Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms

Abstract

AbstractLand degradation has been recognized as one of the most adverse environmental impacts during the last century. The occurrence of sinkholes is increasing dramatically in many regions worldwide contributing to land degradation. The rise in the sinkhole frequency is largely due to human‐induced hydrological alterations that favour dissolution and subsidence processes. Mitigating detrimental impacts associated with sinkholes requires understanding different aspects of this phenomenon such as the controlling factors and the spatial distribution patterns. This research illustrates the development and validation of sinkhole susceptibility models in Hamadan Province, Iran, where a large number of sinkholes are occurring under poorly understood circumstances. Several susceptibility models were developed with a training sample of sinkholes, a number of conditioning factors, and four different statistical approaches: naïve Bayes, Bayes net (BN), logistic regression, and Bayesian logistic regression. Ten conditioning factors were initially considered. Factors with negligible contribution to the quality of predictions, according to the information gain ratio technique, were discarded for the development of the final models. The validation of susceptibility models, performed using different statistical indices and receiver operating characteristic curves, revealed that the BN model has the highest prediction capability in the study area. This model provides reliable predictions on the future distribution of sinkholes, which can be used by watershed and land use managers for designing hazard and land‐degradation mitigation plans.