Modelling Groundwater Recharge with Multiple Climate Models in Machine Learning Frameworks
|Resource type:||Composite Resource|
|Storage:||The size of this resource is 2.5 MB|
|Created:||Aug 06, 2019 at 9:07 p.m.|
|Last updated:|| Aug 06, 2019 at 9:19 p.m.
|Citation:||See how to cite this resource|
Groundwater supplies 70% of global irrigation water needs; 25% of total freshwater consumption the United States; and a source of safe drinking water to 90% of the United States rural population. Climate models are increasingly being used to simulate the groundwater recharge. However, these climate models often have uncertainty in their recharge predictions. These uncertainties in climate models’ predictions stem from the difference in the models’ structure, the models’ parameters, and the models’ physics. In this study, ten regional climate models (RCMs) are used to model groundwater recharge. The RCMs used in this study were obtained from the North American Regional Climate Change Assessment Program (NARCCAP). In order to combat the uncertainty in the RCMs’ recharge predictions, the predictions are averaged in machine learning frameworks. The machine learning models used in this study include the artificial neural network (ANN), the deep neural networks (DNNs), and the support vector regression (SVR) models. Results suggest that the radial basis function-based SVR model was the superior model in modelling recharge.
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This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|University of Wyoming||The Bishop, Floyd & Wilma Endowment and Paul A. Rechard Fellowship|
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This resource is shared under the Creative Commons Attribution-ShareAlike CC BY-SA.http://creativecommons.org/licenses/by-sa/4.0/
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