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| BP neural network modeling on soil detachment rate of road |
| Dong Jianzhi,Cao Longxi,Zhang Keli |
| State Key Laboratory of Earth Surface Processes and Resource Ecology,School of Geography and Remote Sensing Science,Beijing Normal University,100875,Beijing,China |
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Abstract Soil detachment is one of the key processes of soil erosion,and it provides material for transportation.It is important to predict soil detachment rate precisely both for better understanding of soil erosion process and soil erosion modeling.This paper used both BP neural network model and regression models to simulate soil detachment rate of road with the soil detachment rate data obtained from flume experiment in a large scale of slope gradient(8.8%-46.6%) and flow rate(1-5 L/s),and compared the results of two means.The results showed that: BP neural network model can predict soil detachment rate very well with the data which are easily obtained,including slope gradient,flow rate and road type;BP neutral network model improved the accuracy of regression model in predicting soil detachment rate in every type of road.Since BP neutral network model can combine the different road types,different flow rates and different slope gradients into one model,it can improve the efficiency of predicting soil detachment of road and provide a new approach to simulate soil detachment rate of road.
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Received: 21 November 2009
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