School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, 450001, China.
School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, 450001, China.
J Environ Manage. 2024 Jul;364:121466. doi: 10.1016/j.jenvman.2024.121466. Epub 2024 Jun 12.
One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time series prediction models. However, the LSTM model has issues of underestimating peak flows and poor robustness in flood forecasting applications. Therefore, based on a thorough analysis of complex underlying surface attributes, this study proposes a framework for distinguishing runoff models and integrates a Grid-based Runoff Generation Model (GRGM). Simultaneously considering the time series characteristics of runoff processes, including rising, peak, and recession, a runoff process vectorization (RPV) method is proposed. In this study, a hybrid deep learning flood forecasting framework, GRGM-RPV-LSTM, is constructed by coupling the GRGM, RPV, and LSTM neural network models. Taking the Jialu River in the Zhongmu station control basin as an example, the model is validated using 18 instances of measured floods and compared with the LSTM and GRGM-LSTM models. The study shows that the GRGM model has a relative error and average coefficient of determination for simulating runoff of 8.41% and 0.976, respectively, indicating that considering the spatial distribution of runoff patterns leads to more accurate runoff calculations. Under the same lead time conditions, the GRGM-RPV-LSTM hybrid forecasting model has a Nash efficiency coefficient greater than 0.9, demonstrating better simulation performance compared to the GRGM-LSTM and LSTM models. As the lead time increases, the GRGM-RPV-LSTM model provides more accurate peak flow predictions and exhibits better robustness. The research findings can provide scientific basis for coordinated management of flood control and disaster reduction in watersheds.
流域洪水预测和减灾的一个重要非工程措施是应用机器学习洪水预测模型，其中长短期记忆 (LSTM) 是最具代表性的时间序列预测模型之一。然而，LSTM 模型在洪水预测应用中存在低估洪峰流量和鲁棒性差的问题。因此，本研究在深入分析复杂下垫面属性的基础上，提出了一种区分径流模型的框架，并集成了基于网格的径流生成模型 (GRGM)。同时考虑到径流过程的时间序列特征，包括上升、峰值和退水，提出了径流过程矢量化 (RPV) 方法。本研究通过耦合 GRGM、RPV 和 LSTM 神经网络模型，构建了一种混合深度学习洪水预测框架 GRGM-RPV-LSTM。以中牟站控制流域的贾鲁河为例，采用 18 场实测洪水对模型进行验证，并与 LSTM 和 GRGM-LSTM 模型进行比较。研究表明，GRGM 模型模拟径流的相对误差和平均决定系数分别为 8.41%和 0.976，表明考虑径流模式的空间分布可以提高径流计算的准确性。在相同的预见期条件下，GRGM-RPV-LSTM 混合预测模型的纳什效率系数大于 0.9，表明与 GRGM-LSTM 和 LSTM 模型相比，具有更好的模拟性能。随着预见期的增加，GRGM-RPV-LSTM 模型提供了更准确的洪峰流量预测，并且具有更好的鲁棒性。研究结果可为流域防洪减灾的协同管理提供科学依据。
