Experimental study of overland flow resistance coefficient model of grassland based on BP neural network

Peng Jiao, Er Yang, Yong Xin Ni, M. Mostafa
2018 E3S Web of Conferences  
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
more » ... ance 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%.
doi:10.1051/e3sconf/20183801030 fatcat:5ekacd6p7nhqvg4qic6usc2kay