Comparative Assessment of Data Mining Techniques for Flash Flood Prediction

Abstract Data mining techniques have recently drawn considerable attention from the research community for their ability to predict flash flood phenomena. These techniques can bring large-scale flood data into real practice and have become the necessary tools for impact assessment, societal resilience, and disaster control. Although numerous studies have been conducted on data mining techniques and flash flood predictions, domain-specific flash flood prediction models based on existing data mining techniques are still lacking. Notably, this study has focused on the performance of four data mining techniques, namely, logistic regression (LR), artificial neural networks (ANN), k-nearest neighbour (kNN), and support vector machine (SVM) in a comparative assessment as prediction models. The area under the curve (AUC) was utilised to validate these models. The value of AUC was higher than 0.9 for all models. Accordingly, the outcomes outlined in this study can contribute to Halim et al. the current literature by boosting the performance of data mining techniques for predicting flash floods through a comparison of the most recent data mining techniques. Keywords: Artificial neural networks (ANN), Flash flood, k-nearest neighbor (kNN), Logistic regression (LR), Support vector machine (SVM)

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