Arora Aman, Durga G Purna, Pandey Manish, Arabameri Alireza
Université Gustave Eiffel, GERS-LEE, 44344, Bouguenais, France.
Laboratoire D'Informatique En Image Et, Systems De Information, Institut National Des Sciences Appliquees, 69100, Villeurbanne, France.
Sci Rep. 2025 Sep 24;15(1):32757. doi: 10.1038/s41598-025-07403-w.
The Kosi Megafan, located in the Himalayan foreland basin, is highly susceptible to devastating floods, posing significant threats to lives and livelihoods. Accurate flood susceptibility mapping is crucial for effective flood risk management in this dynamic environment. This study evaluates and optimizes five advanced machine learning algorithms - Random Subspace, J48, Maximum Entropy (MaxEnt), Artificial Neural Network (ANN-MLP), and Biogeography-Based Optimization- for flood susceptibility zonation within the Kosi Megafan. A comprehensive dataset incorporating 19 conditioning factors, derived from ALOS PALSAR DEM, Sentinel-2A, Landsat 5 TM, ENVISAT-1 ASAR (ENVISAT-1 Advanced Synthetic Aperture Radar), and other ancillary data sources, was used to train and validate the models. Model performance was assessed using a suite of metrics, including accuracy, true skill statistics (TSS), sensitivity, specificity, Kappa, AUC, and the Seed Cell Area Index. Notably, the ANN-MLP model demonstrated exceptional performance on the validation dataset, achieving an accuracy of 0.982, TSS of 0.964, and Kappa of 0.964, outperforming the other models. MaxEnt also exhibited strong performance, confirming its robustness in environmental modeling. The analysis of variable importance revealed that normalized difference vegetation index (NDVI), altitude, distance to road, rainfall, and distance to river were the most influential factors governing flood susceptibility in the region. The generated flood susceptibility maps, particularly those derived from the ANN-MLP and MaxEnt models, provide valuable tools for identifying high-risk areas and informing flood mitigation strategies. This study highlights the potential of advanced machine learning techniques, especially ANN-MLP, in significantly improving the accuracy and reliability of flood susceptibility assessments in complex and dynamic environments like the Kosi Megafan, paving the way for more effective flood risk management and disaster preparedness.
位于喜马拉雅前陆盆地的科西巨型扇极易遭受毁灭性洪水侵袭，对生命和生计构成重大威胁。在这种动态环境中，准确的洪水易发性制图对于有效的洪水风险管理至关重要。本研究评估并优化了五种先进的机器学习算法——随机子空间、J48、最大熵（MaxEnt）、人工神经网络（ANN-MLP）和基于生物地理学的优化算法，用于科西巨型扇内的洪水易发性分区。使用了一个综合数据集，该数据集包含从ALOS PALSAR数字高程模型、哨兵-2A、陆地卫星5专题制图仪、环境卫星-1先进合成孔径雷达（ENVISAT-1先进合成孔径雷达）和其他辅助数据源中获取的19个条件因子，用于训练和验证模型。使用一套指标评估模型性能，包括准确率、真技能统计量（TSS）、灵敏度、特异性、卡帕系数、曲线下面积（AUC）和种子细胞面积指数。值得注意的是，ANN-MLP模型在验证数据集上表现出卓越性能，准确率达到0.982，TSS为0.964，卡帕系数为0.964，优于其他模型。MaxEnt也表现出强大性能，证实了其在环境建模中的稳健性。变量重要性分析表明，归一化植被指数（NDVI）、海拔、距道路距离、降雨量和距河流距离是该地区洪水易发性的最具影响力因素。生成的洪水易发性地图，特别是那些来自ANN-MLP和MaxEnt模型的地图，为识别高风险区域和制定洪水缓解策略提供了有价值的工具。本研究强调了先进机器学习技术，特别是ANN-MLP，在显著提高像科西巨型扇这样复杂动态环境中洪水易发性评估的准确性和可靠性方面的潜力，为更有效的洪水风险管理和灾害准备铺平了道路。
