In this study, we developed a MATLAB program for flood prediction in a watershed. The program consists of three modules. The instantaneous unit hydrograph (IUH) generation module utilizes a power-law based interpolation method to generate IUHs. The generated IUH is a function of the rainfall excess intensity and therefore considers nonlinearity. The long short-term memory (LSTM) module employs “lstmLayer” from the MATLAB deep learning toolbox to predict total rainfall excess; this is then used to estimate the curve number () value for each flood event. The LSTM module uses a land surface modeling dataset and rainfall-runoff data as inputs. The flood hydrograph generation module calculates effective rainfall hyetographs and then predicts flood hydrographs using a convolution integration. A detailed description of the program is provided along with an application example for real watersheds. The application results demonstrated that our program can be effectively used for flood prediction in practice, particularly for large flood events.

中文翻译：

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使用非线性瞬时单位过程线和深度学习进行洪水预测：MATLAB 程序

在本研究中，我们开发了一个用于流域洪水预测的 MATLAB 程序。该计划由三个模块组成。瞬时单位过程线 (IUH) 生成模块利用基于幂律的插值方法来生成 IUH。生成的 IUH 是降雨过剩强度的函数，因此考虑了非线性。长短期记忆 (LSTM) 模块采用 MATLAB 深度学习工具箱中的“lstmLayer”来预测总降雨量过剩；然后将其用于估计每个洪水事件的曲线编号 () 值。LSTM 模块使用地表建模数据集和降雨径流数据作为输入。洪水过程线生成模块计算有效降雨量过程线，然后使用卷积积分预测洪水过程线。提供了该程序的详细描述以及真实流域的应用示例。应用结果表明，我们的程序可以有效地用于实践中的洪水预测，特别是大型洪水事件。