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A Simple Way to Increase the Prediction Accuracy of Hydrological Processes Using an Artificial Intelligence Model

Author

Listed:
  • Ieva Meidute-Kavaliauskiene

    (Department of Business Technologies and Entrepreneurship, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania)

  • Milad Alizadeh Jabehdar

    (Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran)

  • Vida Davidavičienė

    (Department of Business Technologies and Entrepreneurship, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania)

  • Mohammad Ali Ghorbani

    (Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran)

  • Saad Sh. Sammen

    (Department of Civil Engineering, College of Engineering, Diyala University, Baqubah 32001, Diyala, Iraq)

Abstract

Rainfall and evaporation, which are known as two complex and unclear processes in hydrology, are among the key processes in the design and management of water resource projects. The application of artificial intelligence, in comparison with physical and empirical models, can be effective in the face of the complexity of hydrological processes. The present study was prepared with the aim of increasing the accuracy in monthly prediction of rainfall (R) and pan evaporation (EP) by providing a simple solution to determining new inputs for forecasting scenarios. Initially, the prediction of two parameters, R and EP, for the current and one–three lead times, by determining the different input modes, was developed with the SVM model. Then, in order to increase the accuracy of the predictions, the month number (τ) was added to all scenarios in predicting both the R and EP parameters. The results of the intelligent model using several statistical indices (i.e., root mean square error (RMSE), Kling–Gupta (KGE) and correlation coefficient (CC)), with the help of case visual indicators, were compared. The month number (τ) was able to greatly improve the prediction accuracy of both the R and EP parameters under the SVM model and overcome the complexities within these two hydrological processes that the scenarios were not initially able to solve with high accuracy. This is proven in all time steps. According to the RMSE, KGE and CC indices, the highest increase in the forecast accuracy for the upcoming two months of rainfall (R t+2 ) for Ardabil station in scenario 2 (SVM-2) was 19.1, 858 and 125%, and for the current month of pan evaporation (EP t ) for Urmia station in scenario 6 (SVM-6), this occurred at the rates of 40.2, 11.1 and 7.6%, respectively. Finally, in order to investigate the characteristic of the month number in the SVM model under special conditions such as considering the highest values of the R and EP time series, it was proved that by using the month number of the SVM model, again, the accuracy could be improved (on average, 17% improvement for rainfall, and 13% for pan evaporation) in almost all time steps. Due to the wide range of effects of the two variables studied in the hydrological discussion, the results of the present study can be useful in agricultural sciences and in water management in general and will help owners.

Suggested Citation

  • Ieva Meidute-Kavaliauskiene & Milad Alizadeh Jabehdar & Vida Davidavičienė & Mohammad Ali Ghorbani & Saad Sh. Sammen, 2021. "A Simple Way to Increase the Prediction Accuracy of Hydrological Processes Using an Artificial Intelligence Model," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7752-:d:592556
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    References listed on IDEAS

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    1. Chen, Serena H. & Jakeman, Anthony J. & Norton, John P., 2008. "Artificial Intelligence techniques: An introduction to their use for modelling environmental systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 379-400.
    2. Mohammed Sanusi Shiru & Shamsuddin Shahid & Inhwan Park, 2021. "Projection of Water Availability and Sustainability in Nigeria Due to Climate Change," Sustainability, MDPI, vol. 13(11), pages 1-16, June.
    3. Kieu Anh Nguyen & Walter Chen & Bor-Shiun Lin & Uma Seeboonruang, 2020. "Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    4. Saad SH. Sammen & T. A. Mohamed & A. H. Ghazali & A. H. El-Shafie & L. M. Sidek, 2017. "Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 549-562, January.
    5. Lamine Diop & Saeed Samadianfard & Ansoumana Bodian & Zaher Mundher Yaseen & Mohammad Ali Ghorbani & Hana Salimi, 2020. "Annual Rainfall Forecasting Using Hybrid Artificial Intelligence Model: Integration of Multilayer Perceptron with Whale Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 733-746, January.
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