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Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting

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  • Veysel Güldal
  • Hakan Tongal

Abstract

Accurate prediction of lake-level changes is a very important problem for a wise and sustainable use. In recent years significant lake level fluctuations have occurred and can be related to the climatic change. Such a problem is crucial to the works and decisions related to the water resources and management. This study is aimed to predict future lake levels during hydrometeorological changes and anthropogenic activities taking place in the Lake Eğirdir which is the most important water storage of Lake Region, one of the biggest fresh water lakes of Turkey. For this aim, recurrent neural network (RNN), adaptive network-based fuzzy inference system (ANFIS) as prediction models which have various input structures were constructed and the best fit model was investigated. Also, the classical stochastic models, auto-regressive (AR) and auto-regressive moving average (ARMA) models are generated and compared with RNN and ANFIS models. The performances of the models are examined with the form of numerical and graphical comparisons in addition to some statistic efficiency criteria. The results indicated that the RNN and ANFIS can be applied successfully and provide high accuracy and reliability for lake-level changes than the AR and the ARMA models. Also it was shown that these stochastic models can be used in the lake management policies with the acceptable risk. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • Veysel Güldal & Hakan Tongal, 2010. "Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 105-128, January.
    • Handle: RePEc:spr:waterr:v:24:y:2010:i:1:p:105-128
    DOI: 10.1007/s11269-009-9439-9
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    Cited by:

    1. Meral Buyukyildiz & Gulay Tezel & Volkan Yilmaz, 2014. "Estimation of the Change in Lake Water Level by Artificial Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4747-4763, October.
    2. Xuesong Zhang & Kaiguang Zhao, 2012. "Bayesian Neural Networks for Uncertainty Analysis of Hydrologic Modeling: A Comparison of Two Schemes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2365-2382, June.
    3. Hadi Sanikhani & Ozgur Kisi, 2012. "River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1715-1729, April.
    4. Amir Hossein Zaji & Hossein Bonakdari & Bahram Gharabaghi, 2019. "Advancing Freshwater Lake Level Forecast Using King’s Castle Optimization with Training Sample Adaption and Adaptive Neuro-Fuzzy Inference System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4215-4230, September.
    5. Salvatore Campisi-Pinto & Jan Adamowski & Gideon Oron, 2012. "Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3539-3558, September.
    6. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    7. Muhammet Emiroglu & Ozgur Kisi, 2013. "Prediction of Discharge Coefficient for Trapezoidal Labyrinth Side Weir Using a Neuro-Fuzzy Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1473-1488, March.
    8. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    9. Hossein Bonakdari & Isa Ebtehaj & Pijush Samui & Bahram Gharabaghi, 2019. "Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3965-3984, September.
    10. Hossein Kakahaji & Hamed Banadaki & Abbas Kakahaji & Abdulamir Kakahaji, 2013. "Prediction of Urmia Lake Water-Level Fluctuations by Using Analytical, Linear Statistic and Intelligent Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(13), pages 4469-4492, October.
    11. Samad Emamgholizadeh & Khadije Moslemi & Gholamhosein Karami, 2014. "Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5433-5446, December.
    12. Hadi Sanikhani & Ozgur Kisi & Mohammad Nikpour & Yagob Dinpashoh, 2012. "Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4347-4365, December.
    13. Andres Ticlavilca & Mac McKee, 2011. "Multivariate Bayesian Regression Approach to Forecast Releases from a System of Multiple Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 523-543, January.

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