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| Type: | Resource | |
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| Created: | Jan 27, 2023 at 2:32 a.m. | |
| Last updated: | Jan 27, 2023 at 1:33 p.m. (Metadata update) | |
| Published date: | Jan 27, 2023 at 1:33 p.m. | |
| DOI: | 10.4211/hs.a85f6240d2c94e2281f4a88c5728323b | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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Abstract
The reservoir operation changes the downstream water level and the surrounding groundwater level. Predicting the groundwater level flux is crucial, especially before making the dam removal decision. However, investigating the condition of dam removal without demolishing the infrastructure is challenging. The novelty of this study comes from analyzing the groundwater level changes using the observed pre- and post-weir removal data. We built daily groundwater level prediction models for 14 groundwater observation wells using five machine learning algorithms. The support vector regression was the best machine learning algorithm in predicting the daily groundwater level. The groundwater level was the highest during normal operation and summer (rainy season) and the lowest during the full opening and winter (dry season). The groundwater changes were up to 3.15 m near the weir, and impacts extended 3.80 km but no further than 7 km. The final product was groundwater level maps that can assist groundwater level management and weir operation strategies based on groundwater level forecasting. Future studies can reconfigure and modify the groundwater prediction process used in this research to fit different hydrological and metrological variables to dams or weirs under consideration for removal.
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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