Past, Present, and Future of Using Neuro-Fuzzy Systems for Hydrological Modeling and Forecasting
Neuro-fuzzy systems (NFS), as part of artificial intelligence (AI) techniques, have become popular in modeling and forecasting applications in many fields in the past few decades. NFS are powerful tools for mapping complex associations between inputs and outputs by learning from available data. Ther...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/10/2/36 |
| _version_ | 1797620673819967488 |
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| author | Yik Kang Ang Amin Talei Izni Zahidi Ali Rashidi |
| author_facet | Yik Kang Ang Amin Talei Izni Zahidi Ali Rashidi |
| author_sort | Yik Kang Ang |
| collection | DOAJ |
| description | Neuro-fuzzy systems (NFS), as part of artificial intelligence (AI) techniques, have become popular in modeling and forecasting applications in many fields in the past few decades. NFS are powerful tools for mapping complex associations between inputs and outputs by learning from available data. Therefore, such techniques have been found helpful for hydrological modeling and forecasting, including rainfall–runoff modeling, flood forecasting, rainfall prediction, water quality modeling, etc. Their performance has been compared with physically based models and data-driven techniques (e.g., regression-based methods, artificial neural networks, etc.), where NFS have been reported to be comparable, if not superior, to other models. Despite successful applications and increasing popularity, the development of NFS models is still challenging due to a number of limitations. This study reviews different types of NFS algorithms and discusses the typical challenges in developing NFS-based hydrological models. The challenges in developing NFS models are categorized under six topics: data pre-processing, input selection, training data selection, adaptability, interpretability, and model parameter optimization. At last, future directions for enhancing NFS models are discussed. This review–prospective article gives a helpful overview of the suitability of NFS techniques for various applications in hydrological modeling and forecasting while identifying research gaps for future studies in this area. |
| first_indexed | 2024-03-11T08:44:59Z |
| format | Article |
| id | doaj.art-77cb788bc36f44d5a56f8d94b80f57a1 |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-11T08:44:59Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-77cb788bc36f44d5a56f8d94b80f57a12023-11-16T20:51:38ZengMDPI AGHydrology2306-53382023-01-011023610.3390/hydrology10020036Past, Present, and Future of Using Neuro-Fuzzy Systems for Hydrological Modeling and ForecastingYik Kang Ang0Amin Talei1Izni Zahidi2Ali Rashidi3Civil Engineering Discipline, School of Engineering, Monash University Malaysia, Subang Jaya 47500, Selangor, MalaysiaCivil Engineering Discipline, School of Engineering, Monash University Malaysia, Subang Jaya 47500, Selangor, MalaysiaCivil Engineering Discipline, School of Engineering, Monash University Malaysia, Subang Jaya 47500, Selangor, MalaysiaFuture Building Initiative, Monash University, Melbourne, VIC 3145, AustraliaNeuro-fuzzy systems (NFS), as part of artificial intelligence (AI) techniques, have become popular in modeling and forecasting applications in many fields in the past few decades. NFS are powerful tools for mapping complex associations between inputs and outputs by learning from available data. Therefore, such techniques have been found helpful for hydrological modeling and forecasting, including rainfall–runoff modeling, flood forecasting, rainfall prediction, water quality modeling, etc. Their performance has been compared with physically based models and data-driven techniques (e.g., regression-based methods, artificial neural networks, etc.), where NFS have been reported to be comparable, if not superior, to other models. Despite successful applications and increasing popularity, the development of NFS models is still challenging due to a number of limitations. This study reviews different types of NFS algorithms and discusses the typical challenges in developing NFS-based hydrological models. The challenges in developing NFS models are categorized under six topics: data pre-processing, input selection, training data selection, adaptability, interpretability, and model parameter optimization. At last, future directions for enhancing NFS models are discussed. This review–prospective article gives a helpful overview of the suitability of NFS techniques for various applications in hydrological modeling and forecasting while identifying research gaps for future studies in this area.https://www.mdpi.com/2306-5338/10/2/36neuro-fuzzy systemshydrological modelingartificial intelligence |
| spellingShingle | Yik Kang Ang Amin Talei Izni Zahidi Ali Rashidi Past, Present, and Future of Using Neuro-Fuzzy Systems for Hydrological Modeling and Forecasting Hydrology neuro-fuzzy systems hydrological modeling artificial intelligence |
| title | Past, Present, and Future of Using Neuro-Fuzzy Systems for Hydrological Modeling and Forecasting |
| title_full | Past, Present, and Future of Using Neuro-Fuzzy Systems for Hydrological Modeling and Forecasting |
| title_fullStr | Past, Present, and Future of Using Neuro-Fuzzy Systems for Hydrological Modeling and Forecasting |
| title_full_unstemmed | Past, Present, and Future of Using Neuro-Fuzzy Systems for Hydrological Modeling and Forecasting |
| title_short | Past, Present, and Future of Using Neuro-Fuzzy Systems for Hydrological Modeling and Forecasting |
| title_sort | past present and future of using neuro fuzzy systems for hydrological modeling and forecasting |
| topic | neuro-fuzzy systems hydrological modeling artificial intelligence |
| url | https://www.mdpi.com/2306-5338/10/2/36 |
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