Predicting River Flow Using an AI-Based Sequential Adaptive Neuro-Fuzzy Inference System
Artificial intelligence (AI) techniques have been successfully adopted in predictive modeling to capture the nonlinearity of natural systems. The high seasonal variability of rivers in cold weather regions poses a challenge to river flow forecasting, which tends to be complex and data demanding. Thi...
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2020
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Online Access: | https://doi.org/10.3390/w12061622 https://doaj.org/article/b62b1e57ad9b4dc09dd5e32dca97df2a |
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ftdoajarticles:oai:doaj.org/article:b62b1e57ad9b4dc09dd5e32dca97df2a 2023-05-15T15:26:02+02:00 Predicting River Flow Using an AI-Based Sequential Adaptive Neuro-Fuzzy Inference System Chiara Belvederesi John A. Dominic Quazi K. Hassan Anil Gupta Gopal Achari 2020-06-01T00:00:00Z https://doi.org/10.3390/w12061622 https://doaj.org/article/b62b1e57ad9b4dc09dd5e32dca97df2a EN eng MDPI AG https://www.mdpi.com/2073-4441/12/6/1622 https://doaj.org/toc/2073-4441 doi:10.3390/w12061622 2073-4441 https://doaj.org/article/b62b1e57ad9b4dc09dd5e32dca97df2a Water, Vol 12, Iss 1622, p 1622 (2020) ANFIS hydrological modeling Athabasca River water resources predictive modeling Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 article 2020 ftdoajarticles https://doi.org/10.3390/w12061622 2022-12-30T23:51:45Z Artificial intelligence (AI) techniques have been successfully adopted in predictive modeling to capture the nonlinearity of natural systems. The high seasonal variability of rivers in cold weather regions poses a challenge to river flow forecasting, which tends to be complex and data demanding. This study proposes a novel technique to forecast flows that use a single-input sequential adaptive neuro-fuzzy inference system (ANFIS) along the Athabasca River in Alberta, Canada. After estimating the optimal lead time between four hydrometric stations, gauging data measured near the source were used to predict river flow near the mouth, over approximately 1000 km. The performance of this technique was compared to nonsequential and multi-input ANFISs, which use gauging data measured at each of the four hydrometric stations. The results show that a sequential ANFIS can accurately predict river flow ( r 2 = 0.99, Nash–Sutcliffe = 0.98) with a longer lead time (6 days) by using a single input, compared to nonsequential and multi-input ANFIS (2 days). This method provides accurate predictions over large distances, allowing for flow forecasts over longer periods of time. Therefore, governmental agencies and community planners could utilize this technique to improve flood prevention and planning, operations, maintenance, and the administration of water resource systems. Article in Journal/Newspaper Athabasca River Directory of Open Access Journals: DOAJ Articles Athabasca River Canada Nash ENVELOPE(-62.350,-62.350,-74.233,-74.233) Sutcliffe ENVELOPE(-81.383,-81.383,50.683,50.683) Water 12 6 1622 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
ANFIS hydrological modeling Athabasca River water resources predictive modeling Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
spellingShingle |
ANFIS hydrological modeling Athabasca River water resources predictive modeling Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Chiara Belvederesi John A. Dominic Quazi K. Hassan Anil Gupta Gopal Achari Predicting River Flow Using an AI-Based Sequential Adaptive Neuro-Fuzzy Inference System |
topic_facet |
ANFIS hydrological modeling Athabasca River water resources predictive modeling Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
description |
Artificial intelligence (AI) techniques have been successfully adopted in predictive modeling to capture the nonlinearity of natural systems. The high seasonal variability of rivers in cold weather regions poses a challenge to river flow forecasting, which tends to be complex and data demanding. This study proposes a novel technique to forecast flows that use a single-input sequential adaptive neuro-fuzzy inference system (ANFIS) along the Athabasca River in Alberta, Canada. After estimating the optimal lead time between four hydrometric stations, gauging data measured near the source were used to predict river flow near the mouth, over approximately 1000 km. The performance of this technique was compared to nonsequential and multi-input ANFISs, which use gauging data measured at each of the four hydrometric stations. The results show that a sequential ANFIS can accurately predict river flow ( r 2 = 0.99, Nash–Sutcliffe = 0.98) with a longer lead time (6 days) by using a single input, compared to nonsequential and multi-input ANFIS (2 days). This method provides accurate predictions over large distances, allowing for flow forecasts over longer periods of time. Therefore, governmental agencies and community planners could utilize this technique to improve flood prevention and planning, operations, maintenance, and the administration of water resource systems. |
format |
Article in Journal/Newspaper |
author |
Chiara Belvederesi John A. Dominic Quazi K. Hassan Anil Gupta Gopal Achari |
author_facet |
Chiara Belvederesi John A. Dominic Quazi K. Hassan Anil Gupta Gopal Achari |
author_sort |
Chiara Belvederesi |
title |
Predicting River Flow Using an AI-Based Sequential Adaptive Neuro-Fuzzy Inference System |
title_short |
Predicting River Flow Using an AI-Based Sequential Adaptive Neuro-Fuzzy Inference System |
title_full |
Predicting River Flow Using an AI-Based Sequential Adaptive Neuro-Fuzzy Inference System |
title_fullStr |
Predicting River Flow Using an AI-Based Sequential Adaptive Neuro-Fuzzy Inference System |
title_full_unstemmed |
Predicting River Flow Using an AI-Based Sequential Adaptive Neuro-Fuzzy Inference System |
title_sort |
predicting river flow using an ai-based sequential adaptive neuro-fuzzy inference system |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/w12061622 https://doaj.org/article/b62b1e57ad9b4dc09dd5e32dca97df2a |
long_lat |
ENVELOPE(-62.350,-62.350,-74.233,-74.233) ENVELOPE(-81.383,-81.383,50.683,50.683) |
geographic |
Athabasca River Canada Nash Sutcliffe |
geographic_facet |
Athabasca River Canada Nash Sutcliffe |
genre |
Athabasca River |
genre_facet |
Athabasca River |
op_source |
Water, Vol 12, Iss 1622, p 1622 (2020) |
op_relation |
https://www.mdpi.com/2073-4441/12/6/1622 https://doaj.org/toc/2073-4441 doi:10.3390/w12061622 2073-4441 https://doaj.org/article/b62b1e57ad9b4dc09dd5e32dca97df2a |
op_doi |
https://doi.org/10.3390/w12061622 |
container_title |
Water |
container_volume |
12 |
container_issue |
6 |
container_start_page |
1622 |
_version_ |
1766356596249591808 |