Record Details
Comparative evaluation of machine learning models for groundwater quality assessment
University of Nebraska - Lincoln Digital Commons
| Field | Value |
|---|---|
| Title | Comparative evaluation of machine learning models for groundwater quality assessment |
| Creator | Bedi, Shine Samal, Ashok Ray, Chittaranjan Snow, Daniel D. |
| Description | Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset... |
| Date | 2020-01-01T08:00:00Z |
| Type | text |
| Format | application/pdf |
| Identifier | https://digitalcommons.unl.edu/csearticles/268 https://digitalcommons.unl.edu/context/csearticles/article/1303/viewcontent/Bedi_EMA_2020_Comparative_evaluation__MS_FINAL.pdf |
| Source | CSE Journal Articles |
| Publisher | DigitalCommons@University of Nebraska - Lincoln |
| Subject | Artificial neural networks (ANN) Support vector machines (SVM) XGBoost Data imbalance Feature importance Groundwater quality Computer Sciences Environmental Indicators and Impact Assessment Environmental Monitoring Environmental Sciences Water Resource Management |
