Skip to main content
Log in

Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Predicting the dynamics of water-level in lakes plays a vital role in navigation, water resources planning and catchment management. In this paper, the Extreme Learning Machine (ELM) approach was used to predict the daily water-level in the Urmia Lake. Daily water-level data from the Urmia Lake in northwest of Iran were used to train, test and validate the employed models. Results showed that the ELM approach can accurately forecast the water-level in the Urmia Lake. Outcomes from the ELM model were also compared with those of genetic programming (GP) and artificial neural networks (ANNs). It was found that the ELM technique outperforms GP and ANN in predicting water-level in the Urmia Lake. It also can learn the relation between the water-level and its influential variables much faster than the GP and ANN. Overall, the results show that the ELM approach can be used to predict dynamics of water-level in lakes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Altunkaynak A (2007) Forecasting surface water level fluctuations of Lake Van by artificial neural networks. Water Resour Manag 21(2):399–408

    Article  Google Scholar 

  • Annema AJ, Hoen K, Wallinga H (1994) Precision requirements for single-layer feedforward neural networks. In: Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, 145–151

  • Buyukyildiz M, Tezel G, Yilmaz V (2014) Estimation of the change in lake water level by artificial intelligence methods. Water Resour Manag 28(13):4747–4763

    Article  Google Scholar 

  • Chevillon G, Hendry DF (2005) Non-parametric direct multi-step estimation for forecasting economic processes. Int J Forecast 21(2):201–218

    Article  Google Scholar 

  • Fan S, Hyndman RJ (2012) Short-term load forecasting based on a semi-parametric additive model. IEEE Trans Power Syst 27(1):134–141

    Article  Google Scholar 

  • Farmer JD, John J, Sidorowich S (1987) Predicting chaotic time series. Phys Rev Lett 59(8):45–848

    Article  Google Scholar 

  • Ghouti L, Sheltami TR, Alutaibi KS (2013) Mobility prediction in mobile Ad Hoc networks using extreme learning machines. Procedia Comput Sci 19:305–312

    Article  Google Scholar 

  • Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Int Joint Conf Neural Netw 2:985–990

    Google Scholar 

  • Huang GB, Zhu QY, Siew CK (2003) Real-time learning capability of neural networks, Technical Report ICIS/45/2003, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, April 2003

  • Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Article  Google Scholar 

  • Karimi S, Shiri J, Kisi O, Makarynskyy O (2012) Forecasting water level fluctuations of Urmieh Lake using gene expression programming and adaptive neuro- fuzzy inference system. Int J Ocean Clim Syst 3(2):109–125

    Article  Google Scholar 

  • Karimi S, Kisi O, Shiri J, Makarynskyy O (2013) Neuro fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia. Comput Geosci 52:50–59

    Article  Google Scholar 

  • Karunanithi N, Grenney WJ, Whitley D, Bovee K (1994) Neural networks for river flow prediction. J Comput Civ Eng 8(2):201–220

    Article  Google Scholar 

  • Kisi O, Shiri J, Nikoofar B (2012) Forecasting daily lake levels using artificial intelligence approaches. Comput Geosci 41:169–180

    Article  Google Scholar 

  • Kisi O, Shiri J, Karimi S, Shamshirband S, Motamedi S, Petković D, Hashim R (2015) A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm. Appl Math Comput 270:731–743

  • Koza JR (1992) Genetic programming: on the programming of computers by natural selection. MIT Press, Cambridge, MA

    Google Scholar 

  • Liang NY, Huang GB, Rong HJ, Saratchandran P, Sundararajan N (2006) A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17:1411–1423

    Article  Google Scholar 

  • Marcellino M, Stock J, Watson MW (2006) A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. J Econ 135:499–526

    Article  Google Scholar 

  • Martin C (1989) Nonlinear prediction of chaotic time series. Physica D 35:335–356

    Article  Google Scholar 

  • McSharry PE, Bouwman S, Bloemhof G (2005) Probabilistic forecasts of the magnitude and timing of peak electricity demand. IEEE Trans Power Syst 20:1166–1172

    Article  Google Scholar 

  • Nian R, He B, Zheng B, Heeswijk MV, Yu Q, Miche Y (2014) Extreme learning machine towards dynamic model hypothesis in fish ethology research. Neurocomputing 128:273–284

    Article  Google Scholar 

  • Ramanathan R, Engle RF, Granger CWJ, Vahid F, Brace C (1997) J Forecast 13:161–174

    Article  Google Scholar 

  • Sajjadi S, Shamshirband S, Alizamir M, Yee L, Mansor Z, Manaf AA, Altameem TA, Mostafaeipour A (2016) Extreme learning machine for prediction of heat load in district heating systems. Energy Build 122:222–227

  • Shamshirband S, Mohammadi K, Chen HL, Samy GN, Petković D, Ma C (2015) Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: a case study for Iran. J Atmos Sol Terr Phys 134:109–117

  • Shiri J, Makarynskyy O, Kisi O, Dierickx W, FakheriFard A (2011) Prediction of short term operational water levels using an adaptive neuro-fuzzy inference system. J Waterw Port Coast Ocean Div ASCE 137(6):344–354

    Article  Google Scholar 

  • Singh R, Balasundaram S (2007) Application of extreme learning machine method for time series analysis. Int J Intell Technol 2:256–62

    Google Scholar 

  • Sulaiman M, El-Shafie A, Karim O, Basri H (2011) Improved water level forecasting performance by using optimal steepness coefficients in an artificial neural network. Water Resour Manag 25:2525–2541

    Article  Google Scholar 

  • Vuglinskiy V (2009) Water Level: water level in lakes and reservoirs, water storage. Assessment of the status of the development of the standards for the terrestrial essential climate variables, Global Terrestrial Observing System (GTOS), Rome, Italy, pp 26

  • Wang X, Han M (2014) Online sequential extreme learning machine with kernels for nonstationary time series prediction. Neurocomputing 145:90–97

    Article  Google Scholar 

  • Yu Q, Miche Y, Séverin E, Lendasse A (2014) Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing 128:296–302

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jalal Shiri or Shahaboddin Shamshirband.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shiri, J., Shamshirband, S., Kisi, O. et al. Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach. Water Resour Manage 30, 5217–5229 (2016). https://doi.org/10.1007/s11269-016-1480-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-016-1480-x

Keywords

Navigation