Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Open access

Abstract

Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

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  • ACHARYA N. SHRIVASTAVA N.A. PANIGRAHI B.K. MOHANTY U.C. 2014. Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine. Climate Dynamics. Vol. 43. Iss. 5-6 p. 1303-1310.

  • ADAMOWSKI J. CHAN H.F. 2011. A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology. Vol. 407. Iss. 1 p. 28-40.

  • ALLEN D.M. STAHL K. WHITFIELD P.H. MOORE R.D. 2014. Trends in groundwater levels in British Columbia. Canadian Water Resources Journal. Vol. 39. Iss. 1 p. 15-31. DOI: 10.1080/07011784.2014.885677.

  • ALMASRI M.N. KALUARACHCHI J.J. 2005. Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrate loading and recharge data. Environmental Modelling and Software. Vol. 20. Iss. 7 p. 851-871.

  • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology 2000. Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering. Vol. 5. Iss. 2 p. 115-123.

  • BANERJEE P.R. PRASAD K. SINGH V.S. 2009. Forecasting of groundwater level in hard rock region using artificial neural network. Environmental Geology. Vol. 58. Iss. 6 p. 1239-1246.

  • BEHZAD M. ASGHARI K. COPPOLA Jr E.A. 2009. Comparative study of SVMs and ANNs in aquifer water level prediction. Journal of Computing in Civil Engineering. Vol. 24. Iss. 5 p. 408-413.

  • CHANG C.C. LIN C.J. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST). Vol. 2. Iss. 3. No. 27.

  • DALIAKOPOULOS I. COULIBALYA P. TSANI I.K. 2005. Groundwater level forecasting using artificial neural network. Journal of Hydrology. Vol. 309. Iss. 1-4 p. 229-240.

  • DIBIKE Y.B. VELICKOV S. SOLOMATINE D. ABBOTT M.B. 2001. Model induction with support vector machines: introduction and applications. Journal of Computing Civil Engineering. Vol. 15 p. 208-216.

  • EMAMGHOLIZADEH S. MOSLEMI K. KARAMI G. 2014. Prediction the groundwater level of Bastam Plain (Iran) by artificial neural network (ANN) and adaptive neuro- fuzzy inference system (ANFIS). Water Resources Management. Vol. 28. Iss. 15 p. 5433-5446.

  • FALLAH-MEHDIPOUR E. HADDAD O.B. MARIÑO M.A. 2013. Prediction and simulation of monthly groundwater levels by genetic programming. Journal of Hydroenvironment Research. Vol. 7. Iss. 4 p. 253-260.

  • FALLAH-MEHDIPOUR E. HADDAD O.B. MARIÑO M.A. 2014. Genetic programming in groundwater modeling. Journal of Hydrologic Engineering. Vol. 19. Iss. 12 04014031.

  • Farmwest undated. Evapotranspiration [online]. [Access 20.03.2016]. Available at: www.farmwest.com

  • Government of Canada undated. Weather information [online]. [Access 20.03.2016]. Available at: www.weather.gc.ca

  • GURDAK J.S. HANSON R.T. GREEN T.R. 2009. Effects of climate variability and change on groundwater resources of the United States. United States Geological Survey. Fact Sheet 2009-3074.

  • HE Z. WEN X. LIU H. DU J. 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. Vol. 509 p. 379-386.

  • HUANG G. HUANG G.B. SONG S. YOU K. 2015. Trends in extreme learning machines: A review. Neural Network. Vol. 61 p. 32-48.

  • HUANG G.B. WANG D.H. LAN Y. 2011. Extreme learning machines: A survey. International Journal of Machine Learning and Cybernetics. Vol. 2. Iss. 2 p. 107-122.

  • HUANG G.B. ZHU Q.Y. SIEW C.K. 2004. Extreme learning machine: A new learning scheme of feedforward neural networks. In: Proceedings of the international joint conference on neural networks. 25-29 July 2004. Budapest Hungary. Vol. 2 p. 985-990.

  • HUANG G.B. ZHU Q.Y. SIEW C.K. 2006. Extreme learning machine: Theory and applications. Neurocomputing. Vol. 70 p. 489-501.

  • KIM S.J. HYUN Y. LEE K.K. 2005. Time series modeling for evaluation of groundwater discharge rates into an urban subway system. Geoscience Journal. Vol. 9. Iss. 1 p. 15-22.

  • LI F. QIAO J. ZHAO Y. ZHANG W. 2014. Risk assessment of groundwater and its application. Part II. Using a groundwater risk maps to determine control levels of the groundwater. Water Resources Management. Vol. 28. Iss. 13 p. 4875-4893.

  • LIANG N.Y. HUANG G.B. RONG H.J. SARATCHANDRAN P. SUNDARARAJAN N. 2006. A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Transactions Neural Networks. No. 17 p. 1411-1423.

  • MASKEY S. DIBIKE Y.B. JONOSKI A. SOLOMATINE D. 2000. Groundwater model approximation with artificial neural network for selecting optimal pumping strategy for plume removal. In: Proceedings Workshop 2nd Joint Artificial Intelligence in Civil Engineering Applications. ds. O. Schleider A. Zijderveld. March 2000. Cottbus Germany p. 67-80.

  • Ministry of Environment undated. [online]. [Access 20.03.2016]. Available at: www.env.gov.bc.ca

  • MOHAMMADI K. 2008. Groundwater table estimation using MODFLOW and artificial neural networks. Water Science and Technology Library. Vol. 68. Iss. 2 p. 127-138.

  • MOHAMMADI K. SHAMSHIRBAND S. MOTAMEDI S. PETKOVIĆ D. HASHIM R. GOCIC M. 2015a. Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture. Vol. 117 p. 214-225.

  • MOHAMMADI K. SHAMSHIRBAND S. YEE P.L. PETKOVIĆ D. ZAMANI M. CH S. 2015b. Predicting the wind power density based upon extreme learning machine. Energy. Vol. 86 p. 232-239.

  • MOHANTY S. JHA M.K. KUMAR A. PANDA D.K. 2013. Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi-Surua Inter-basin of Odisha India. Journal of Hydrology. Vol. 495 p. 38-51.

  • MOORE R.D. MCKENDRY I.G. 1996. Spring snowpack anomaly patterns and winter climatic variability British Columbia Canada. Water Resources Research. Vol. 32. Iss. 3 p. 623-632.

  • NURHAYATI SOEKARNO I. HADIHARDAJA I.K. CAHYONO M. 2013. The prediction of groundwater level on tidal lowlands reclamation using extreme learning machine. Journal of Theoretical and Applied Information Technology. Vol. 56. Iss. 1 p. 75-84.

  • PÉREZ-MARTÍN M.A. ESTRELA T. ANDREU J. FERRER J. 2014. Modeling water resources and river-aquifer interaction in the Júcar River Basin Spain. Water Resources Management. Vol. 28. Iss. 12 p. 4337-4358.

  • PLATT J.C. 1999. Fast training of support vector machines using sequential minimal optimization. In: Advances in kernel methods: Support vector learning. Eds. B. Schölkopf Ch.J.C. Burges A.J. Smola. Cambridge. MIT Press p. 185-208.

  • PRADHAN B. 2013. A comparative study on the predictive ability of the decision tree support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers and Geosciences. Vol. 51 p. 350-365.

  • QU J. ZUO M.J. 2010. Support vector machine based data processing algorithm for wear degree classification of slurry pump systems. Measurement. Vol. 43. Iss. 6 p. 781-791.

  • RABUNAL J.R. PUERTAS J. SUAREZ J. RIVERO D. 2007. Determination of the unit hydrograph of a typical urban basin genetic programming and artificial neural networks. Hydrological Processes. Vol. 21. Iss. 4 p. 476-485.

  • SAFAVI H.R. ESMIKHANI M. 2013. Conjunctive use of surface water and groundwater: Application of support vector machines (SVMs) and genetic algorithms. Water Resources Management. Vol. 27. Iss. 7 p. 2623-2644.

  • SAVIC D.A. WALTERS G.A. DAVIDSON J.W. 1999. A genetic programming approach to rainfall-runoff modeling. Water Resources Management. Vol. 13. Iss. 3 p. 219-231.

  • SCHÖLKOPF B. SMOLA A.J. 2002. Learning with kernels: Support vector machines regularization optimization and beyond. Cambridge. MIT Press. ISBN 9780262194 754 pp. 648.

  • SETHI R.R. KUMAR A. SHARMA S.P. VERMA H.C. 2010. Prediction of water table depth in a hard rock basin by using artificial neural network. International Journal of Water Resources and Environmental Engineering. Vol. . Iss. 4 p. 95-102.

  • SHAMSHIRBAND S. MOHAMMADI K. TONG C.W. PETKOVIĆ D. PORCU E. MOSTAFAEIPOUR A. CH S. SEDAGHAT A. 2015a. Application of extreme learning machine for estimation of wind speed distribution. Climate Dynamics. vol. 46. Iss. 5 p. 1893-1907.

  • SHAMSHIRBAND S. MOHAMMADI K. YEE L. PETKOVIĆ D. MOSTAFAEIPOUR A. 2015b. A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation. Renewable and Sustainable Energy Reviews. Vol. 52 p. 1031-1042.

  • SREEKANTH P. GEETHANJALI D.N. SREEDEVI P.D. AHMED S. KUMAR N.R. JAYANTHI P.D.K. 2009. Forecasting groundwater level using artificial neural networks. Current Science. Vol. 96. Iss. 7 p. 933-939.

  • SUN Z.L. CHOI T.M. AU K.F. YU Y. 2008. Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems. Vol. 46. Iss. 1 p. 411-419.

  • SURYANARAYANA C. SUDHEER C. MAHAMMOOD V. PANIGRAHI B.K. 2014. An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam India. Neurocomputing. Vol. 145 p. 324-335.

  • TODD D.K. MAYS L.W. 2005. Groundwater hydrology. Third Revision. Danvers MA. John Wiley and Sons Inc. ISBN 9780471059370 pp. 636.

  • TRIPATHI S. SRINIVAS V.V. NANJUNDIAH R.S. 2006. Downscaling of precipitation for climate change scenarios: A support vector machine approach. Journal of Hydrology. Vol. 330. Iss. 3 p. 621-640.

  • VAPNIK V.N. 1995. The nature of statistical learning theory. New York USA. Springer-Verlag pp. 314.

  • WANG X. HAN M. 2014. Online sequential extreme learning machine with kernels for non-stationary time series prediction. Neurocomputing. Vol. 145 p. 90-97.

  • YOON H. HYUN Y. LEE K.K. 2007. Forecasting solute breakthrough curves through the unsaturated zone using artificial neural networks. Journal of Hydrology. Vol. 335 p. 68-77.

  • YOON H. JUN S.C. HYUN Y. BAE G.O. LEE K.K. 2011. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology. Vol. 396. Iss. 1 p. 128-138.

  • ZHAO Z. LI P. XU X. 2013. Forecasting model of coal mine water inrush based on extreme learning machine. Applied Mathematics and Information Sciences. Vol. 7 p. 1243-1250.

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