Artificial intelligence based models for stream-flow forecasting: 2000-2015

Zaher Mundher Yaseen, Ahmed El-shafie, Othman Jaafar, Haitham Abdulmohsin Afan, Khamis Naba Sayl

Research output: Contribution to journalArticle

120 Citations (Scopus)

Abstract

The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

Original languageEnglish
Pages (from-to)829-844
Number of pages16
JournalJournal of Hydrology
Volume530
DOIs
Publication statusPublished - 1 Nov 2015

Fingerprint

artificial intelligence
streamflow
modeling
inflow
time series
engineering

Keywords

  • Artificial intelligence
  • Fast orthogonal search
  • Stream-flow forecasting
  • Swarm intelligence

ASJC Scopus subject areas

  • Water Science and Technology

Cite this

Yaseen, Z. M., El-shafie, A., Jaafar, O., Afan, H. A., & Sayl, K. N. (2015). Artificial intelligence based models for stream-flow forecasting: 2000-2015. Journal of Hydrology, 530, 829-844. https://doi.org/10.1016/j.jhydrol.2015.10.038

Artificial intelligence based models for stream-flow forecasting : 2000-2015. / Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba.

In: Journal of Hydrology, Vol. 530, 01.11.2015, p. 829-844.

Research output: Contribution to journalArticle

Yaseen, Zaher Mundher ; El-shafie, Ahmed ; Jaafar, Othman ; Afan, Haitham Abdulmohsin ; Sayl, Khamis Naba. / Artificial intelligence based models for stream-flow forecasting : 2000-2015. In: Journal of Hydrology. 2015 ; Vol. 530. pp. 829-844.
@article{43d81860c80c4c6b8470618a30c5daa7,
title = "Artificial intelligence based models for stream-flow forecasting: 2000-2015",
abstract = "The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.",
keywords = "Artificial intelligence, Fast orthogonal search, Stream-flow forecasting, Swarm intelligence",
author = "Yaseen, {Zaher Mundher} and Ahmed El-shafie and Othman Jaafar and Afan, {Haitham Abdulmohsin} and Sayl, {Khamis Naba}",
year = "2015",
month = "11",
day = "1",
doi = "10.1016/j.jhydrol.2015.10.038",
language = "English",
volume = "530",
pages = "829--844",
journal = "Journal of Hydrology",
issn = "0022-1694",
publisher = "Elsevier",

}

TY - JOUR

T1 - Artificial intelligence based models for stream-flow forecasting

T2 - 2000-2015

AU - Yaseen, Zaher Mundher

AU - El-shafie, Ahmed

AU - Jaafar, Othman

AU - Afan, Haitham Abdulmohsin

AU - Sayl, Khamis Naba

PY - 2015/11/1

Y1 - 2015/11/1

N2 - The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

AB - The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

KW - Artificial intelligence

KW - Fast orthogonal search

KW - Stream-flow forecasting

KW - Swarm intelligence

UR - http://www.scopus.com/inward/record.url?scp=84945157492&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84945157492&partnerID=8YFLogxK

U2 - 10.1016/j.jhydrol.2015.10.038

DO - 10.1016/j.jhydrol.2015.10.038

M3 - Article

AN - SCOPUS:84945157492

VL - 530

SP - 829

EP - 844

JO - Journal of Hydrology

JF - Journal of Hydrology

SN - 0022-1694

ER -