Using machine learning to identify karst sinkholes from LiDAR-derived topographic depressions in the Bluegrass Region of Kentucky

Junfeng Zhu, Adam M. Nolte, Nathan Jacobs, Ming Ye

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Information about the distribution and characteristics of existing sinkholes is critical for understanding karst aquifer systems and evaluating sinkhole hazards. LiDAR provides accurate and high-resolution topographic information and has been used to improve delineation of sinkholes in many karst regions. LiDAR data also reveal many topographic depressions, however, and identifying sinkholes from these depressions through manual visual inspection can be slow and laborious. To improve the efficiency of the identification process, we applied six machine learning methods (logistic regression, naive Bayes, neural network, random forests, RUSBoost, and support vector machine) to a dataset of morphometric characteristics of LiDAR-derived topographic depressions. Sinkhole data from Bourbon, Woodford, and Jessamine Counties in the Bluegrass Region of Kentucky were used to derive the dataset for training and testing the machine learning methods. The dataset consisted of 22,884 records with 10 variables for each record. For each method, a random subset of 80% of the records was used for training and the remaining 20% was used for testing. The test receiver operating characteristic curves showed that all six methods were applicable to the dataset, as demonstrated by all area under the curves (AUCs) being greater than 0.87. Neural network emerged as the method that performed best, with an AUC of 0.95 and a testing average accuracy of 0.85. To further improve the sinkhole mapping process, we subsequently developed a two-step process that combined the trained neural network classifier and manual visual inspection and applied the process to Scott County, also in the Bluegrass region. We were able to locate 97% of the sinkholes in the county by manually inspecting only 27% of the topographic depressions the neural network classified as having relatively high probabilities of being sinkholes. This study showed that machine learning is a promising method for improving sinkhole identification efficiency in karst areas in which high-resolution topographic information is available.

Original languageEnglish
Article number125049
JournalJournal of Hydrology
Volume588
DOIs
StatePublished - Sep 2020

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.

Keywords

  • LiDAR
  • Machine learning
  • Morphometric characteristic
  • Sinkhole
  • Topographic depression

ASJC Scopus subject areas

  • Water Science and Technology

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