Predictive_model_Assam
| Authors: | |
|---|---|
| Owners: | Bibhash Nath |
| Type: | Resource |
| Storage: | The size of this resource is 14.2 MB |
| Created: | Jan 29, 2022 at 1:57 a.m. |
| Last updated: | Feb 24, 2022 at 4:31 p.m. (Metadata update) |
| Published date: | Feb 03, 2022 at 12:09 a.m. |
| DOI: | 10.4211/hs.d4f4b7601c694667bdf62a7826cad1a6 |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
|---|---|
| Views: | 1336 |
| Downloads: | 139 |
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Abstract
The resources contain grid averaged arsenic concentration (mean concentrations) and predictor variables in Jorhat and Golaghat districts of Assam. GPS location has included error terms for privacy. Basic workflow random forest model in python environment is also provided. Final model was determined through random 10-fold cross-validation. Final model was used in the prediction of arsenic probability in unknown locations. We have also checked spatial cross-validation. The results were found to be consistent and confirmed the overall distribution of high/moderate/low-risk zones for arsenic in groundwater.
Some of the original point data can be downloaded from: https://www.hydroshare.org/resource/bbe23dfacab647568a18dc338114d6d7/
reference: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2017WR022485
Subject Keywords
Coverage
Spatial
Content
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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