Stacking state-of-the-art ensemble for flash-flood potential assessment

Abstract

It is clear that flash flood events are on the rise, and the detection of flash flood-prone areas has now become a top priority for governments throughout the world. Flash-flood potential mapping can provide very useful information that can be used in order to adopt the most appropriate measures to reduce the risk of these phenomena. A precise model is needed in order to predict flash flood potential, which can help to improve flood management strategies, as it can be used to develop more effective flood management methods. In this present research, Index of Entropy (IOE), Logistic Regression (LR), four machine learning models (, Support Vector Machine (SVM), Classification and Regression Tree (CART), Naive Bayes (NB), k-Nearest Neighbour (k-NN)) and their 5 stacking ensembles were used to construct a new GIS-based framework for flash flood-potential mapping in the Suha River Basin, Romania. For the purposes of running the models, 8 topographical and environmental factors (slope, land use, plan curvature, profile curvature, convergence index, aspect, hydrological soil groups and tpi), along with 111 torrential locations were used to run the five applied models. In order to facilitate training and validation purposes, the entire dataset was divided into 70:30 parts. ROC Curve and statistical measures were employed in order to verify the model reliability and also validate the results. It is noteworthy here that in the training procedure, the stacking ensemble constructed through the combination of all machine learning and statistical learning models (LCkNS) was better than all the other models with a maximum accuracy of 0.972. The models used, revealed that between 10% and 22% of Suha river basin is occupied by high and very high flash-flood potential. As a result, the data generated by this research provides reliable results that can assist policymakers at the local and national levels in developing a concrete strategic plan to reduce flood frequency in their area by utilizing a flood warning system.