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, Ahmed El-Shafie

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalNatural Hazards
DOIs
Publication statusAccepted/In press - 9 Jan 2017

Fingerprint

artificial intelligence
streamflow
river basin
GIS
runoff
spatial distribution
state of the art
method
station

Keywords

  • Artificial intelligence
  • Geo-statistical models
  • Ungauged river

ASJC Scopus subject areas

  • Water Science and Technology
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)

Cite this

Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations : state of the art. / Valizadeh, Nariman; Mirzaei, Majid; Allawi, Mohammed Falah; Afan, Haitham Abdulmohsin; Mohd, Nuruol Syuhadaa; Hussain, Aini; El-Shafie, Ahmed.

In: Natural Hazards, 09.01.2017, p. 1-16.

Research output: Contribution to journalArticle

Valizadeh, Nariman ; Mirzaei, Majid ; Allawi, Mohammed Falah ; Afan, Haitham Abdulmohsin ; Mohd, Nuruol Syuhadaa ; Hussain, Aini ; El-Shafie, Ahmed. / Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations : state of the art. In: Natural Hazards. 2017 ; pp. 1-16.
@article{c067fae5169242db8ea8e8006673390b,
title = "Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art",
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",
author = "Nariman Valizadeh and Majid Mirzaei and Allawi, {Mohammed Falah} and Afan, {Haitham Abdulmohsin} and Mohd, {Nuruol Syuhadaa} and Aini Hussain and Ahmed El-Shafie",
year = "2017",
month = "1",
day = "9",
doi = "10.1007/s11069-017-2740-7",
language = "English",
pages = "1--16",
journal = "Natural Hazards",
issn = "0921-030X",
publisher = "Springer Netherlands",

}

TY - JOUR

T1 - Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations

T2 - state of the art

AU - Valizadeh, Nariman

AU - Mirzaei, Majid

AU - Allawi, Mohammed Falah

AU - Afan, Haitham Abdulmohsin

AU - Mohd, Nuruol Syuhadaa

AU - Hussain, Aini

AU - El-Shafie, Ahmed

PY - 2017/1/9

Y1 - 2017/1/9

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Geo-statistical models

KW - Ungauged river

UR - http://www.scopus.com/inward/record.url?scp=85008625545&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85008625545&partnerID=8YFLogxK

U2 - 10.1007/s11069-017-2740-7

DO - 10.1007/s11069-017-2740-7

M3 - Article

AN - SCOPUS:85008625545

SP - 1

EP - 16

JO - Natural Hazards

JF - Natural Hazards

SN - 0921-030X

ER -