Treffer: Flood prediction using nonlinear instantaneous unit hydrograph and deep learning: A MATLAB program.

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 (C N) 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. • We have developed a MATLAB program for flood prediction. • IUH generation module for surface runoff utilizes a nonlinear power law-based interpolation. • LSTM module employs a deep learning model to estimate the curve number and rainfall loss. • Flood hydrograph generation module predicts runoff by - convolution integration. • The applications to small and large watersheds showed reasonable accuracies. [ABSTRACT FROM AUTHOR]

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