Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm

Abstract

Flash floods are responsible for loss of life and considerable property damage in many countries. Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by land-use planners and emergency managers. The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach (DBPGA) based on Deep Belief Network (DBN) with Back Propagation (BP) algorithm optimized by the Genetic Algorithm (GA). For this task, a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation (ORAE) technique. Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model. Statistical metrics include sensitivity, specificity accuracy, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC) were used to assess the validity of the proposed model. The result shows that the proposed model has the highest goodness-of-fit (AUC = 0.989) and prediction accuracy (AUC = 0.985), and based on the validation dataset it outperforms benchmark models including LR (0.885), LMT (0.934), BLR (0.936), ADT (0.976), NBT (0.974), REPTree (0.811), ANFIS-BAT (0.944), ANFIS-CA (0.921), ANFIS-IWO (0.939), ANFIS-ICA (0.947), and ANFIS-FA (0.917). We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.