Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling

Abstract

Flood spatial susceptibility prediction is the first essential step in developing flood mitigation strategies and reducing flood damage. Flood occurrence is a complex process that is not easily predicted through simple methods. This study describes optimization of support vector regression (SVR) using meta-optimization algorithms including the grasshopper optimization algorithm (GOA) and particle swarm optimization (PSO) for flood modeling at Qazvin Plain, Iran. Geospatial data including nine readily available geo-environmental flood conditioning factors (i.e., ground slope, aspect, elevation, planform curvature, profile curvature, proximity to a river, land use, lithology and rainfall) were derived. The information gain ratio (IGR) method was used to determine the relative importance of input variables. A historical flood inventory map for 43 locations was created from existing reports. The geospatial data and historical flood levels were used to construct the training and testing datasets. Then, the training dataset was used to generate flood-susceptibility maps using the optimized SVR model with the GOA and PSO algorithms. Finally, the predictive accuracy of the models was quantified using the statistical measures of root mean square error (RMSE), mean absolute error (MAE), and area under the receiver operating characteristic (ROC) curve (AUC). Although both the GOA and PSO algorithms improved SVR performance, the SVR-GOA model performed best (AUC = 0.959, RMSE = 0.31 and MSE = 0.098), followed by the SVR-PSO model (AUC = 0.959, RMSE = 0.33 and MSE = 0.11) and standalone SVR model (AUC = 0.87, RMSE = 0.35 and MSE = 0.12). Elevation, lithology and aspect had the highest IGR values and were identified as the most effective predictors of flood susceptibility.