Development of Regional-Scale Pedotransfer functions based on Bayesian Neural Networks in the Hetao Irrigation District of China
In order to study determination the soil hydraulic parameters in the distributed hydrological models on farmland environmental effects resulted from watersaving practices of large scale irrigation district, the Bayesian Neural Networks and BP ANN model were applied to establish regional pedotransfer functions models based on the relationship of measured soil characteristic contents (saturated water content 6 s, residual water content 8 r and field water content 9 r) , soil particle percentage, organic matter and bulk density and fitted VG model parameters of different soil texture classes from 22 soil water and salt monitoring points 110 soil samples in the Hetao Irrigation District. Then, the adaptability of two kinds of ANN models were evaluated by simulated and predicted results through the statistical results and SWRC figures. The several conclusions were reached: the ANN and BNN are both feasible PTFs methods. But, the training simulated accuracy of traditional BP model is better than that of BNN; however, the predicted accuracy of BNN model generally is better than the BP model. Furthermore, the number of input factors groups has significantly influenced the predictive accuracy of BP model. But there are little influences on the different inputs factors of BNN model. So, the BNN showed good robustness for the simple inputs. Second, the predicted SWRC has better fitness with measured and VG fitted curve than that of ANN. So, the BNN model is better than the traditional artificial neural network model has better adaptability in the peodotransfer function establishment when it uses only soil particle distribution. The BNN method is a practical method for regional pedotransfer function establishment.
Bayesian neural network Hetao Irrigation District Pedotransfer functions BP neural networks
Zhongyi Qu Xianyue Li Dan Tian Raghavendra B. Jana Binayak P.Monhanty
College of Water Resources and Civil Engineering Inner Mongolia Agricultural University Hohhot, Chin Department of Biological and Agricultural Engineering Texas A&M University College Station, USA
国际会议
2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)
上海
英文
775-780
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）