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A Data-Driven Method for Identifying Drought-Induced Crack-Prone Levees Based on Decision Trees

Author

Listed:
  • Shaniel Chotkan

    (Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands)

  • Raymond van der Meij

    (Deltares, 2629 HV Delft, The Netherlands)

  • Wouter Jan Klerk

    (Deltares, 2629 HV Delft, The Netherlands)

  • Phil J. Vardon

    (Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands)

  • Juan Pablo Aguilar-López

    (Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands)

Abstract

In this paper, we aim to identify factors affecting susceptibility to drought-induced cracking in levees and use them to build a machine learning model that can identify crack-prone levees on a regional scale. By considering the key relationship between the size of cracks and the moisture content, we observed that low moisture contents act as an important driver in the cracking mechanism. In addition, factors which control the deformation at low moisture content were seen to be important. Factors that affect susceptibility to cracking were proposed. These factors are precipitation, evapotranspiration, soil subsidence, grass color, soil type, peat layer thickness, soil stiffness and levee orientation. Statistics show that the cumulative precipitation deficit is best associated with the occurrence of the cracks (cracks are characterized by higher precipitation deficits). Model tree classification algorithms were used to predict whether a given input of the factors can lead to cracking. The performance of a model predicting long cracks was evaluated with a Matthews correlation coefficient (MCC) of 0.31, while a model predicting cracks in general was evaluated with an MCC of 0.51. Evaluation of the model trees indicated that the peat thickness, the soil stiffness and the orientation of the levee can be used to determine crack-proneness of the levees. To maintain validity and usefulness of the data-driven models, it is important that asset managers of levees also register locations on which no cracks are observed.

Suggested Citation

  • Shaniel Chotkan & Raymond van der Meij & Wouter Jan Klerk & Phil J. Vardon & Juan Pablo Aguilar-López, 2022. "A Data-Driven Method for Identifying Drought-Induced Crack-Prone Levees Based on Decision Trees," Sustainability, MDPI, vol. 14(11), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6820-:d:830522
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    References listed on IDEAS

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    1. Mingcheng Zhu & Shouqian Li & Xianglong Wei & Peng Wang, 2021. "Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods," Sustainability, MDPI, vol. 13(7), pages 1-14, March.
    2. Volker Liermann & Sangmeng Li, 2021. "Methods of Machine Learning," Springer Books, in: Volker Liermann & Claus Stegmann (ed.), The Digital Journey of Banking and Insurance, Volume III, pages 225-238, Springer.
    3. Sabri Boughorbel & Fethi Jarray & Mohammed El-Anbari, 2017. "Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-17, June.
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