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Assessing the effectiveness of artificial intelligence models in predicting Zayanderud dam inflow at different time scales

  • Research Article - Hydrology and Hydraulics
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Abstract

The challenge of managing sustainable water supply is growing more complex as a result of global climate change. The utilization of dams and reservoirs is essential for effective water resource management. These systems are of utmost importance in the storage and utilization of water throughout the entire year. In water resource planning for these systems, a significant emphasis is placed on predicting inflow to dams. In recent years, there has been notable advancement in artificial intelligence approaches, which have the potential to decrease prediction uncertainties. This study conducts a comparison between implementing machine learning models, specifically the group method of data handling (GMDH), random forest (RF), and adaptive neuro-fuzzy inference system (ANFIS), and the probabilistic Bayesian network method as well as long short-term memory networks (LSTM). This study presents a novel comparison between two black box approaches, analyzing probabilities across three different time scales: monthly, seasonal, and annual. Three temporal approaches were employed to estimate the inflow rate to Zayanderud dam, considering 10 scenarios and identifying influential variables. The results obtained from the simulation showed that the GMDH method, a complex neural network technique, exhibited superior performance in all three temporal scales: monthly, seasonal, and annual. The obtained results were evaluated through the utilization of four statistical indicators. The NS coefficient for the annual approach is 0.98, for the seasonal approach is 0.93, and for the monthly approach is 0.95. Additionally, the mean squared error (MSE) was 0.07, 0.09, and 0.11 for the annual, seasonal, and monthly approaches, respectively. The results showed that the transfer inflow from the Koohrang tunnels and Cheshmeh Langan tunnel greatly influenced the predictions in all three approaches. Furthermore, the precipitation parameter (R) displayed sensitivity solely in the annual and monthly assessments, while no correlation was evident in the seasonal approach.

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Authors and Affiliations

  1. Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Saloomeh Akbari Osmavandani & Ahmad Sharafati

  2. Water Research Institute, Ministry of Energy Water Research Institute, Tehran, Iran

    Hamid Kardan Moghaddam

Authors
  1. Saloomeh Akbari Osmavandani
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  2. Ahmad Sharafati
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  3. Hamid Kardan Moghaddam
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Correspondence to Ahmad Sharafati.

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The authors declare that they have no competing interests.

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Edited by Dr. Khabat Khosravi (ASSOCIATE EDITOR) / Prof. Jochen Aberle (CO-EDITOR-IN-CHIEF).

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Akbari Osmavandani, S., Sharafati, A. & Moghaddam, H.K. Assessing the effectiveness of artificial intelligence models in predicting Zayanderud dam inflow at different time scales. Acta Geophys. (2023). https://doi.org/10.1007/s11600-023-01257-4

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  • DOI: https://doi.org/10.1007/s11600-023-01257-4

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