences. Land subsidence susceptibility maps (LSSMs) provide one of the effective tools to manage vulnerable areas and to reduce or prevent land subsidence. In this study, we used a new approach to improve decision stump classification (DSC) performance and combine it with machine learning algorithms (MLAs) of naïve Bayes tree (NBTree), J48 decision tree, alternating decision tree (ADTree), logistic model tree (LMT), and support vector machine (SVM) in land subsidence susceptibility mapping (LSSSM). We employ data from 94 subsidence locations, among which 70% were used to train learning hybrid models and the other 30% were used for validation. In addition, the models’ performance was assessed by ROC-AUC, accuracy, sensitivity, specificity, odd ratio, root-mean-square error (RMSE), kappa, frequency ratio, and F-score techniques. A comparison of the results obtained from the different models reveals that the new DSC-ADTree hybrid algorithm has the highest accuracy (AUC = 0.983) in preparing LSSSMs as compared to other learning models such as DSC-J48 (AUC = 0.976), DSC-NBTree (AUC = 0.959), DSC-LMT (AUC = 0.948), DSC-SVM (AUC = 0.939), and DSC (AUC = 0.911). The LSSSMs generated through the novel scientific approach presented in our study provide reliable tools for managing and reducing the risk of land subsidence.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The models and code that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work was sponsored in part by the key talent introduction project of Xihua University Foundation (Z17109). Dr. Chenchen Fan helped us in revising the manuscript.

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Authors and Affiliations

  1. School of Energy and Power Engineering, Xihua University, Chengdu, 610039, China

    Rui Zhao

  2. Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu, 610039, China

    Rui Zhao

  3. Department of Geomorphology, Tarbiat Modares University, Jalal Ale Ahmad Highway, Tehran, 9821, Iran

    Alireza Arabameri

  4. School of Earth Sciences and Resources, China University of Geosciences, Beijing, China

    M. Santosh

  5. Department of Earth Sciences, University of Adelaide, Adelaide, South Australia, Australia

    M. Santosh

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  1. Rui Zhao
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  3. M. Santosh
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by AA. The first draft of the manuscript was written by AA, MS, and RZ commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Alireza Arabameri.

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Zhao, R., Arabameri, A. & Santosh, M. Land subsidence susceptibility mapping: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithms. Environ Sci Pollut Res 31, 15443–15466 (2024). https://doi.org/10.1007/s11356-024-32075-w

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