Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search

摘要

Floods are among the deadliest natural hazards for humans and the environment. Identifying the most floodsusceptible areas is a fundamental step in the development of flood mitigation strategies and for reducing flood damage. There is an ongoing global debate regarding the most suitable model for flood-susceptibility modeling and predictions. There is also a growing interest in the development of parsimonious and precise models for flood-susceptibility prediction. This study proposed several novel hybrid intelligence models based on the metaoptimization of the support vector regression (SVR) and group method of data handling (GMDH) using different meta-heuristic algorithms, i.e., the genetic algorithm (GA) and harmony search (HS). In contrast to the traditional models, in the SVR model computational complexity does not depend on the dimensionality of the input space. GMDH model has also advantage of being appropriate to analyze multi-parametric data sets. The methodology was developed for the Haraz-Neka watershed, one of the most flood-prone areas in the coastal margins of the Caspian Sea. A total of nine geospatial parameters (slope degree, aspect, elevation, plan curvature,profile curvature, distance to the river, land use, lithology, and rainfall) were identified as the main floodconditioning factors using information gain ratio (IGR) analyses. Based on existing reports, 132 flood locations were identified in the study area, 92 points (70) were used together with geospatial data for flood-susceptibility modeling, and the remaining 40 points (30) were used to validate the models. An initial flood-susceptibility model was constructed based on the SVR and GMDH models. The model parameters were optimized using the GA and HS to reproduce the flood-susceptibility maps. The prediction accuracy of the resultant maps was evaluated in terms of various statistical measures, i.e., mean square error (MSE), root mean square error (RMSE), receiver operating characteristic (RO