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	Assessing artificial neural networks coupled with wavelet analysis for multi-layer soil moisture dynamics prediction
	Yang Junjun; He Zhibin; Zhao Weijun; Du Jun; Chen Longfei; Zhu Xi
来源期刊	Sciences in Cold and Arid Regions
ISSN	1674-3822
出版年	2016
卷号	8 期号:2 页码:116-124
英文摘要	Soil moisture simulation and prediction in semi-arid regions are important for agricultural production, soil conservation and climate change. However, considerable heterogeneity in the spatial distribution of soil moisture, and poor ability of distributed hydrological models to estimate it, severely impact the use of soil moisture models in research and practical applications. In this study, a newly-developed technique of coupled (WA-ANN) wavelet analysis (WA) and artificial neural network (ANN) was applied for a multi-layer soil moisture simulation in the Pailugou catchment of the Qilian Mountains, Gansu Province, China. Datasets included seven meteorological factors: air and land surface temperatures, relative humidity, global radiation, atmospheric pressure, wind speed, precipitation, and soil water content at 20, 40, 60, 80, 120 and 160 cm. To investigate the effectiveness of WA-ANN, ANN was applied by itself to conduct a comparison. Three main findings of this study were: (1) ANN and WA-ANN provided a statistically reliable and robust prediction of soil moisture in both the root zone and deepest soil layer studied (NSE >0.85, NSE means Nash-Sutcliffe Efficiency coefficient); (2) when input meteorological factors were transformed using maximum signal to noise ratio (SNR) and one-dimensional auto de-noising algorithm (heursure) in WA, the coupling technique improved the performance of ANN especially for soil moisture at 160 cm depth; (3) the results of multi-layer soil moisture prediction indicated that there may be different sources of water at different soil layers, and this can be used as an indicator of the maximum impact depth of meteorological factors on the soil water content at this study site. We conclude that our results show that appropriate simulation methodology can provide optimal simulation with a minimum distortion of the raw-time series; the new method used here is applicable to soil sciences and management applications.
英文关键词	artificial neural network de-noising wavelet analysis time series analysis soil moisture prediction
类型	Article
语种	英语
收录类别	CSCD
WOS研究方向	Agriculture
CSCD记录号	CSCD:5676706
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/335629
作者单位	Yang Junjun, Linze Inland River Basin Research Station, Chinese Ecosystem Research Network, Cold and Arid Regions Environmental and Engineering Research Institute, Key Laboratory of Eco-hydrology of Inland River Basin, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China.; He Zhibin, Linze Inland River Basin Research Station, Chinese Ecosystem Research Network, Cold and Arid Regions Environmental and Engineering Research Institute, Key Laboratory of Eco-hydrology of Inland River Basin, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China.; Du Jun, Linze Inland River Basin Research Station, Chinese Ecosystem Research Network, Cold and Arid Regions Environmental and Engineering Research Institute, Key Laboratory of Eco-hydrology of Inland River Basin, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China.; Chen Longfei, Linze Inland River Basin Research Station, Chinese Ecosystem Research Network, Cold and Arid Regions Environmental and Engineering Research Institute, Key Laboratory of Eco-hyd...
推荐引用方式 GB/T 7714	Yang Junjun,He Zhibin,Zhao Weijun,et al. Assessing artificial neural networks coupled with wavelet analysis for multi-layer soil moisture dynamics prediction[J],2016,8(2):116-124.
APA	Yang Junjun,He Zhibin,Zhao Weijun,Du Jun,Chen Longfei,&Zhu Xi.(2016).Assessing artificial neural networks coupled with wavelet analysis for multi-layer soil moisture dynamics prediction.Sciences in Cold and Arid Regions,8(2),116-124.
MLA	Yang Junjun,et al."Assessing artificial neural networks coupled with wavelet analysis for multi-layer soil moisture dynamics prediction".Sciences in Cold and Arid Regions 8.2(2016):116-124.