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Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

Author

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  • Abdus Samad Azad

    (Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Tronoh 31750, Perak, Malaysia)

  • Rajalingam Sokkalingam

    (Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Tronoh 31750, Perak, Malaysia)

  • Hanita Daud

    (Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Tronoh 31750, Perak, Malaysia)

  • Sajal Kumar Adhikary

    (Department of Civil Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh)

  • Hifsa Khurshid

    (Department of Civil Engineering, Universiti Teknologi PETRONAS, Tronoh 31750, Perak, Malaysia)

  • Siti Nur Athirah Mazlan

    (Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Tronoh 31750, Perak, Malaysia)

  • Muhammad Babar Ali Rabbani

    (Department of Civil Engineering, Sarhad University of Science and Information Technology, Peshawar 25000, Pakistan)

Abstract

Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR’s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population’s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box–Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2) 12 but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA–ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA–ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA–ANN hybrid model could be a viable option for the accurate prediction of reservoir water level.

Suggested Citation

  • Abdus Samad Azad & Rajalingam Sokkalingam & Hanita Daud & Sajal Kumar Adhikary & Hifsa Khurshid & Siti Nur Athirah Mazlan & Muhammad Babar Ali Rabbani, 2022. "Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study," Sustainability, MDPI, vol. 14(3), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1843-:d:742847
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    References listed on IDEAS

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    1. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    3. Fang, Tingting & Lahdelma, Risto, 2016. "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system," Applied Energy, Elsevier, vol. 179(C), pages 544-552.
    4. Muhammad Ali Musarat & Wesam Salah Alaloul & Muhammad Babar Ali Rabbani & Mujahid Ali & Muhammad Altaf & Roman Fediuk & Nikolai Vatin & Sergey Klyuev & Hamna Bukhari & Alishba Sadiq & Waqas Rafiq & Wa, 2021. "Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach," Sustainability, MDPI, vol. 13(19), pages 1-26, September.
    5. Li, Hong-Xing & Da, Xu Li, 2000. "A neural network representation of linear programming," European Journal of Operational Research, Elsevier, vol. 124(2), pages 224-234, July.
    6. Jeong, Kwangbok & Koo, Choongwan & Hong, Taehoon, 2014. "An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)," Energy, Elsevier, vol. 71(C), pages 71-79.
    7. Abdüsselam Altunkaynak, 2007. "Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(2), pages 399-408, February.
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