Groundwater Potential Mapping Using Remote Sensing and Random Forest Machine Learning Model: A Case Study from Lower Part of Wadi Yalamlam, Western Saudi Arabia

Abstract

Groundwater storage is influenced by many geo-environmental factors. Most of these factors are prepared in the form of categorical data. The present study utilized raster satellite data instead of categorical data and a Random Forest machine learning model to identify groundwater potential zones at the downstream parts of Wadi Yalamlam, western Saudi Arabia. Eighteen groundwater-influenced variables are prepared in continuous raster format from ASTER GDEM, TRMM, and SPOT-5 satellite data. The Random Forest (RF) model is trained using (70%) of the target variable and validated using the rest (30%). The accuracy, sensitivity, and F1-score are all generated to evaluate the model performance. SPOT band 3, band 4, and the rainfall variables are the most important for groundwater potential mapping contributing 11%, 7%, and 8% during the prediction stage. The GDEM elevation variable contributed 6% and the slope variable scored 1%. The main conclusions of the study are: (1) The RF machine learning algorithm successfully identified three groundwater potential zones with an accuracy of 96%. (2) The high, moderate, and low potential groundwater zones covered 11.5%, 59.9%, and 28.6% of the study area respectively. (3) Majority of high and moderate zones lie within the pumping rate range between 10 and 20 m3/day. (4) The approach developed in this study can be applied to any other wadis having the same conditions to help authorities and decision-makers in planning and development projects.