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: email@example.com
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/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.