Print Email Facebook Twitter A Data-Driven Method for Identifying Drought-Induced Crack-Prone Levees Based on Decision Trees Title A Data-Driven Method for Identifying Drought-Induced Crack-Prone Levees Based on Decision Trees Author Chotkan, S.A. (TU Delft Hydraulic Structures and Flood Risk) van der Meij, Raymond (Deltares) Klerk, Wouter Jan (Deltares) Vardon, P.J. (TU Delft Geo-engineering) Aguilar Lopez, J.P. (TU Delft Hydraulic Structures and Flood Risk) Date 2022 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. Subject droughthydrologyleveesmachine learning To reference this document use: http://resolver.tudelft.nl/uuid:e594e03d-0020-4067-b8a6-b806be9cf508 DOI https://doi.org/10.3390/su14116820 ISSN 2071-1050 Source Sustainability, 14 (11) Part of collection Institutional Repository Document type journal article Rights © 2022 S.A. Chotkan, Raymond van der Meij, Wouter Jan Klerk, P.J. Vardon, J.P. Aguilar Lopez Files PDF sustainability_14_06820.pdf 6.82 MB Close viewer /islandora/object/uuid:e594e03d-0020-4067-b8a6-b806be9cf508/datastream/OBJ/view