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

Valizadeh, Nariman and Mirzaei, Majid and Allawi, Mohammed Falah and Afan, Haitham Abdulmohsin and Mohd, Nuruol Syuhadaa and Hussain, Aini and El-Shafie, Ahmed (2017) Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art. Natural Hazards, 86 (3). pp. 1377-1392. ISSN 0921-030X

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Official URL: https://doi.org/10.1007/s11069-017-2740-7

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.

Item Type: Article
Uncontrolled Keywords: Artificial intelligence; Geo-statistical models; Ungauged river
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 26 Oct 2019 03:51
Last Modified: 26 Oct 2019 03:51
URI: http://eprints.um.edu.my/id/eprint/22855

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