气候变化领域集成服务门户(CCPortal): Modeling soil water content in extreme arid area using an adaptive neuro-fuzzy inference system

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DOI	10.1016/j.jhydrol.2015.05.034
	Modeling soil water content in extreme arid area using an adaptive neuro-fuzzy inference system
	Si, Jianhua; Feng, Qi; Wen, Xiaohu; Xi, Haiyang; Yu, Tengfei; Li, Wei; Zhao, Chunyan
发表日期	2015
ISSN	0022-1694
EISSN	1879-2707
卷号	527
英文摘要	Modeling of soil water content (SWC) is one of the most studied topics in hydrology due to its essential application to water resources management. In this study, an adaptive neuro fuzzy inference system (ANFIS) method is used to simulate SWC in the extreme arid area. In-situ SWC datasets for soil layers, with depths of 40 cm (layer 1), 60 cm (layer 2) below surface was taken for the present study. The models analyzed different combinations of antecedent SWC values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANFIS models in training and validation sets are compared with the observed data. In layer 1, the model which consists of six antecedent values of SWC, has been selected as the best fit model for SWC modeling. On the other hand, which includes two antecedent values of SWC, has been selected as the best fit model for SWC modeling at layer 2. In order to assess the ability of ANFIS model relative to that of the ANN model, the best fit of ANFIS model of layer 1 and layer 2 structures are also tested by two artificial neural networks (ANN), namely, Levenberg-Marquardt feedforward neural network (ANN-1) and Bayesian regularization feedforward neural network (ANN-2). The comparison was made according to the various statistical measures. A detailed comparison of the overall performance indicated that the ANFIS model performed better than both the ANN-1 and ANN-2 in SWC modeling for the validation data sets in this study. (C) 2015 Elsevier B.V. All rights reserved.
关键词	Adaptive neuro fuzzy inference system Neural networks Soil water content Modeling Ejina basin
学科领域	Engineering; Geology; Water Resources
语种	英语
WOS研究方向	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
来源期刊	JOURNAL OF HYDROLOGY
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/111804
作者单位	Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Si, Jianhua,Feng, Qi,Wen, Xiaohu,et al. Modeling soil water content in extreme arid area using an adaptive neuro-fuzzy inference system[J]. 中国科学院西北生态环境资源研究院,2015,527.
APA	Si, Jianhua.,Feng, Qi.,Wen, Xiaohu.,Xi, Haiyang.,Yu, Tengfei.,...&Zhao, Chunyan.(2015).Modeling soil water content in extreme arid area using an adaptive neuro-fuzzy inference system.JOURNAL OF HYDROLOGY,527.
MLA	Si, Jianhua,et al."Modeling soil water content in extreme arid area using an adaptive neuro-fuzzy inference system".JOURNAL OF HYDROLOGY 527(2015).