E3S Web Conf.
Volume 38, 20182018 4th International Conference on Energy Materials and Environment Engineering (ICEMEE 2018)
|Number of page(s)||4|
|Section||Environmental Science and Environmental Engineering|
|Published online||04 June 2018|
Experimental study of overland flow resistance coefficient model of grassland based on BP neural network
Yellow River Institute of Hydraulic Research, Key Laboratory of Soil and Water Loss Process and Control on the Loess Plateau of Ministry of Water Resources, Zhengzhou 450003, China
* Corresponding author: firstname.lastname@example.org
The overland flow resistance on grassland slope of 20° was studied by using simulated rainfall experiments. Model of overland flow resistance coefficient was established based on BP neural network. The input variations of model were rainfall intensity, flow velocity, water depth, and roughness of slope surface, and the output variations was overland flow resistance coefficient. Model was optimized by Genetic Algorithm. The results show that the model can be used to calculate overland flow resistance coefficient, and has high simulation accuracy. The average prediction error of the optimized model of test set is 8.02%, and the maximum prediction error was 18.34%.
© The Authors, published by EDP Sciences, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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