本文關鍵詞：基于神經網絡模型和地統(tǒng)計學方法的土壤養(yǎng)分空間分布預測，，由筆耕文化傳播整理發(fā)布。

基于神經網絡模型和地統(tǒng)計學方法的土壤養(yǎng)分空間分布預測

Prediction of soil nutrients spatial distribution based on neural network model combined with goestatistics

[1] [2] [3] [4] [5] [6] [7] [8]

LI Qi-quan,WANG Chang-quan,ZHANG Wen-jiang,YU Yong,LI Bing,YANG Juan,BAI Gen-chuan,CAI Yan（1College of Resources and Environment,Sichuan Agricultural University,Chengdu 611130,China;2St

[1]四川農業(yè)大學資源環(huán)境學院,成都611130; [2]四川大學水力學與山區(qū)河流開發(fā)保護國家重點實驗室,成都610065; [3]四川農業(yè)大學林學院,四川雅安625014

文章摘要：采用徑向基函數神經網絡模型與普通克里格法相結合的方法,預測川中丘陵區(qū)縣域尺度土壤養(yǎng)分（有機質和全氮）的空間分布,并與普通克里格法和回歸克里格法進行比較.結果表明：各方法對研究區(qū)土壤養(yǎng)分的預測結果相似.與多元回歸模型相比,神經網絡模型對驗證樣點土壤有機質和全氮的預測值與樣點實測值的相關系數分別提高了12.3%和16.5%,表明神經網絡模型能更準確地捕捉土壤養(yǎng)分與定量環(huán)境因子間的復雜關系.對469個驗證樣點預測結果的誤差分析表明,神經網絡模型與普通克里格法相結合的方法對土壤有機質和全氮預測結果的平均絕對誤差、平均相對誤差、均方根誤差較普通克里格法分別降低了6.9%、7.4%、5.1%和4.9%、6.1%、4.6%,降低幅度達到極顯著水平（P〈0.01）;與回歸克里格法相比則分別降低了2.4%、2.6%、1.8%和2.1%、2.8%、2.2%,降低幅度達顯著水平（P〈0.05）.

Abstr：In this study,a radial basis function neural network model combined with ordinary kriging（RBFNN_OK） was adopted to predict the spatial distribution of soil nutrients（organic matter and total N） in a typical hilly region of Sichuan Basin,Southwest China,and the performance of this method was compared with that of ordinary kriging（OK） and regression kriging（RK）.All the three methods produced the similar soil nutrient maps.However,as compared with those obtained by multiple linear regression model,the correlation coefficients between the measured values and the predicted values of soil organic matter and total N obtained by neural network model increased by 12.3% and 16.5%,respectively,suggesting that neural network model could more accurately capture the complicated relationships between soil nutrients and quantitative environmental factors.The error analyses of the prediction values of 469 validation points indicated that the mean absolute error（MAE）,mean relative error（MRE）,and root mean squared error（RMSE） of RBFNN_OK were 6.9%,7.4%,and 5.1%（for soil organic matter）,and 4.9%,6.1%,and 4.6%（for soil total N） smaller than those of OK（P0.01）,and 2.4%,2.6%,and 1.8%（for soil organic matter）,and 2.1%,2.8%,and 2.2%（for soil total N） smaller than those of RK,respectively（P0.05）.

文章關鍵詞：

Keyword：:radial basis function neural network model ordinary kriging regression kriging soil nutrient.

課題項目：國家杰出青年科學基金項目（40825003）; 國家自然科學基金項目（41201214,40801175）資助

  本文關鍵詞：基于神經網絡模型和地統(tǒng)計學方法的土壤養(yǎng)分空間分布預測，由筆耕文化傳播整理發(fā)布。