Hi, I'm an error. x

Modelling Groundwater Recharge with Multiple Climate Models in Machine Learning Frameworks


Authors:
Owners:
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
Sharing Status: Public
Views: 96
Downloads: 12
+1 Votes: Be the first one to  +1 this.  (You need to be logged in to rate this.)
Comments: No comments (yet)

Abstract

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.

Subject Keywords

  • No subject keywords have been added.
  • ${ k }

Duplicate. Keyword not added.

Error: ${ error }
Deleting all keywords will set the resource sharing status to private.

Resource Level Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
M
North Latitude
49.3725°
East Longitude
-89.8473°
South Latitude
43.1861°
West Longitude
-98.1090°

Temporal

Start Date:
End Date:

Content

Credits

Funding Agencies

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

How to Cite

A., K. (2019). Modelling Groundwater Recharge with Multiple Climate Models in Machine Learning Frameworks, HydroShare, http://www.hydroshare.org/resource/a29126de8c7544cf9ef98b6f608dce32

This resource is shared under the Creative Commons Attribution-ShareAlike CC BY-SA.

 http://creativecommons.org/licenses/by-sa/4.0/
CC-BY-SA

Comments

There are currently no comments

New Comment

required