| A hillslope infiltration and runoff prediction model of neural networks optimized by genetic algorithm |
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| DOI：10.7606/j.issn.1000-7601.2011.02.36 |
| Key Words: neural networks genetic algorithm runoff infiltration prediction model |
| Author Name | Affiliation | | BAI Peng | Northwest Key Laboratory of Water Resources and Environment Ecology, Ministry of Education, Xi’an University of Technology,Xi’an, Shaanxi 710048,China | | SONG Xiaoyu | Northwest Key Laboratory of Water Resources and Environment Ecology, Ministry of Education, Xi’an University of Technology,Xi’an, Shaanxi 710048,China | | WANG Juan | Northwest Key Laboratory of Water Resources and Environment Ecology, Ministry of Education, Xi’an University of Technology,Xi’an, Shaanxi 710048,China | | SHI Wenjuan | Northwest Key Laboratory of Water Resources and Environment Ecology, Ministry of Education, Xi’an University of Technology,Xi’an, Shaanxi 710048,China | | WANG Quanjiu | Northwest Key Laboratory of Water Resources and Environment Ecology, Ministry of Education, Xi’an University of Technology,Xi’an, Shaanxi 710048,China
State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Yangling, Shaanxi 712100,China |
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| Abstract: |
| The method of back-propagation neural networks optimized by genetic algorithms was used to establish a
hillslope runoff and infiltration model. The rainfall intensity，rainfall duration, initial soil water content and slope gradi-ent were selected as the model inputs，and the runoff volume and infiltration volume were the model outputs. Through simulating and predicting， the results showed that simulation mean reletive errors were respectively 6.32% and 1.93%, and the prediction mean reletive errors were 5.71%0 and 1.92%. In order to compare the prediction effects of the model with those of other models, the unoptimized back-propagation neural network model and the Philip regression model under the condition of fixed rainfall intensity were applied to predict the infiltration amount，and the comprasion results showed that the mean reletive errors of the three models in infitration amount prediction were 1.92%, 5.29% and 9.10%, respectively，while the maximum mean reletive errors were 6.48%, 25.88%，20.36%，which showed that the prediction effecets of optimized back-propagation networks had better peformance than the other two models obviously. |
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