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

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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.

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Acknowledgements

This work was supported by a grant from University of Malaya’s BKP Grant BK023-2015.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Faculty of Engineering, The University of Auckland, Auckland, New Zealand

    Nariman Valizadeh

  2. Faculty of Engineering and Science, Universiti Tuanku Abdul Rahman, Kuala Lumpur, Malaysia

    Majid Mirzaei

  3. Civil and Structural Engineering Department, University Kebangsaan Malaysia, Bangi, Malaysia

    Mohammed Falah Allawi & Haitham Abdulmohsin Afan

  4. Civil Engineering Department, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Nuruol Syuhadaa Mohd & Ahmed El-Shafie

  5. Department of Electronics System Engineering, University Kebangsaan Malaysia, Bangi, Malaysia

    Aini Hussain

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  1. Nariman Valizadeh
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Valizadeh, N., Mirzaei, M., Allawi, M.F. et al. Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art. Nat Hazards 86, 1377–1392 (2017). https://doi.org/10.1007/s11069-017-2740-7

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