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

Zhongyi Qu, Xianyue Li, Dan Tian, Raghavendra B. Jana, Binayak P. Monhanty

Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

2 Цитирования (Scopus)

Аннотация

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 s, residual water content r and field water content 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.

Язык оригиналаАнглийский
Название основной публикацииProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Страницы756-761
Число страниц6
DOI
СостояниеОпубликовано - 2011
Опубликовано для внешнего пользованияДа
Событие2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, Китай
Продолжительность: 26 июл. 201128 июл. 2011

Серия публикаций

НазваниеProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Том2

Конференция

Конференция2011 7th International Conference on Natural Computation, ICNC 2011
Страна/TерриторияКитай
ГородShanghai
Период26/07/1128/07/11

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