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Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach

Author

Listed:
  • Muhammad Ali Musarat

    (Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak 32610, Malaysia)

  • Wesam Salah Alaloul

    (Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak 32610, Malaysia)

  • Muhammad Babar Ali Rabbani

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

  • Mujahid Ali

    (Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak 32610, Malaysia)

  • Muhammad Altaf

    (Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak 32610, Malaysia)

  • Roman Fediuk

    (Polytechnic Institute, Far Eastern Federal University, 690000 Vladivostok, Russia)

  • Nikolai Vatin

    (Institute of Civil Engineering, Peter the Great St. Petersburg Polytechnic University, 195291 St. Petersburg, Russia)

  • Sergey Klyuev

    (Department of Theoretical Mechanics and Resistance of Materials, Belgorod State Technological University Named after V.G. Shukhov, 308012 Belgorod, Russia)

  • Hamna Bukhari

    (National Institute of Transport, NIT-SCEE, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Alishba Sadiq

    (Centre for Intelligent Signal and Imaging Research (CISIR), Electrical and Electronic Engineering Department, University Teknologi PETRONAS, Bandar Seri Iskandar, Perak 32610, Malaysia)

  • Waqas Rafiq

    (Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak 32610, Malaysia
    Department of Civil Engineering, COMSATS University Islamabad Wah Campus, Wah Cantt 47000, Pakistan)

  • Waqas Farooq

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

Abstract

The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation in water levels in rivers, such as floods and droughts, are catastrophic in every manner; therefore, forecasting at an early stage would prevent possible disasters and relief efforts could be set up on time. This study aims to digitally model the water level in the Kabul River to prevent and alleviate the effects of any change in water level in this river downstream. This study used a machine learning tool known as the automatic autoregressive integrated moving average for statistical methodological analysis for forecasting the river flow. Based on the hydrological data collected from the water level of Kabul River in Swat, the water levels from 2011–2030 were forecasted, which were based on the lowest value of Akaike Information Criterion as 9.216. It was concluded that the water flow started to increase from the year 2011 till it reached its peak value in the year 2019–2020, and then the water level will maintain its maximum level to 250 cumecs and minimum level to 10 cumecs till 2030. The need for this research is justified as it could prove helpful in establishing guidelines for hydrological designers, the planning and management of water, hydropower engineering projects, as an indicator for weather prediction, and for the people who are greatly dependent on the Kabul River for their survival.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10720-:d:644080
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    References listed on IDEAS

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    1. Ernst Wit & Edwin van den Heuvel & Jan-Willem Romeijn, 2012. "‘All models are wrong...’: an introduction to model uncertainty," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 217-236, August.
    2. 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.
    3. Tao Guo & Wei He & Zhonglian Jiang & Xiumin Chu & Reza Malekian & Zhixiong Li, 2018. "An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level," Energies, MDPI, vol. 12(1), pages 1-11, December.
    4. Thomas J. Mack & Michael P. Chornack & Mohammad R. Taher, 2013. "Groundwater-level trends and implications for sustainable water use in the Kabul Basin, Afghanistan," Environment Systems and Decisions, Springer, vol. 33(3), pages 457-467, September.
    5. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    6. Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    7. Dimitrios Myronidis & Konstantinos Ioannou & Dimitrios Fotakis & Gerald Dörflinger, 2018. "Streamflow and Hydrological Drought Trend Analysis and Forecasting in Cyprus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1759-1776, March.
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