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Machine Learning and Artificial Intelligence-Based Approaches

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Hydrological Data Driven Modelling

Part of the book series: Earth Systems Data and Models ((ESDM,volume 1))

Abstract

The skill of Artificial Intelligence (AI)-based computational mechanisms to model important nonlinear hydrological processes is addressed in this chapter. Three major themes are illustrated: (1) conventional data-based nonlinear concepts such as Box and Jenkins Models, ARX, ARIMAX, and intelligent computing tools such as LLR, ANN, ANFIS , and SVMs ; (2) the discrete wavelet transform (DWT), a powerful signal processing tool and its application in hydrology , and (3) conjunction models of DWT, namely neuro-wavelet models, Wavelet-ANFIS models, and Wavelet-SVM s. This chapter gives a detailed description of the training algorithms used in this book and points out the conceptual advantages of Levenberg–Marquardt (LM) algorithms over Broyden-Fletcher-Goldfarb-Shanno (BFGS) training algorithms and Conjugate Gradient (CG) training algorithms.

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References

  1. Aksoy H, Guven A, Aytek A, Yuce MI, Unal NE et al (2007) Discussion of generalized regression neural networks for evapotranspiration modelling by O. Kişi (2006). Hydrol Sci J 52(4):825–828

    Article  Google Scholar 

  2. Aksoy H, Guven A, Aytek A, Yuce MI, Unal NE et al (2008) Comment on Kisi O (2007) Evapotranspiration modelling from climatic data using a neural computing technique. Hydrol Process 21(14):1925–1934. Hydrol Process 22(14):2715–2717

    Article  Google Scholar 

  3. Anctil F, Tape TG (2004) An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition. J Env Eng Sci 3:S121–S128

    Article  Google Scholar 

  4. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000a) Artificial neural networks in hydrology—I: preliminary concepts. J Hydraul Eng ASCE 5(2):115–123

    Google Scholar 

  5. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000b) Artificial neural networks in hydrology—II: hydrologic applications. J Hydraul Eng ASCE 5(2):124–137

    Google Scholar 

  6. Bartolini P, Salas JD, Obeysekera JTB (1988) Multivariate periodic ARMA(1,1) processes. Water Resour Res 24(8):1237–1246

    Google Scholar 

  7. Bayazit M, Akso H (2001) Using wavelets for data generation. J Appl Stat 28(2):157–166

    Article  Google Scholar 

  8. Bayazit M, Onoz B, Aksoy H et al (2001) Nonparametric streamflow simulation by wavelet or Fourier analysis. Hydrol Sci J 46(4):623–634

    Article  Google Scholar 

  9. Ben-Hur A, Weston J (2012) A user’s guide to support vector machines. Technical Report. http://pyml.sourceforge.net/doc/howto.pdf

  10. Bishop CM (1996) Neural networks for pattern recognition. Oxford University Press. ISBN 0-19-853864-2

    Google Scholar 

  11. Boser BE, Guyon I, Vapnik V (1992) A training algorithm for optimal margin classifiers. In: Proceedings fifth ACM workshop on computational learning theory, pp 144–152

    Google Scholar 

  12. Box GE, Jenkins G (1970) Time series analysis, forecasting and control. Revised edition. Holden-Day, San Francisco (2nd edn 1976)

    Google Scholar 

  13. Box GEP, Jenkins GM (1976) Time series analysis: Forecasting and control. Holden-Day, San Francisco

    Google Scholar 

  14. Bray M, Han D (2004) Identification of support vector machines for runoff modelling. J Hydroinf 6(4):265–280

    Google Scholar 

  15. Chanerley AA, Alexander NA (2007) Correcting data from an unknown accelerometer using recursive least squares and wavelet de-noising. Comput Struct 85:1679–1692

    Article  Google Scholar 

  16. Chang TJ, Delleur JW, Kavvas ML (1987) Application of discrete autoregressive moving average models for estimation of daily runoff. J Hydrol 91:119–135

    Article  Google Scholar 

  17. Chen S-T, Yu P-S (2007) Pruning of support vector networks on flood forecasting. J Hydrol 347(1–2):67–78

    Article  Google Scholar 

  18. Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3–4):244–257

    Article  Google Scholar 

  19. Daubechies I (1988) Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math 41:909–996

    Article  Google Scholar 

  20. Daubechies I (1992) Ten lectures on wavelets. SIAM, ch, Philadelphia, PA, pp 3–5

    Book  Google Scholar 

  21. Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25(1):80–108

    Article  Google Scholar 

  22. Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V et al (1997) Support vector regression machines. In: Mozer M, Jordan M, Petsche T (eds) Advances in neural information processing dsystems vol 9. MIT Press, Cambridge, pp 155–161

    Google Scholar 

  23. Durrant PJ (2001) winGamma: a non-linear data analysis and modelling tool with applications to flood prediction. Ph.D. thesis, Department of Computer Science, Cardiff University, Wales, UK

    Google Scholar 

  24. Elman J (1990) Finding structure in time. Cognitive Science 14(2):179–211

    Article  Google Scholar 

  25. Fletcher R (1987) Practical methods of optimization, 2nd edn. Wiley, New York

    Google Scholar 

  26. Ganguli P, Reddy MJ (2013) Ensemble prediction of regional droughts using climate inputs and the SVM—copula approach. hydrological processes. doi:10.1002/hyp.9966

  27. Gautam D (2000) Neural network based system identification approach for the modelling of water resources and environmental systems. 2nd Joint Workshop on AI Methods in Civil Engineering Applications Cottbus/Germany. http://www.bauinf.tu-cottbus.de/Events/Neural00/Participants.html. Accessed March 26–28

  28. Goh T, Tan K (1977) Stochastic modelling and forecasting of solar radiation data. Sol Energy 19(6):755–757. doi:10.1016/0038-092X(77)90041-X

    Article  Google Scholar 

  29. Han D, Chan L, Zhu N et al (2007) Flood forecasting using support vector machines. J Hydroinf 9(4):267–276

    Article  Google Scholar 

  30. Han H, Felker P (1997) Estimation of daily soil water evaporation using an artificial neural network. J Arid Environ 37:251–260

    Article  Google Scholar 

  31. Han D, Kwong T, Li S et al (2007) Uncertainties in real-time flood forecasting with neural networks. Hydrol Process 21:223–228

    Article  Google Scholar 

  32. Han D, Cluckie ID, Karbassioun D, Lawry J, Krauskopf B et al. (2002) River flow modelling using fuzzy decision trees, Water Resour Manage 16(6):431–445

    Google Scholar 

  33. Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley, Reading, MA

    Google Scholar 

  34. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  35. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554–2558

    Article  Google Scholar 

  36. Horikawa S, Furuhashi T, Uchikawa Y et al (1992) On fuzzy modeling using fuzzy networks with back-propagation algorithm. IEEE Trans Neural Networks 3(5):801–806

    Article  Google Scholar 

  37. Hossein T, Ozgur K, Azadeh E, Hosseinzadeh PTA et al (2012) SVM, ANFIS, regression and climate based models for reference evapotranspiration modelling using limited climatic data in a semi-arid highland environment. J Hydrol 444–445:78–89

    Google Scholar 

  38. Jain A, Sudheer KP, Srinivasulu S (2004) Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrol Process 18:571–581

    Article  Google Scholar 

  39. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Sys Manage Cybernetics 23(3):665–685

    Article  Google Scholar 

  40. Jian YL, Chun TC, Ch Kwok W et al (2006) Using support vector machines for long term discharge prediction. Hydrol Sci J 51(4):599–612. doi:10.1623/hysj.51.4.599

  41. Jones AJ (2004) New tools in non-linear modelling and prediction. Comput Manage Sci 1:109–149. doi:10.1007/s10287-003-0006-1

    Article  Google Scholar 

  42. Jonsdottir H, Aa Nielsen H, Madsen H, Eliasson J, Palsson OP, Nielsen MK et al. (2007) Conditional parametric models for storm sewer runoff. Water Res Res 43:1–9

    Google Scholar 

  43. Jordan MI (1986) Serial order: A parallel distributed processing approach. Institute for Cognitive Science Report 8604. UC, San Diego

    Google Scholar 

  44. Karamouz M, Razavi S, Araghinejad S et al (2008) Long-lead seasonal rainfall forecasting using time-delay recurrent neural networks: a case study. Hydrol Process 22:229–241

    Article  Google Scholar 

  45. Keskin ME, Terzi O, Taylan D (2004) Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrol Sci J 49(6):1001–1010

    Article  Google Scholar 

  46. Keskin ME, TerziO (2006) Artificial neural network models of daily pan evaporation. J Hydrol Eng ASCE 11:65–70. doi:10.1061/(ASCE)1084-0699(2006)11:1(65)

  47. Kishor N, Singh SP (2007) Nonlinear predictive control for a NNARX hydro plant model. Neural Comput Appli 16(2):101–108

    Article  Google Scholar 

  48. Kisi O (2006) Evapotranspiration estimation using feed-forward neural networks. Nord Hydrol 37(3):247–260

    Article  Google Scholar 

  49. Kisi O (2008) Stream flow forecasting using neuro-wavelet technique. Hydrol Process 22:4142–4152

    Article  Google Scholar 

  50. Kosko B (1992) Fuzzy systems as universal approximators. In: Proceedings of the IEEE international conference on fuzzy systems, pp 1153–1162

    Google Scholar 

  51. Koutsoyiannis D (2007) Discussion of “Generalized regression neural networks for evapotranspiration modelling” by O. Kişi (2006). Hydrol Sci J 52(4):832–835

    Google Scholar 

  52. Kumar M, Raghuwanshi NS, Singh R, Wallender W, Pruitt WO et al (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128(4):224–233

    Article  Google Scholar 

  53. Labat D (2005) Recent advances in wavelet analyses: part 1 a review of concepts. J Hydrol 314:275–288

    Article  Google Scholar 

  54. Lafreni`ere M, Sharp M (2003) Wavelet analysis of inter-annual variability in the runoff regimes of glacial and nival stream catchments, Bow Lake, Alberta. Hydrol Process 17:1093–1118

    Google Scholar 

  55. Lane SN (2007) Assessment of rainfall-runoff models based upon wavelet analysis. Hydrol Process 21:586–607

    Article  Google Scholar 

  56. Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15:101–124

    Article  Google Scholar 

  57. Maren AJ, Harston CT, Pap RM (1990) Handbook of neural computing applications. Academic Press, San Diego, CA

    Google Scholar 

  58. McCulloch WS, Pitts W (1943) A logical calculus of the ideas imminent in nervous activity. Bull Math Biophys 5:115–133

    Article  Google Scholar 

  59. Mechaqrane A, Zouak M (2004) A comparison of linear and neural network ARX models applied to a prediction of the indoor temperature of a building. Neural Comput Applic 13:32–37

    Article  Google Scholar 

  60. Minsky M, Papert S (1969) Perceptrons. MIT Press, Oxford

    Google Scholar 

  61. Moghaddamnia A, Ghafari M, Piri JD et al (2008) Evaporation estimation using support vector machines technique. World Acad Sci Eng Technol 43:14–22

    Google Scholar 

  62. Mohsen B, Keyvan A, Coppola EA Jr (2010) Comparative study of SVMs and ANNs in aquifer water level prediction. J Comput Civil Eng 24(5). http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000043.h

  63. Morlet JM, Grossman A (1984) Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 15(4):723–736

    Article  Google Scholar 

  64. Moss ME, Bryson MC (1974) Autocorrelation structure of monthly streamflows. Water Resour Res 10(4):737–744

    Article  Google Scholar 

  65. Mustacchi C, Cena V, Rocchi M (1979) Stochastic simulation of hourly global radiation sequences. Sol Energy 23(1):47–51. doi:10.1016/0038-092X(79)90042-2

    Article  Google Scholar 

  66. Nayak PC, Sudheer KP, Jain SK et al (2007) Rainfall-runoff modeling through hybrid intelligent system. Water Res 43:1–17

    Google Scholar 

  67. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modelling hydrological time series. J Hydrol 291:52–66

    Article  Google Scholar 

  68. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS et al (2005) Short-term flood forecasting with a neurofuzzy model. Water Res Res 41:1–16

    Article  Google Scholar 

  69. Ogulata RT, Ogulata SN (2002) Solar radiation on Adana, Turkey. Appl Energy 71(4):351–358

    Article  Google Scholar 

  70. Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342:199–212

    Article  Google Scholar 

  71. Penrose R (1955) A generalized inverse for matrices. Proc Cambridge Philoso Soc 51:406–413

    Article  Google Scholar 

  72. Penrose R (1956) On best approximate solution of linear matrix equations. Proc Cambridge Philoso Soc 52:17–19

    Article  Google Scholar 

  73. Press WH, Teukolsky SA, Vetterling WT, Flannery BP et al. (1992) Numerical recipes in C, 2nd edn. Cambridge University Press, Cambridge. ISBN 0-521-43108-5

    Google Scholar 

  74. Remesan R, Bray M, Shamim MA, Han D (2009) Rainfall-runoff modelling using a wavelet-based hybrid SVM scheme. In: Proceedings of symposium JS. 4 at the joint convention of the international association of hydrological sciences (IAHS) and the International Association of Hydrogeologists (IAH)

    Google Scholar 

  75. Rosenblatt F (1962) Priciples of neurodynamics: perceptrons and the theory of brain mechanics. Spartan, Washington D.C

    Google Scholar 

  76. Rumelhart DE, HintonG E, Williams RJ (1986) Learning representations by backpropagating errors. Nature 323(6088):533–536

    Article  Google Scholar 

  77. Sang YF (2013) A review on the applications of wavelet transform in hydrology time series analysis. Atmos Res 122:8–15

    Article  Google Scholar 

  78. Sang YF, Wang D, Wu JC, Zhu QP, Wang L et al (2009) Entropy-based wavelet de-noising method for time series analysis. Entropy 11(4):1123–1147

    Article  Google Scholar 

  79. Sang YF, Wang ZG, Liu CM et al (2012) Discrete wavelet-based trend identification in hydrologic time series. Hydrol Process 9356. http://dx.doi.org/10.1002/hyp

  80. See L, Openshaw S (2000) Applying soft computing approaches to river level forecasting. Hydrol Sci J 44(5):763–779

    Article  Google Scholar 

  81. Seyyed NBA, Reza K, Mohammad RBL, Kazem S et al (2010) Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN. J Expert Syst Appl Int J archive 37(10):7154–7161

    Article  Google Scholar 

  82. Sfetsos A, Coonick H (2001) Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Sol Energy 68(2):169–178. doi:10.1016/S0038-092X(99)00064-X

    Article  Google Scholar 

  83. Sherman LK (1932) Streamflow from rainfall by unit graph method. Eng News Rec 108:501–505

    Google Scholar 

  84. Sudheer KP, Gosain AK, Rangan DM, Saheb SM et al (2002) Modelling evaporation using an artificial neural network algorithm. Hydrol Process 16:3189–3202

    Article  Google Scholar 

  85. Tan SBK, Shuy EB, Chua LHC (2007) Modelling hourly and daily open-water evaporation rates in areas with an equatorial climate. Hydrol Process 21:486–499. doi:10.1002/hyp.6251

    Article  Google Scholar 

  86. Tao PC, Delleur JW (1976) Seasonal and nonseasonal ARMA models in hydrology. J Hydraul Eng ASCE 102(HY10):1541–1559

    Google Scholar 

  87. Terzi O, Keskin ME (2005) Modelling of daily pan evaporation. J Appl Sci 5(2):368–372

    Article  Google Scholar 

  88. Terzi O, Keskin ME, Taylan ED (2006) Estimating evaporation using ANFIS. J Irrig Drain Eng, ASCE 503–507

    Google Scholar 

  89. Todini E (1978) Using a desk-top computer for an on-line flood warning system. IBM J Res Dev 22:464–471

    Article  Google Scholar 

  90. Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteorol Soc 79(1):61–78

    Article  Google Scholar 

  91. Unal NE, Aksoy H, Akar T et al (2004) Annual and monthly rainfall data generation schemes. Stochast Environ Res Risk Assess 18(4):245–257

    Article  Google Scholar 

  92. Van Geer FC, Zuur AF (1997) An extension of Box-Jenkins transfer/noise models for spatial interpolation of groundwater head series. J Hydrol 192:65–80

    Google Scholar 

  93. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  94. Wang W, Ding J (2003) Wavelet network model and its application to the prediction of the hydrology. Nature Sci 1(1):67–71

    Google Scholar 

  95. Wei CC (2012) Wavelet support vector machines for forecasting precipitation in tropical cyclones: comparisons with GSVM, regression, and MM5. Weather Forecast 27:438–450

    Article  Google Scholar 

  96. Wei W, Wang X, Xie D, Liu H (2007) Soil water content forecasting by support vector machine in purple hilly region. Computer and computing technologies in agriculture. Int Fed Inf Process 258:223–230. doi:10.1007/978-0-387-77251-6_25

  97. Werbos PJ (1988) Generalization of backpropagation with application to a recurrent gas market model. Neural Netw 1(4):339–356

    Article  Google Scholar 

  98. Wu CL, Chau KW, Li YS et al (2008) River stage prediction based on a distributed support vector regression. J Hydrol 358:96–111

    Article  Google Scholar 

  99. Xingang D, Ping W, Jifan C (2003) Multiscale characteristics of the rainy season rainfall and interdecadal decaying of summer monsoon in North China. Chin Sci Bull 48:2730–2734

    Article  Google Scholar 

  100. Xiong LH, Shamseldin AY, O’Connor KM et al (2001) A nonlinear combination of the forecasts of rainfall-runoff models by the first order Takagi-Sugeno fuzzy system. J Hydrol 245(1–4):196–217

    Article  Google Scholar 

  101. Young PC (2002) Advances in real-time flood forecasting. Phil Trans R Soc A 360(1796):1433–1450

    Article  Google Scholar 

  102. Young PC, Garnier H (2006) Identification and estimation of continuous-time, data-based mechanistic (DBM) models for environmental systems. Environ Modell Softw 21(8):1055–1072

    Article  Google Scholar 

  103. Yu PS, Chen ST, Chang IF et al (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3–4):704–716

    Article  Google Scholar 

  104. Yueqing X, Shuangcheng L, Yunlong C et al (2004) Wavelet analysis of rainfall variation in the Hebei Plain. Sci China Ser D 48:2241–2250

    Google Scholar 

  105. Zadeh LA (1965) Fuzzy sets. Inform. Control 8(3):338–353

    Article  Google Scholar 

  106. Zhou H, Wu L, Guo Y (2006) Mid and long term hydrologic forecasting for drainage are based on WNN and FRM. In: Sixth international conference on intelligent systems design and applications, ISDA, pp 7–1

    Google Scholar 

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Authors and Affiliations

  1. Cranfield Water Science Institute, Cranfield University, Cranfield, Bedfordshire, UK

    Renji Remesan

  2. Department of Computer Science, University of Bristol, Bristol, UK

    Jimson Mathew

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  1. Renji Remesan
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Remesan, R., Mathew, J. (2015). Machine Learning and Artificial Intelligence-Based Approaches. In: Hydrological Data Driven Modelling. Earth Systems Data and Models, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-09235-5_4

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