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Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data

Author

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  • Ozgur Kisi

    (International Balck Sea University)

  • Meysam Alizamir

    (Islamic Azad University)

  • Mohammad Zounemat-Kermani

    (Shahid Bahonar University of Kerman)

Abstract

The accuracies of three different evolutionary artificial neural network (ANN) approaches, ANN with genetic algorithm (ANN-GA), ANN with particle swarm optimization (ANN-PSO) and ANN with imperialist competitive algorithm (ANN-ICA), were compared in estimating groundwater levels (GWL) based on precipitation, evaporation and previous GWL data. The input combinations determined using auto-, partial auto- and cross-correlation analyses and tried for each model are: (i) GWL t−1 and GWL t−2; (ii) GWL t−1, GWL t−2 and P t ; (iii) GWL t−1, GWL t−2 and E t ; (iv) GWL t−1, GWL t−2, P t and E t ; (v) GWL t−1, GWL t−2 and P t−1 where GWL t , P t and E t indicate the GWL, precipitation and evaporation at time t, individually. The optimal ANN-GA, ANN-PSO and ANN-ICA models were obtained by trying various control parameters. The best accuracies of the ANN-GA, ANN-PSO and ANN-ICA models were obtained from input combination (i). The mean square error accuracies of the ANN-GA and ANN-ICA models were increased by 165 and 124% using ANN-PSO model. The results indicated that the ANN-PSO model performed better than the other models in modeling monthly groundwater levels.

Suggested Citation

  • Ozgur Kisi & Meysam Alizamir & Mohammad Zounemat-Kermani, 2017. "Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 367-381, May.
    • Handle: RePEc:spr:nathaz:v:87:y:2017:i:1:d:10.1007_s11069-017-2767-9
    DOI: 10.1007/s11069-017-2767-9
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    References listed on IDEAS

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    1. Purna Nayak & Y. Rao & K. Sudheer, 2006. "Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(1), pages 77-90, February.
    2. S. Mohanty & Madan Jha & S. Raul & R. Panda & K. Sudheer, 2015. "Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5521-5532, December.
    3. Shishir Gaur & Sudheer Ch & Didier Graillot & B. Chahar & D. Kumar, 2013. "Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 927-941, February.
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    Cited by:

    1. Mohammad Zounemat-Kermani & Abdollah Ramezani-Charmahineh & Reza Razavi & Meysam Alizamir & Taha B.M.J. Ouarda, 2020. "Machine Learning and Water Economy: a New Approach to Predicting Dams Water Sales Revenue," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 1893-1911, April.
    2. Alizamir, Meysam & Kim, Sungwon & Kisi, Ozgur & Zounemat-Kermani, Mohammad, 2020. "A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions," Energy, Elsevier, vol. 197(C).
    3. Meysam Alizamir & Ozgur Kisi & Ali Najah Ahmed & Cihan Mert & Chow Ming Fai & Sungwon Kim & Nam Won Kim & Ahmed El-Shafie, 2020. "Advanced machine learning model for better prediction accuracy of soil temperature at different depths," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-25, April.
    4. Jui-Sheng Chou & Dinh-Nhat Truong & Yonatan Che, 2020. "Optimized multi-output machine learning system for engineering informatics in assessing natural hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 101(3), pages 727-754, April.
    5. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.

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