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Machine Learning and Water Economy: a New Approach to Predicting Dams Water Sales Revenue

Author

Listed:
  • Mohammad Zounemat-Kermani

    (Shahid Bahonar University of Kerman)

  • Abdollah Ramezani-Charmahineh

    (Shahid Bahonar University of Kerman
    Shahrekord University)

  • Reza Razavi

    (IAU University)

  • Meysam Alizamir

    (Islamic Azad University)

  • Taha B.M.J. Ouarda

    (Canada Research Chair in Statistical Hydro-Climatology, INRS-ÉTÉ, 490, rue de la Couronne)

Abstract

The proper prediction of water sales revenue allows for pricing policies with a specified trend for the optimized use of water resources. The present work focuses on the prediction of the economic status of water sales revenue in a semi-arid environment. To meet this objective, evaporation data (E), dam input water volume (I), and dam output water volume (O) are used as independent factors to estimate water revenue (R) in the case study of Jiroft Dam, Iran. Different machine learning models are used, including classification and regression tree (CART), Chi-squared automatic interaction detector (CHAID), multi-layer perceptron neural network (MLP), and radial basis function neural network (RBF). The data are obtained daily from 20 March 2012 to 20 March 2015 and defined in six input combinations to the models using multicollinearity analyses. To compare these models, the Nash-Sutcliffe efficiency coefficient (NSEC), the root mean square error (RMSE), and the coefficient of correlation (CC) criteria are employed. All the models act better when records of water sales revenue are incorporated as additional input factors to the machine learning models. The MLP neural-based model indicates the best predicted values for daily water sales revenue (RMSE = 638.3 $ and CC = 0.798) followed by the RBF neural model (RMSE = 655.1 $ and CC = 0.786).

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:6:d:10.1007_s11269-020-02529-0
    DOI: 10.1007/s11269-020-02529-0
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    1. Gomez-Limon, Jose A. & Martinez, Yolanda, 2006. "Multi-criteria modelling of irrigation water market at basin level: A Spanish case study," European Journal of Operational Research, Elsevier, vol. 173(1), pages 313-336, August.
    2. Zaher Mundher Yaseen & Minglei Fu & Chen Wang & Wan Hanna Melini Wan Mohtar & Ravinesh C. Deo & Ahmed El-shafie, 2018. "Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1883-1899, March.
    3. Cortignani, Raffaele & Severini, Simone, 2009. "Modeling farm-level adoption of deficit irrigation using Positive Mathematical Programming," Agricultural Water Management, Elsevier, vol. 96(12), pages 1785-1791, December.
    4. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    5. 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.
    6. Mehrdad Jeihouni & Ara Toomanian & Ali Mansourian, 2020. "Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 139-154, January.
    7. Shishir Gaur & Apurve Dave & Anurag Gupta & Anurag Ohri & Didier Graillot & S. B. Dwivedi, 2018. "Application of Artificial Neural Networks for Identifying Optimal Groundwater Pumping and Piping Network Layout," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5067-5079, December.
    8. Riesgo, Laura & Gomez-Limon, Jose A., 2006. "Multi-criteria policy scenario analysis for public regulation of irrigated agriculture," Agricultural Systems, Elsevier, vol. 91(1-2), pages 1-28, November.
    9. Fereshteh Modaresi & Shahab Araghinejad & Kumars Ebrahimi, 2018. "A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasti," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 243-258, January.
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    1. Yahia Mutalib Tofiq & Sarmad Dashti Latif & Ali Najah Ahmed & Pavitra Kumar & Ahmed El-Shafie, 2022. "Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5999-6016, December.

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