Tan, Woon Yang and Lai, Sai Hin and Teo, Fang Yenn and El-Shafie, Ahmed (2022) State-of-the-art development of two-waves artificial intelligence modeling techniques for river streamflow forecasting. Archives Of Computational Methods In Engineering, 29 (7). pp. 5185-5211. ISSN 1134-3060, DOI https://doi.org/10.1007/s11831-022-09763-2.
Full text not available from this repository.Abstract
Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This paper is to review the recent development of the artificial intelligence (AI) methods for river streamflow forecasting over the last two decades. The original establishments of forecasting model are derived from the neural network theory, supervised machine learning and fuzzy logic theory. The first wave of development comes with evolutional theory of optimization algorithm and the probabilistic theory. The distinction of AI model is the ability to process complex data that are non-linear, non-stationary and stochastic without prior physical knowledge. However, due to the limitations of the AI model, issues arise in the form of over-fitting, trap in local minima and slow learning process; and subsequently limit the possibility of gaining higher accuracy. This has prompted researchers to try numerous new approaches to increase the efficiency of data processing. The second wave comes with the hybrid models, modular techniques and ensemble models which has revolutionize into contemporary streamflow forecasting. With the latest hybrid models, results have shown retrieval of additional information are made possible that lead to better conclusion. This paper has suggested the research gaps and some future recommendations. It will be useful for new researcher and existing one to identify and be acquainted with the trends of different techniques of artificial intelligence.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Autoregressive moving average; Support vector machine; Rainfall time-series; Neural-network |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 28 Aug 2023 07:48 |
| Last Modified: | 28 Aug 2023 07:48 |
| URI: | http://eprints.um.edu.my/id/eprint/40971 |
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