Using the General Regression Neural Network Method to Calibrate the Parameters of a Sub-Catchment

Qing-Chi Cai,Jen-Yang Lin,Tsung-Hung Hsu

doi:10.3390/w13081089

Qing-Chi Cai, Jen-Yang Lin + Show 1 more

Open Access

https://doi.org/10.3390/w13081089

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Abstract

Computer software is an effective tool for simulating urban rainfall–runoff. In hydrological analyses, the storm water management model (SWMM) is widely used throughout the world. However, this model is ineffective for parameter calibration and verification owing to the complexity associated with monitoring data onsite. In the present study, the general regression neural network (GRNN) is used to predict the parameters of the catchment directly, which cannot be achieved using SWMM. Then, the runoff curve is simulated using SWMM, employing predicted parameters based on actual rainfall events. Finally, the simulated and observed runoff curves are compared. The results demonstrate that using GRNN to predict parameters is helpful for achieving simulation results with high accuracy. Thus, combining GRNN and SWMM creates an effective tool for rainfall–runoff simulation.

Highlights

Water 2021, 13, 1089. https://Urbanization has resulted in land use changes that include increased amounts of impervious pavement in cities
storm water management model (SWMM) contains a flexible set of hydraulic modeling capabilities that enables it to account for hydrologic processes, estimate the production of pollutant loads, and evaluate the performance of green infrastructure under single-event or long-term precipitation
SWMM is widely adopted in Taiwan for hydrological research owing to its high potentiality [16,17,18,19]

Summary

Introduction

Urbanization has resulted in land use changes that include increased amounts of impervious pavement in cities. This has affected the original water cycle in such cities by increasing urban surface runoff volume, the confluence time of urban flooding, and water pollution [1,2,3], which have led to significant human and economic casualties. Numerical hydrologic modeling is a primary tool for designing site drainage plans and assessing their potential improvement over the original conditions. Many numerical hydrologic models are available, including the storm water management model (SWMM), soil and water assessment tool (SWAT), the Hydrologic. SWMM is the most widely used worldwide for urban watershed hydrology and water quality modeling [15]. SWMM is widely adopted in Taiwan for hydrological research owing to its high potentiality [16,17,18,19]

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