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Sciences in Cold and Arid Regions ›› 2016, Vol. 8 ›› Issue (2): 116-124.doi: 10.3724/SP.J.1226.2016.00116

• ARTICLES • Previous Articles    

Assessing artificial neural networks coupled with wavelet analysis for multi-layer soil moisture dynamics prediction

JunJun Yang1, ZhiBin He1, WeiJun Zhao2,3, Jun Du1, LongFei Chen1, Xi Zhu1   

  1. 1. Linze Inland River Basin Research Station, Chinese Ecosystem Research Network, Key Laboratory of Eco-hydrology of Inland River Basin, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;
    2. Academy of Water Resources Conservation Forests in Qilian Mountains of Gansu Province, Zhangye, Gansu 734000, China;
    3. College of Forestry, Gansu Agricultural University, Lanzhou, Gansu 730070, China
  • Received:2015-06-24 Revised:2015-09-02 Published:2018-11-23
  • Contact: ZhiBin He E-mail:hzbmail@lzb.ac.cn
  • Supported by:
    This work was supported by funding from the Major Research Plan of National Natural Science Foundation of China(Grant No.91225302).

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Abstract: 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.

Key words: artificial neural network, de-noising, wavelet analysis, time series analysis, soil moisture prediction

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