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

• ARTICLES • 上一篇    

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
  • 收稿日期:2015-06-24 修回日期:2015-09-02 发布日期:2018-11-23
  • 通讯作者: ZhiBin He E-mail:hzbmail@lzb.ac.cn
  • 基金资助:
    This work was supported by funding from the Major Research Plan of National Natural Science Foundation of China(Grant No.91225302).

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).

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.

关键词: artificial neural network, de-noising, wavelet analysis, time series analysis, soil moisture prediction

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

Al-Hamdan OZ, Cruise JF, 2010. Soil moisture profile development from surface observations by principle of maximum entropy. Journal of Hydrologic Engineering, 15(5):327-337. DOI:10.1061/(asce)he.1943-5584.0000196.
Brown ME, Lary DJ, Vrieling A, et al., 2008. Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS. International Journal of Remote Sensing, 29(24):7141-7158. DOI:10.1080/01431160802238435.
Chen M, Willgoose GR, Saco PM, 2014. Spatial prediction of temporal soil moisture dynamics using HYDRUS-1D.Hydrological Processes, 28(2):171-185. DOI:10.1002/hyp.9518.
Coifman RR, Wickerhauser MV, 1992. Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory, 38(2):713-718. DOI:10.1109/18.119732.
Deng JQ, Chen XM, Du ZJ, et al., 2011. Soil water simulation and predication using stochastic models based on LS-SVM for red soil region of China. Water Resources Management, 25(11):2823-2836. DOI:10.1007/s11269-011-9840-z.
Dreyfus G, 2005. Neural Networks Methodology and applications.ESPCI, Laboratoire d'Electronique 10 rue Vauquelin 75005 Paris, France.
Dumedah G, Walker JP, Chik L, 2014. Assessing artificial neural networks and statistical methods for infilling missing soil moisture records. Journal of Hydrology, 515:330-344. DOI:10.1016/j.jhydrol.2014.04.068.
Elshorbagy A, Parasuraman K, 2008. On the relevance of using artificial neural networks for estimating soil moisture content.Journal of Hydrology, 362(1-2):1-18. DOI:10.1016/j.jhydrol.2008.08.012.
Haykin S, 1999. Neural networks:A Comprehensive Foundation(2nd Edition). Sai PrintoPack Pvt. Ltd., Pearson Education(Singapore) Pte. Ltd., Indian Branch, 482 F. I. E. Patparganj, Delhi 110092, India.
He ZB, Wen XH, Liu H, et al., 2014. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology, 509:379-386. DOI:10.1016/j.jhydrol.2013.11.054.
He ZB, Zhao WZ, Liu H, et al., 2012. The response of soil moisture to rainfall event size in subalpine grassland and meadows in a semi-arid mountain range:A case study in northwestern China's Qilian Mountains. Journal of Hydrology, 420:183-190. DOI:10.1016/j.j.hydrol.2011.11.056.
Karul C, Soyupak S, Cilesiz AF, et al., 2000. Case studies on the use of neural networks in eutrophication modeling. Ecological Modelling, 134(2-3):145-152. DOI:10.1016/s0304-3800(00)00360-4.
Klemas V, Finkl CW, Kabbara N, 2014. Remote sensing of soil moisture:An overview in relation to coastal soils. Journal of Coastal Research, 30(4):685-696. DOI:10.2112/jcoastres-d-13-00072.1.
Kokaly RF, Clark RN, 1999. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67(3):267-287. DOI:10.1016/s0034-4257(98)00084-4.
Latt ZZ, Wittenberg H, 2014. Improving flood forecasting in a developing country:A comparative study of stepwise multiple linear regression and artificial neural network. Water Resources Management, 28(8):2109-2128. DOI:10.1007/s11269-014-0600-8.
Legates DR, McCabe GJ, 1999. Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1):233-241.DOI:10.1029/1998wr900018.
Messer SR, Agzarian J, Abbott D, 2001. Optimal wavelet denoising for phonocardiograms. Microelectronics Journal, 32(12):931-941. DOI:10.1016/s0026-2692(01)00095-7.
Moriasi DN, Arnold JG, Van Liew MW, et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3):885-900.
Qin J, Liang SL, Yang K, et al., 2009. Simultaneous estimation of both soil moisture and model parameters using particle filtering method through the assimilation of microwave signal. Journal of Geophysical Research-Atmospheres, 114:D15103. DOI:10.1029/2008jd011358.
Rumelhart DE, Hinton GE, Williams RJ, 1986. Learning representations by back-propagating errors. Nature, 323(6088):533-536.
Sang YF, 2012. A practical guide to discrete wavelet decomposition of hydrologic time series. Water Resources Management, 26(11):3345-3365. DOI:10.1007/s11269-012-0075-4.
Sang YF, Wang D, 2008. Wavelets selection method in hydrologic series wavelet analysis. Journal of Hydraulic Engineering, 39(3):295-300, 306.
Sang YF, Wang D, Wu JC, et al., 2009. Entropy-based wavelet de-noising method for time series analysis. Entropy, 11(4):1123-1147. DOI:10.3390/e11041123.
Tiwari MK, Song KY, Chatterjee C, et al., 2013. Improving reliability of river flow forecasting using neural networks, wavelets and self-organising maps. Journal of Hydroinformatics, 15(2):486-502. DOI:10.2166/hydro.2012.130.
Wofsy SC, Goulden ML, Munger JW, et al., 1993. Net exchange of CO2 in a midlatitude forest. Science, 260(5112):1314-1317.DOI:10.1126/science.260.5112.1314.
Wu SH, Jansson PE, 2013. Modelling soil temperature and moisture and corresponding seasonality of photosynthesis and transpiration in a boreal spruce ecosystem. Hydrology and Earth System Sciences, 17(2):735-749. DOI:10.5194/hess-17-735-2013.
Yu ZB, Liu D, Lu HS, et al., 2012. A multi-layer soil moisture data assimilation using support vector machines and ensemble particle filter. Journal of Hydrology, 475:53-64. DOI:10.1016/j.jhydrol.2012.08.034.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!