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Title: | A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction |
Authors: | Adnan, MSG Siam, ZS Kabir, I Kabir, Z Ahmed, MR Hassan, QK Rahman, RM Dewan, A |
Keywords: | flood susceptibility;machine learning algorithm;uncertainty analysis;GIS;remote sensing |
Issue Date: | 23-Nov-2022 |
Publisher: | Elsevier |
Citation: | Adnan, M.S.G. et al. (2023) 'A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction', Journal of Environmental Management, 326, 116813, pp. 1 - 14. doi: 10.1016/j.jenvman.2022.116813. |
Abstract: | 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. |
Description: | Data availability:
Data will be made available on request. Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0301479722023866?via%3Dihub#appsec1 . |
URI: | https://bura.brunel.ac.uk/handle/2438/29114 |
DOI: | https://doi.org/10.1016/j.jenvman.2022.116813 |
ISSN: | 0301-4797 |
Other Identifiers: | ORCiD: Mohammed Sarfaraz Gani Adnan https://orcid.org/0000-0002-7276-1891 ORCiD: Zakaria Shams Siam https://orcid.org/0000-0002-7502-2285 ORCiD: Irfat Kabir https://orcid.org/0000-0002-9874-0503 ORCiD: M. Razu Ahmed https://orcid.org/0000-0002-4771-3353 ORCiD: Rashedur M. Rahman https://orcid.org/0000-0002-4514-6279 116813 |
Appears in Collections: | Dept of Civil and Environmental Engineering Research Papers |
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FullText.pdf | Copyright © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | 12.25 MB | Adobe PDF | View/Open |
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