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Modelling the root zone soil moisture using artificial neural networks, a case study

  • Mustafa Al-Mukhtar
Original Article
  • 190 Downloads

Abstract

Surface soil moisture constitutes a major component in the Earth’s water cycle. In many cases, modelling and predicting soil moisture represent a serious problem in water resources field due to the problematic measurements or lack of measurements, etc. Data-driven models such as artificial neural networks (ANN) have been characterized as a robust tool to overcome these shortcomings. This study aims to identify the optimum ANNs to model the root zone soil moisture (up to 2 m depth) in the upper reach of the Spree River using the synthetic soil moisture data from SWAT model. Thus, three different approaches were developed and compared to determine the highest performing method. These networks can be broadly categorized into dynamic, static, and statistical neural networks, which are layer recurrent network (LRN), feedforward (FF), and radial basis networks, respectively. Data sets of precipitation and antecedent soil moisture were selected based on quantification of cross-, auto-, and partial autocorrelation coefficients to represent the best behaviour of root soil moisture. The time series data were subdivided into two subsets: one for network training and the second for network testing. The determination coefficient (R 2), root-mean-square error, and Nash–Sutcliffe efficiency were employed to test the goodness of fit between the actual and modelled data. Results show that, among the used methods, the LRN and FF networks have the top performance criteria, showing a reliable ability to be used as estimator for the soil moisture in this catchment.

Keywords

Soil moisture Artificial neural networks Comparison Temporal variation SWAT model Spree River 

Notes

Acknowledgments

This work was supported by funding from the Ministry of Higher Education and Scientific Research in Iraq (MOHESR) and the German Academic Exchange Service (DAAD).

References

  1. Al-Mukhtar M, Volkmar D, Merkel B (2013) Evaluation of the climate generator model CLIGEN for rainfall data simulation in Bautzen catchment area, Germany. Hydrol Res. doi: 10.2166/nh.2013.073 Google Scholar
  2. Al-Mukhtar M, Dunger V, Merkel B (2014) Assessing the impacts of climate change on hydrology of the upper reach of the spree river: Germany. Water Resour Manag 28(10):2731–2749. doi: 10.1007/s11269-014-0675-2 CrossRefGoogle Scholar
  3. Arnold J, Srinivasan R (1998) Large area hydrologic modeling and assessment part I: model development1. JAWRA 34(1):73–89Google Scholar
  4. ASCE Task Committee on Application of Artificial Neural Networks in hydrology (2000) Artificial neural networks in hydrology. II: hydrologic applications. J Hydrol Eng 5(2):124–137CrossRefGoogle Scholar
  5. Beale MH, Hagen MT, Demuth HB (2012) Neural network toolbox: users guide. The MathWorks, IncGoogle Scholar
  6. Boden AG (2005) Bodenkundliche Kartieranleitung, 5. Auflage. Herausgeber: Bundesanstalt für Geowissenschaften und Rohstoffe, HannoverGoogle Scholar
  7. Chang F-J, Chen P-A, Lu Y-R, Huang E, Chang K-Y (2014) Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control. J Hydrol 517:836–846. doi: 10.1016/j.jhydrol.2014.06.013 CrossRefGoogle Scholar
  8. Collins DBG, Bras RL (2008) Climatic control of sediment yield in dry lands following climate and land cover change. Water Resour Res 44(10):357–367CrossRefGoogle Scholar
  9. Coppola E Jr, Poulton M, Charles E, Dustman J, Szidarovszky F (2003) Application of artificial neural networks to complex groundwater management problems. Nat Resour Res 12(4):303–320CrossRefGoogle Scholar
  10. Coulibaly P, Anctil F, Aravena R, Bobée B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885–896CrossRefGoogle Scholar
  11. Crow WT et al (2012) Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Rev Geophys 50:RG2002. doi: 10.1029/2011RG000372 CrossRefGoogle Scholar
  12. Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25(1):80–108. doi: 10.1177/030913330102500104 CrossRefGoogle Scholar
  13. DeBarry PA (2004) Watersheds: processes, assessment, and management. Wiley Hoboken, NJGoogle Scholar
  14. Dolling OR, Varas EA (2002) Artificial neural networks for streamflow prediction. J Hydraul Res 40(5):547–554CrossRefGoogle Scholar
  15. Dumedah G, Coulibaly P (2011) Evaluation of statistical methods for infilling missing values in high-resolution soil moisture data. J Hydrol 400(1–2):95–102. doi: 10.1016/j.jhydrol.2011.01.028 CrossRefGoogle Scholar
  16. Dumedah G, Walker JP, Chik L (2014) Assessing artificial neural networks and statistical methods for infilling missing soil moisture records. J Hydrol 515:330–344. doi: 10.1016/j.jhydrol.2014.04.068 CrossRefGoogle Scholar
  17. Elman JL (1990) Finding structure in time* 1. Cogn Sci 14(2):179–211. doi: 10.1207/s15516709cog1402_1 CrossRefGoogle Scholar
  18. Eltahir EAB (1998) A soil moisture–rainfall feedback mechanism: 1. Theory and observations. Water Resour Res 34(4):765–776CrossRefGoogle Scholar
  19. Entekhabi D, Rodriguez-Iturbe I (1994) Analytical framework for the characterization of the space-time variability of soil moisture. Adv Water Resour 17:35–45. doi: 10.1016/0309-1708(94)90022-1 CrossRefGoogle Scholar
  20. Gao X, Wu P, Zhao X, Shi Y, Wang J, Zhang B (2011) Soil moisture variability along transects over a well-developed gully in the Loess Plateau, China. Catena 87(3):357–367. doi: 10.1016/j.catena.2011.07.004 CrossRefGoogle Scholar
  21. Gao X, Wu P, Zhao X, Wang J, Shi Y, Zhang B, Tian L, Li H (2013) Estimation of spatial soil moisture averages in a large gully of the Loess Plateau of China through statistical and modeling solutions. J Hydrol 486:466–478. doi: 10.1016/j.jhydrol.2013.02.026 CrossRefGoogle Scholar
  22. Haan CT (2002) Statistical methods in hydrology, 2nd edn. The Iowa State University Press, IowaGoogle Scholar
  23. Jackson TJ (1997) Soil moisture estimation using special satellite microwave/imager satellite data over a grassland region. Water Resour Res 33(6):1475–1484CrossRefGoogle Scholar
  24. Jha M, Pan Z, Takle ES, Gu R (2004) Impacts of climate change on streamflow in the upper Mississippi river basin: a regional climate model perspective. J Geophys Res 109(D9):D09105. doi: 10.1029/2003JD003686 CrossRefGoogle Scholar
  25. Kisi Ö (2004) River flow modeling using artificial neural networks. J Hydrol Eng 9(1):60–63CrossRefGoogle Scholar
  26. Kişi Ö (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539CrossRefGoogle Scholar
  27. Kohonen T (2012) Self-organization and associative memory, vol 8. Springer, BerlinGoogle Scholar
  28. Langevin CD, Panday S (2012) Future of groundwater modeling. Groundwater 50(3):334–339CrossRefGoogle Scholar
  29. Luk KC, Ball JE, Sharma A (2001) An application of artificial neural networks for rainfall forecasting. Math Comput Model 33:683–693. doi: 10.1016/S0895-7177(00)00272-7 CrossRefGoogle Scholar
  30. Machiwal D, Jha MK (2008) Comparative evaluation of statistical tests for time series analysis: application to hydrological time series/evaluation comparative de tests statistiques pour l’analyse de s{é}ries temporelles: application à des s{é}ries temporelles hydrologiques. Hydrol Sci J 53(2):353–366CrossRefGoogle Scholar
  31. Machiwal D, Jha MK (2012) Hydrologic time series analysis: theory and practice. Springer, BerlinCrossRefGoogle Scholar
  32. Maier HR, Dandy GC (1996) The use of artificial neural networks for the prediction of water quality parameters. Water Resour Res 32(4):1013–1022CrossRefGoogle Scholar
  33. Minns AW, Hall MJ (1996) Artificial neural networks as rainfall-runoff models. Hydrol Sci J 41(3):399–417CrossRefGoogle Scholar
  34. Moriasi DN, Arnold JG, Van Liew MV, Binger RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Am Soc Agric Biol Eng 50(3):885–900Google Scholar
  35. Mutlu E, Chaubey I, Hexmoor H, Bajwa SG (2008) Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrol Process. doi: 10.1002/hyp.7136 Google Scholar
  36. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part1-A discussion of principles. J Hydrol 10(3):282–290CrossRefGoogle Scholar
  37. Njoku EG, Entekhabi D (1996) Passive microwave remote sensing of soil moisture. J Hydrol 184:101–129. doi: 10.1016/0022-1694(95)02970-2 CrossRefGoogle Scholar
  38. Orchard VA, Cook FJ (1983) Relationship between soil respiration and soil moisture. Soil Biol Biochem 15(4):447–453. doi: 10.1016/0038-0717(83)90010-X CrossRefGoogle Scholar
  39. Pan F, Peters-Lidard CD, Sale MJ (2003) An analytical method for predicting surface soil moisture from rainfall observations. Water Resour Res 39(11), Article No. 1314. doi: 10.1029/2003WR002142
  40. Rajurkar MP, Kothyari UC, Chaube UC (2004) Modeling of the daily rainfall-runoff relationship with artificial neural network. J Hydrol 285:96–113. doi: 10.1016/j.jhydrol.2003.08.011 CrossRefGoogle Scholar
  41. Rodriguez-Iturbe I, D’odorico P, Porporato A, Ridolfi L (1999) On the spatial and temporal links between vegetation, climate, and soil moisture. Water Resour Res 35(12):3709–3722CrossRefGoogle Scholar
  42. Tokar AS, Johnson PA (1999) Rainfall-runoff modeling using artificial neural networks. J Hydrol Eng 4(3):232–239CrossRefGoogle Scholar
  43. Wetzel PJ, Atlas D, Woodward RH (1984) Determining soil moisture from geosynchronous satellite infrared data: a feasibility study. J Clim Appl Meteorol 23(3):375–391CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Building and Construction Engineering DepartmentUniversity of TechnologyBaghdadIraq

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