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

Shaniel Chotkan, Raymond van der Meij, Wouter Jan Klerk, Phil J. Vardon, Juan Pablo Aguilar-López*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.

Original languageEnglish
Article number6820
Number of pages23
JournalSustainability (Switzerland)
Volume14
Issue number11
DOIs
Publication statusPublished - 2022

Keywords

  • drought
  • hydrology
  • levees
  • machine learning

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