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

Nariman Valizadeh, Majid Mirzaei, Mohammed Falah Allawi (), Haitham Abdulmohsin Afan, Nuruol Syuhadaa Mohd, Aini Hussain and Ahmed El-Shafie
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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

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2017, vol. 86, issue 3, No 20, 1377-1392

Abstract: 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.

Keywords: Artificial intelligence; Geo-statistical models; Ungauged river (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11069-017-2740-7

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