NDLI: Development of regional-scale pedotransfer functions based on Bayesian Neural Networks in the Hetao Irrigation District of China

Content Provider	IEEE Xplore Digital Library
Author	Zhongyi Qu Xianyue Li Dan Tian Jana, R.B. Monhanty, B.P.
Copyright Year	2011
Description	Author affiliation: College of Water Resources and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, China (Zhongyi Qu; Xianyue Li; Dan Tian) \|\| Department of Biological and Agricultural Engineering, Texas A&M University, College Station, USA (Jana, R.B.; Monhanty, B.P.)
Abstract	In order to study determination the soil hydraulic parameters in the distributed hydrological models on farmland environmental effects resulted from water-saving 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 θs, residual water content θr and field water content θr), 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.
Starting Page	756
Ending Page	761
File Size	426442
Page Count	6
File Format	PDF
ISBN	9781424499502
ISSN	21579563
e-ISBN	9781424499533
DOI	10.1109/ICNC.2011.6022191
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Irrigation Soil measurements Bayesian methods Hetao Irrigation District Artificial neural networks Soil Predictive models Markov processes BP neural networks Bayesian neural network Pedotransfer functions
Content Type	Text
Resource Type	Article

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