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

• Published in: Journal of environmental management Vol. 326; no. Pt B; p. 116813
• Main Authors: Adnan, Mohammed Sarfaraz Gani, Siam, Zakaria Shams, Kabir, Irfat, Kabir, Zobaidul, Ahmed, M. Razu, Hassan, Quazi K., Rahman, Rashedur M., Dewan, Ashraf
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.01.2023
• Subjects: Bangladesh; coasts; environmental management; Flood susceptibility; GIS; Machine learning algorithm; neural networks; prediction; regression analysis; Remote sensing; uncertainty; Uncertainty analysis; Bangladesh; GIS; Uncertainty analysis; Remote sensing; Flood susceptibility; Machine learning algorithm
• ISSN: 0301-4797; 1095-8630; 1095-8630
• DOI: 10.1016/j.jenvman.2022.116813

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. •Uncertainty exists in machine learning (ML)-based flood susceptibility models (FSMs).•Four standalone and hybridized ML-based FSMs were established.•An optimized model was developed to address uncertainties in ML-based FSMs.•Spatial association of flood probability between various models ranging from 0.62 to 0.91.•The optimized model reduced uncertainty and increased prediction accuracy.