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Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art

Author

Listed:
  • Nariman Valizadeh

    (The University of Auckland)

  • Majid Mirzaei

    (Universiti Tuanku Abdul Rahman)

  • Mohammed Falah Allawi

    (University Kebangsaan Malaysia)

  • Haitham Abdulmohsin Afan

    (University Kebangsaan Malaysia)

  • Nuruol Syuhadaa Mohd

    (University of Malaya)

  • Aini Hussain

    (University Kebangsaan Malaysia)

  • Ahmed El-Shafie

    (University of Malaya)

Abstract

Developing an accurate model for discharge estimation techniques of the ungauged river basin is a crucial challenge in water resource management especially in under-development regions. This article is a thorough review of the historical improvement stages of this topic to understand previous challenges that faced researchers, the shortfalls of methods and techniques, how researchers prevailed and what deficiencies still require solutions. This revision focuses on data-driven approaches and GIS-based methods that have improved the accuracy of estimation of hydrological variables, considering their advantages and disadvantages. Past studies used artificial intelligence and geo-statistical methods to forecast the runoff at ungauged river basins, and mapping the spatial distribution has been considered in this study. A recommendation for future research on the potential of a hybrid model utilizing both approaches is proposed and described.

Suggested Citation

  • Nariman Valizadeh & Majid Mirzaei & Mohammed Falah Allawi & Haitham Abdulmohsin Afan & Nuruol Syuhadaa Mohd & Aini Hussain & Ahmed El-Shafie, 2017. "Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art," 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. 86(3), pages 1377-1392, April.
    • Handle: RePEc:spr:nathaz:v:86:y:2017:i:3:d:10.1007_s11069-017-2740-7
    DOI: 10.1007/s11069-017-2740-7
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    Cited by:

    1. Yuefeng Yao & Azim Mallik, 2020. "Stream Flow Changes and the Sustainability of Cruise Tourism on the Lijiang River, China," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    2. Sabrina Ali & Ataur Rahman, 2022. "Development of a kriging-based regional flood frequency analysis technique for South-East Australia," 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. 114(3), pages 2739-2765, December.
    3. Majid Mirzaei & Haoxuan Yu & Adnan Dehghani & Hadi Galavi & Vahid Shokri & Sahar Mohsenzadeh Karimi & Mehdi Sookhak, 2021. "A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation," Sustainability, MDPI, vol. 13(23), pages 1-16, December.

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