Boo, Kenneth Beng Wee and El-Shafie, Ahmed and Othman, Faridah and Sherif, Mohsen and Ahmed, Ali Najah (2024) Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system. Science of The Total Environment, 912. ISSN 0048-9697, DOI https://doi.org/10.1016/j.scitotenv.2023.168760.
Full text not available from this repository.Abstract
A modeling framework utilizing the coactive neuro-fuzzy inference system (CANFIS) has been developed for multi-lead time groundwater level (GWL) forecasting in four different wells located in Texas and Florida, USA. Various model input combinations, including GWL, precipitation, temperature, and surface water level variables, have been derived based on proposed correlation analysis using singular spectrum analysis (SSA) remainders. The models have been trained on data subsets of varying lengths to identify the optimal training data duration. Additionally, we have introduced the bagging ensemble learning method to enhance the performance of the CANFIS model. As part of a comprehensive model evaluation process, the best-performing CANFIS model for each forecasting scenario has undergone uncertainty analysis using bootstrap sampling. Our results reveal that the CANFIS model performs satisfactorily for daily forecasting but leaves room for improvement in monthly forecasting, particularly for two-month and three-month ahead forecasts. Moreover, we have identified several optimal input combinations, highlighting the significance of the temperature variable in monthly forecasting. Furthermore, our findings indicate that additional training data does not necessarily lead to improved performance. The ensemble CANFIS model has demonstrated significant performance enhancement, particularly for monthly forecasting. Finally, the CANFIS model uncertainty analysis has shown satisfactory results for daily forecasting scenarios, while monthly forecasting models exhibit higher uncertainties, particularly during periods with distinctly different GWL fluctuation patterns.
| Item Type: | Article |
|---|---|
| Funders: | United Arab Emirates University [IF059-2021] |
| Uncontrolled Keywords: | Water table modeling; ANFIS; Bootstrap aggregating (bagging); Uncertainty analysis; Machine learning; Artificial intelligence |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 27 Jun 2024 06:57 |
| Last Modified: | 27 Jun 2024 06:57 |
| URI: | http://eprints.um.edu.my/id/eprint/44257 |
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