Kumar Sachin, Choudhary Mahendra Kumar, Thomas T
Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, 462003, India.
National Institute of Hydrology, Bhopal, 462003, India.
Sci Rep. 2024 Nov 1;14(1):26263. doi: 10.1038/s41598-024-77655-5.
Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANN, which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANN model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANN model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANN model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments.
准确的径流预测对于有效的水资源管理和规划至关重要。本研究旨在通过提出一种新颖的混合方法——人工神经网络（ANN），提高数据稀缺的讷尔默达河上游流域（UNB）的径流模拟精度，该方法将基于物理的模型（WEAP）与数据驱动的模型（ANN）相结合。使用观测到的径流数据对WEAP模型进行校准和验证，而使用气象变量和模拟径流对ANN模型进行训练和测试。ANN模型整合了来自WEAP和ANN的模拟流量，以提高预测精度。结果表明，ANN模型优于独立的WEAP模型和ANN模型，在训练期和测试期的NSE值分别更高，为95.5%和92.3%，同时R值高达0.96。改进后的径流预测可为与水资源分配、水库运行以及洪水和干旱风险评估相关的更好决策提供支持。本研究的新颖之处在于开发了ANN模型，该模型利用了基于物理和数据驱动方法的优势，以提高数据有限地区的径流模拟精度。所提出的方法为UNB和其他类似流域的可持续水资源管理策略提供了一个有前景的工具。
