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Abstract
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Reliable flash flood susceptibility maps are a vital tool for land planners and emergency management officials for early flood warning and mitigation. We have developed a new ensemble learning model that predicts flash flood susceptibility at Haraz, Iran. The new model couples a Bayesian Belief Network (BBN) model with an extreme learning machine (ELM) and backpropagation (BP) structure optimized by a genetic algorithm (GA) named GA-BN-NN model. We applied the support vector machine (SVM) technique to a database of 194 flood locations with ten conditioning factors. An artificial neural network (ANN) algorithm with a multi-layer perceptron function, MLP-BP, optimized by a genetic algorithm, GA-MLP, and a shuffled frog-leaping algorithm, SFLA-MLP, were used as benchmark models for assessing the power prediction of the proposed model. Statistical measures, including sensitivity, specificity, accuracy, F1-measure and Jaccard coefficient, and root mean square error, were used to evaluate the goodness-of-fit and prediction accuracy, respectively, of the training and testing datasets. We found that all ten factors are positively correlated with flood occurrence, but slope angle has the highest average merit (AM9.7) and thus contributes most to the occurrence of flooding. Results indicate that the GA-BN-NN model has the highest goodness-of-fit and prediction accuracy (AUC0.966) and hence outperforms other ensemble learning models that we tested — the SFLA-MLP, MLP-BP, and GA-MLP models. We thus conclude that the proposed model is a promising technique for managing risk in flood-prone areas around the world.
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