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

Author

Listed:
  • Ahmed Madani

    (Department of Geology, Faculty of Science, Cairo University, Giza, Egypt)

  • Burhan Niyazi

    (Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, 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 m 3 /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.

Suggested Citation

  • Ahmed Madani & Burhan Niyazi, 2023. "Groundwater Potential Mapping Using Remote Sensing and Random Forest Machine Learning Model: A Case Study from Lower Part of Wadi Yalamlam, Western Saudi Arabia," Sustainability, MDPI, vol. 15(3), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2772-:d:1056500
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    References listed on IDEAS

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    1. Seyed Naghibi & Hamid Pourghasemi, 2015. "A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5217-5236, November.
    2. Phong Tung Nguyen & Duong Hai Ha & Abolfazl Jaafari & Huu Duy Nguyen & Tran Van Phong & Nadhir Al-Ansari & Indra Prakash & Hiep Van Le & Binh Thai Pham, 2020. "Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam," IJERPH, MDPI, vol. 17(7), pages 1-20, April.
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