Flood Susceptibility Modeling in the uMhlatuzana River Catchment using Computer Vision-Based Deep Learning Techniques

Abstract

In this study, covolutional neural networks (CNN) models are employed for flood susceptibility modeling in the uMhalatuzana River catchment in KwaZulu-Natal, South Africa. The CNN models, including 1D-CNN, 2D-CNN, and 3D-CNN, pro-vide a detailed assessment of flood vulnerability in the region. The models use di- verse spatial information, such as topography, land use, and hydrological features, to estimate the likelihood of flooding in different areas of the catchment. The flood susceptibility maps within the uMhalatuzana River catchment, classified into five risk zones namely, ‘very low’, ‘low’, ‘moderate’, ‘high’ and ‘very high’ susceptibility zone, serve as proactive instruments for risk mitigation and disaster management. The 1D-CNN model displays strong overall performance in flood susceptibility modeling, evident in key metrics such as accuracy, precision, recall, area under curve (AUC) score, and F1-score. The results suggest that the model effectively captures patterns in the input data, emphasizing its potential for flood susceptibility modeling. Moreover, the 2D-CNN model outperforms the 1D-CNN, achieving higher values when evaluated using various performance metrics. Finally, the 3D-CNN model outperformed both the 1D-CNN and 2D-CNN, emphasizing its predictive abilities in flood susceptibility modelling. The flood susceptibility maps produced by the 1D-CNN model, shows that most of the study area exhibits very low flood susceptibility (96.4%), with localized areas of higher susceptibility, particularly in the very high-risk category (2.53%). The 2D CNN model demonstrates a more diverse risk distribution, with a substantial portion having very low susceptibility (74.19%) and significant areas of higher risk, notably in the very high-risk category (10.93%). The 3D-CNN model emphasizes a spatial pattern where a large portion has very low susceptibility (84.10%), but with a concentration of high and very high-risk areas, comprising 12.34% of the total area. Finally, the consistent identification of higher risk susceptibility areas enhances the robustness of the assessments. The models’ high accuracy and detailed risk assessments provide valuable tools for decision-makers, urban planners, and emergency response teams in the uMhalatuzana River catchment. The precision of the models facilitates informed strategies for flood risk management, including targeted interventions such as improved drainage systems and early warning systems.