| 基于Logistic回归和RBF神经网络的土壤侵蚀模数预测 |
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| 引用本文： | 周宁,李超,满秀玲.基于Logistic回归和RBF神经网络的土壤侵蚀模数预测[J].水土保持通报,2015,35(3):235-241. |
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| 作者姓名： | 周宁 李超 满秀玲 |
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| 作者单位： | 东北林业大学林学院;黑龙江省水土保持科学研究院;北京林业大学林学院 |
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| 基金项目： | 国家自然科学基金项目“大兴安岭森林流域水文过程对植被和气候变化的响应”(31170420) |
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| 摘 要： | 目的]寻求估算土壤侵蚀模数的新方法,并通过GIS实现对土壤侵蚀空间分布情况的预测。方法]采用土壤侵蚀模数作为判别条件,分别验证基于Logistic回归和RBF神经网络而建立的土壤侵蚀预报模型的适用性,进而构建并验证改进模型——LOG-RBF神经网络土壤侵蚀预测模型。结果](1)Logistic回归模型判别目标土地是否发生土壤侵蚀的优势明显,未发生和发生土壤侵蚀的预测正确率分别为77.4%和97.9%,总预测正确率为94.9%。(2)RBF神经网络模型估计土壤侵蚀模数的能力较强,模拟结果的相对误差和平方和误差分别为0.612%和13.292,R2为0.57。(3)LOG-RBF神经网络土壤侵蚀预测模型预测结果的相对误差和平方和误差比RBF神经网络模型模拟结果分别降低了0.157%和2.601。R2为0.82,拟合程度上优于RBF神经网络模型。随着土壤侵蚀模数的增大,错估现象呈逐渐减少趋势。通过受试者工作特征曲线的判别,LOG-RBF神经网络模型的曲线下面积值比RBF神经网络模型大0.063,模型判断的准确性更高。结论]利用LOG-RBF神经网络土壤侵蚀预测模型可更准确地估计土壤侵蚀模数,基于GIS能够预测土壤侵蚀的空间分布情况。
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| 关 键 词： | Logistic回归 RBF神经网络 土壤侵蚀 预测模型 USLE |
| 收稿时间： | 4/7/2014 12:00:00 AM |
| 修稿时间： | 2014/5/20 0:00:00 |
Prediction of Soil Erosion Modulus Based on Logistic Regression and RBF Neural Network |
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| ZHOU Ning,LI Chao and MAN Xiuling.Prediction of Soil Erosion Modulus Based on Logistic Regression and RBF Neural Network[J].Bulletin of Soil and Water Conservation,2015,35(3):235-241. |
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| Authors: | ZHOU Ning LI Chao and MAN Xiuling |
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| Institution: | ZHOU Ning;LI Chao;MAN Xiuling;College of Forestry,Northeast Forestry University;Heilongjiang Institute of Soil and Water Conservation;College of Forestry,Beijing Forestry University; |
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| Abstract: | Objective] To found a new approach to estimate soil erosion modulus, and achieve predictions of spatial distribution of soil erosion based on GIS. Methods] Taking soil erosion modulus as discriminant conditions, each applicability of soil erosion prediction model built based on Logistic regression and RBF neural network was validated, and then the improved model(soil erosion prediction model) based on LOG-RBF neural network was built and validated. Results] (1) There was obvious advantage for Logistic regression model to discriminant the occurrence of soil erosion, and the accuracy of prediction for un-occurring and occurring was 77.4% and 97.9%, respectively, the total predictive accuracy was 94.9%. (2) RBF neural network model had the stronger ability to estimate soil erosion modulus, the relative error and error sum of squares of the simulation results was 0.612% and 13.292, respectively, and R2 was 0.57. (3) Relative error and error sum of squares of the simulation results was decreased by 0.157% and 2.601, respectively based on LOG-RBF neural network model than RBF neural network model, and R2 was 0.82, so LOG-RBF neural network model had a better fitting degree, and with the soil erosion modulus increase, misjudge phenomenon showed a trend of gradual reduction. Determined by receiver operating characteristic curve, the value of area under curve based on LOG-RBF neural network model was 0.063 larger than RBF neural network model, and the accuracy was higher. Conclusion] LOG-RBF neural network model could be used to estimate soil erosion modulus, and predict spatial distribution of soil erosion based on GIS. |
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| Keywords: | Logistic regression RBF neural network soil erosion prediction model USLE |
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