Water Resources Management

, Volume 25, Issue 11, pp 2823–2836 | Cite as

Soil Water Simulation and Predication Using Stochastic Models Based on LS-SVM for Red Soil Region of China

  • Jianqiang Deng
  • Xiaomin ChenEmail author
  • Zhenjie Du
  • Yong Zhang


The seasonal drought and the low available soil moisture affect the agricultural production in red soil region, China. Therefore, it is necessary to simulate and predict the dynamic changes of soil water in the field. Presently, dynamic model has been applied to obtain the soil water information. While the simulation accuracy of dynamic model depends on many complicated parameters, which are difficult to obtain. In this study, the various nonlinear Stochastic Model of soil water simulation systems and chaotic time series analysis methods of prediction systems had been set up. In the nonlinear Stochastic Model of soil water simulation systems, the daily soil water content simulated by Least squares support vector machine (LS-SVM) with the meteorological factors had more stabilities and advantages in soil water simulation performance over the Back Propagation Artificial Neural Network (BP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). In chaotic time series analysis method of prediction systems, the various signal preprocessing methods including the appropriate de-noising methods and wavelet decomposition methods were applied to preprocess the original chaotic soil water signal. The results of the prediction systems showed that the appropriate de-noising methods and the tendency of wavelet transformation had less effect on the delay time (τ) and embedding dimension (m). The de-noising methods may ignore the detail information of the soil water signal, while the appropriate wavelet transformation to get smaller Maximum Lyapunov Exponent (λ1) of the chaotic soil water signal detail and tendency information can improve the predicting capacity.


Soil water models Chaotic analysis Artificial intelligence De-noising methods Wavelet 


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  1. Asefa T, Kemblowski M, McKee M, Khalil A (2006) Multi-time scale stream flow predictions: the support vector machines approach. J Hydrol 318(1–4):7–16. doi: 10.1016/j.jhydrol.2005.06.001 CrossRefGoogle Scholar
  2. Baghdadi N, Gaultier S, King C (2002) Retrieving surface roughness and soil moisture from SAR data using neural networks. Can J Remote Sens 28(5):701–711CrossRefGoogle Scholar
  3. Behzad M, Asghari K, Eazi M, Palhang M (2009) Generalization performance of support vector machines and neural networks in runoff modeling. Expert Syst Appl 36(4):7624–7629. doi: 10.1016/j.eswa.2008.09.053 CrossRefGoogle Scholar
  4. Ben Mabrouk A, Ben Abdallah N, Dhifaoui Z (2008) Wavelet decomposition and autoregressive model for time series prediction. Appl Math Comput 199(1):334–340. doi: 10.1016/j.amc.2007.09.067 CrossRefGoogle Scholar
  5. Cameira MR, Fernando RM, Pereira LS (2003) Monitoring water and NO3-N in irrigated maize fields in the Sorraia Watershed, Portugal. Agric Water Manage 60(3):199–216. doi: 10.1016/S0378-3774(02)00175-0 CrossRefGoogle Scholar
  6. Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278Google Scholar
  7. Chopart JL, Vauclin M (1990) Water balance estimation model: field test and sensitivity analysis. Soil Sci Soc Am J 54(5):1377–1384CrossRefGoogle Scholar
  8. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297Google Scholar
  9. Davie T (2002) Fundamentals of hydrology, 2nd edn. Routledge, New YorkGoogle Scholar
  10. Dibike YB, Velickov S, Solomatine D, Abbott MB (2001) Model induction with support vector machines: introduction and applications. J Comput Civil Eng 15(3):208–216. doi: 10.1061/(ASCE)0887-3801(2001)15:3(208) CrossRefGoogle Scholar
  11. Donoho DL, Johnstone I (1995) Denoising by soft - thresholding. IEEE Trans Inf Theory 41(3):613–627CrossRefGoogle Scholar
  12. Elshorbagy A, Parasuraman K (2008) On the relevance of using artificial neural networks for estimating soil moisture content. J Hydrol 362(1–2):1–18. doi: 10.1016/j.jhydrol.2008.08.012 CrossRefGoogle Scholar
  13. Entekhabi D, Rodriguez-Iturbe I, Castelli F (1996) Mutual interaction of soil moisture state and atmospheric processes. J Hydrol 184(1–2):3–17. doi: 10.1016/0022-1694(95)02965-6 CrossRefGoogle Scholar
  14. Huang M, Wang KL, Zhang G (2004) Seasonal drought problems in the red soil hilly region of the middle subtropical zone of China. Acta Ecologica Sinica 24(11):2516–2523Google Scholar
  15. Jang JSR (1993) ANFIS: Adaptive network based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRefGoogle Scholar
  16. Kim T-W, Valdes JB (2003) Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. J Hydrol Eng 8(6):319–328. doi: 10.1061/(ASCE)1084-0699(2003)8:6(319) CrossRefGoogle Scholar
  17. Kim HS, Eykholt R, Salas JD (1999) Nonlinear dynamics, delay times, and embedding windows. Phys D Nonlinear Phenom 127(1–2):48–60. doi: 10.1016/S0167-2789(98)00240-1 CrossRefGoogle Scholar
  18. Koster RD, Suarez MJ, Heiser M (2000) Variance and predictability of precipitation at seasonal-to-interannual time scales. J Hydrometeorology 1(1):26–46. doi: 10.1175/1525-7541(2000)001<0026:VAPOPA>2.0.CO;2 CrossRefGoogle Scholar
  19. Koster RD, Dirmeyer PA, Guo Z, Bonan G et al (2004) Regions of strong coupling between soil moisture and precipitation. Science 305(5687):1138–1140. doi: 10.1126/science.1100217 CrossRefGoogle Scholar
  20. Li CL, He YQ (2002) A review on water problems and resolutions of upland region with low - hill red soil. Chin J Soil Sci 33(4):306–309Google Scholar
  21. Liong SY, Sivapragasam C (2002) Flood stage forecasting with support vector machines. J Am Water Resour Assoc 38(1):173–186CrossRefGoogle Scholar
  22. Liu H, Wu W, Wei CF (2003) Study of soil water forecast with neural network. J Soil Water Conserv 17(5):59–62Google Scholar
  23. Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20(2):130–141. doi: 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2 CrossRefGoogle Scholar
  24. Mañé R (1981) On the dimension of the compact invariant sets of certain non-linear maps. Dyn Syst Turbulence Warwick 1980:230–242CrossRefGoogle Scholar
  25. National Soil Survey (1998) China soil. China Agriculture Press, BeijingGoogle Scholar
  26. Neitsch SL, Arnold JG, Kiniry JR, Williams JR, King KW (2002) Soil and water assessment tool theoretical documentation, version 2000. Grassland, Soil and Water Research Laboratory, Agricultural Research Service, TempleGoogle Scholar
  27. Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2005) Soil and water assessment tool (SWAT), theoretical documentation. Blackland Research Center, Grassland, Soil and Water Research Laboratory, Agricultural Research Service, TempleGoogle Scholar
  28. Neuman SP (1972) Finite element computer programs for flow in saturated-unsaturated porous media. 2nd Annu Rep Project A10-SWC-77, Hydraulic Eng Lab, Technion, Haifa, IsraelGoogle Scholar
  29. Nimah MN, Hanks RJ (1973) Model for estimating soil water, plant, and atmospheric interrelations: I. Description and sensitivity. Soil Sci Soc Am J 37(4):522–527CrossRefGoogle Scholar
  30. Nishat S, Guo Y, Baetz BW (2007) Development of a simplified continuous simulation model for investigating long-term soil moisture fluctuations. Agric Water Manage 92(1–2):53–63. doi: 10.1016/j.agwat.2007.04.012 CrossRefGoogle Scholar
  31. Panigrahi B, Panda SN (2003) Field test of a soil water balance simulation model. Agric Water Manage 58(3):223–240. doi: 10.1016/S0378-3774(02)00082-3 CrossRefGoogle Scholar
  32. Porporato A, Rodriguez-Iturbe I (2002) Ecohydrology - a challenging multidisciplinary research perspective. Hydrol Sci J 47(5):811–821CrossRefGoogle Scholar
  33. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation, parallel distributed processing, vol 1: foundations. MIT, Cambridge, pp 318–362Google Scholar
  34. Sheila RM, John A, Derek A (2001) Optimal wavelet denoising for phonocardiograms. Microelectron J 32(12):931–941. doi: 10.1016/S0026-2692(01)00095-7 CrossRefGoogle Scholar
  35. Šimùnek J, van Genuchten MT (2008) Modeling nonequilibrium flow and transport processes using HYDRUS. Vadose Zone J 7(2):782–797. doi: 10.2136/vzj2007.0074 CrossRefGoogle Scholar
  36. Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300. doi: 10.1023/A:1018628609742 CrossRefGoogle Scholar
  37. Takens F (1981) Detecting strange attractors in turbulence. Dyn Syst Turbulence Warwick 1980:366–381CrossRefGoogle Scholar
  38. Wolf A, Swift JB, Swinney HL, Vastano JA (1985) Determining Lyapunov exponents from a time series. Physica D, Nonlinear Phenom 16(3):285–317. doi: 10.1016/0167-2789(85)90011-9 CrossRefGoogle Scholar
  39. Wu J, Lu J, Wang J (2009) Application of chaos and fractal models to water quality time series prediction. Environ Model Softw 24(5):632–636. doi: 10.1016/j.envsoft.2008.10.004 CrossRefGoogle Scholar
  40. Yang HH, Murata N, Amari S (1998) Statistical inference: learning in artificial neural networks. Trends Cogn Sci 2(1):4–10. doi: 10.1016/S1364-6613(97)01114-5 CrossRefGoogle Scholar
  41. Zhang MK, Xu JM (2005) Restoration of surface soil fertility of an eroded red soil in southern China. Soil Tillage Res 80(1–2):13–21. doi: 10.1016/j.still.2004.02.019 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Jianqiang Deng
    • 1
    • 2
  • Xiaomin Chen
    • 1
    Email author
  • Zhenjie Du
    • 1
  • Yong Zhang
    • 1
  1. 1.College of Resources and Environmental SciencesNanjing Agricultural UniversityNanjingChina
  2. 2.Enshi Tobacco Company of Hubei ProvinceEnshiChina

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