Enhancing a convolutional neural network model for land subsidence susceptibility mapping using hybrid meta-heuristic algorithms

Managing natural hazards such as land subsidence (LS) is important because they cause large economic and human loss. LS has become a significant challenge in South Korea due to its many abandoned coal mines. Therefore, preparing LS zoning maps is vital to controlling damage caused by LS. In this study, genetic algorithm (GA) and binary whale optimization algorithm were used to select the most influential conditioning factors from the initial set of 19 factors. Subsequently, the whale optimization algorithm, and Laplacian whale optimization algorithm (LXWOA) were utilized to determine the optimal hyperparameter values for the convolutional neural network (CNN) model. The results showed that the CNN-GA-LXWOA algorithm provided a more accurate and reliable LS susceptibility map, with an area under the receiver operating characteristic curve of 0.9606, root mean square error of 0.2974 and accuracy of 91.09%. This algorithm covered 95.6% of past subsidence occurrences in the high and very high LS susceptibility classes, demonstrating its suitability for predicting future subsidence areas. We found that six main factors (elevation, drift, lineament density, slope, distance to railroads, and railroad density) control LS occurrence in Taebaek, followed by groundwater depth, lithology, and profile curvature. The proposed model may be applied for other regions with different parameters and environmental factors due to its flexible structure. The final map created in this study provides a useful tool for better LS management to mitigate the adverse effects of this natural hazard in the study area.