A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction

Adnan, Mohammed Sarfaraz Gani and Siam, Zakaria Shams and Kabir, Irfat and Kabir, Zobaidul and Ahmed, M. Razu and Hassan, Quazi K. and Rahman, Rashedur M. and Dewan, Ashraf (2023) A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction. Journal of Environmental Management, 326 (Part B). 116813. ISSN 0301-4797 (https://doi.org/10.1016/j.jenvman.2022.116813)

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Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.