Study on runoff production model by artificial neural network and particle swarm algorithm
Huang Jun1,2, Kang Qing1,2, Jin Pingwei1,2, Li Lanbin1,2, Li Hongjun3, Jiang Xuebing1,2
1.Pearl River Hydraulic Research Institute, Pearl River Water Resources Commission of the Ministry of Water Resources, Guangzhou, Guangdong 510611, China; 2.Soil and Water Conservation Monitoring Center of Pearl River Basin, Pearl River Water Resources Commission of the Ministry of Water Resources, Guangzhou, Guangdong 510611, China; 3.Soil and water conservation monitoring stations of Guangdong province, Guangzhou, Guangdong 510611, China
Abstract:Two runoff production models are built by using artificial neural network and particle swarm algorithm, respectively, to study the relationships between runoff production and related factors based on existing multiple scale field data. The results showed that the relationships between runoff production and slope length, width, gradient and antecedent soil water are parabolic, but those between runoff production and vegetation cover and rainfall can be described with power and linear functions, respectively. The optimum topological structure and the network parameters of the BP model for predicating runoff production are determined by means of weighted summation of relative difference, and the relative error between the model estimate and observed data is within ?20%, showing a good prediction accuracy. According to the quantitative relationship between runoff production and each factor, the empirical model of runoff production is established, and the particle swarm algorithm is used to determine model parameters. Its relative error of the empirical model is ?30%, slightly poorer than the BP model.
黄俊,, 亢庆,, 金平伟,, 李岚斌,, 李红军, 姜学兵,. 基于神经网络和粒子群算法产流量模型构建[J]. 排灌机械工程学报, 2015, 33(9): 779-786.
Huang Jun,, Kang Qing,, Jin Pingwei,, Li Lanbin,, Li Hongjun, Jiang Xuebing,. Study on runoff production model by artificial neural network and particle swarm algorithm. Journal of Drainage and Irrigation Machinery Engin, 2015, 33(9): 779-786.
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