Li, W.; Finsa, M.M.; Laskey, K.B.; Houser, P.; Douglas-Bate, R. Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions. Water2023, 15, 3473.
Li, W.; Finsa, M.M.; Laskey, K.B.; Houser, P.; Douglas-Bate, R. Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions. Water 2023, 15, 3473.
Li, W.; Finsa, M.M.; Laskey, K.B.; Houser, P.; Douglas-Bate, R. Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions. Water2023, 15, 3473.
Li, W.; Finsa, M.M.; Laskey, K.B.; Houser, P.; Douglas-Bate, R. Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions. Water 2023, 15, 3473.
Abstract
In water scarcity regions, using data-driven approaches to predict groundwater level is challenging due to limited data availability. However, these regions have substantial water needs and require cost-effective groundwater utilization strategies. In this study, we use artificial intelligence to predict groundwater levels to provide guidance for drilling shallow boreholes for subsistence irrigation. The Bilate watershed, which is located in southern Ethiopia, was selected as the study area. This is typical of areas in Africa with high demand for water and limited availability of well data. Using a non-time-series database of 75 boreholes, machine learning models including multiple linear regression, multivariate adaptive regression spline, artificial neural networks, random forest regression, and gradient boosting regression (GBR) were constructed to predict the depth to the water table. 20 independent variables were considered in the models. GBR performed the best of the approaches with an average 0.77 R-squared value on testing data. Finally, a map of predicted water levels in the Bilate watershed was created based on the best model with water levels ranging from 1.6 to 245.9 meters. With the limited set of borehole data, the results show a clear signal that can provide guidance for borehole drilling decisions for sustainable irrigation with additional implications for drinking water.
Environmental and Earth Sciences, Water Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
