Predictive Modeling of Groundwater Recharge under Climate Change Scenarios in the Northern Area of Saudi Arabia


  • Rabie A. Ramadan Computer Engineering Department, College of Computer Science and Engineering, Hail University, Saudi Arabia | Computer Engineering Department, Faculty of Engineering, Cairo University, Egypt
  • Sahbi Boubaker Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
Volume: 14 | Issue: 2 | Pages: 13578-13583 | April 2024 |


Water scarcity is considered a major problem in dry regions, such as the northern areas of Saudi Arabia and especially the city of Hail. Water resources in this region come mainly from groundwater aquifers, which are currently suffering from high demand and severe climatic conditions. Forecasting water consumption as accurately as possible may contribute to a high level of sustainability of water resources. This study investigated different Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Linear Regression (LR), and Gradient Boosting (GB), to efficiently predict water consumption in such areas. These models were evaluated using a set of performance measures, including Mean Squared Error (MSE), R-squared (R2), Mean Absolute Error (MAE), Explained Variance Score (EVS), Mean Absolute Percentage Error (MAPE), and Median Absolute Error (MedAE). Two datasets, water consumption and weather data, were collected from different sources to examine the performance of the ML algorithms. The novelty of this study lies in the integration of both weather and water consumption data. After examining the most effective features, the two datasets were merged and the proposed algorithms were applied. The RF algorithm outperformed the other models, indicating its robustness in capturing water usage behavior in dry areas such as Hail City. The results of this study can be used by local authorities in decision-making, water consumption analysis, new project construction, and consumer behavior regarding water usage habits in the region.


groundwater, recharge, Hail city, machine learning, prediction


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A. I. Almulhim and I. R. Abubakar, "Developing a sustainable water conservation strategy for Saudi Arabian cities," Groundwater for Sustainable Development, vol. 23, Nov. 2023, Art. no. 101040.

"Saudi Arabia Hourly Climate Integrated Surface Data."

"Per Capita Water Consumption In Saudi Regions."

D. Fiorillo, Z. Kapelan, M. Xenochristou, F. De Paola, and M. Giugni, "Assessing the Impact of Climate Change on Future Water Demand using Weather Data," Water Resources Management, vol. 35, no. 5, pp. 1449–1462, Mar. 2021.

H. Tao et al., "Groundwater level prediction using machine learning models: A comprehensive review," Neurocomputing, vol. 489, pp. 271–308, Jun. 2022.

A. Boudhaouia and P. Wira, "A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning," Forecasting, vol. 3, no. 4, pp. 682–694, Dec. 2021.

K. Smolak, B. Kasieczka, W. Fialkiewicz, W. Rohm, K. Siła-Nowicka, and K. Kopańczyk, "Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models," Urban Water Journal, vol. 17, no. 1, pp. 32–42, Jan. 2020.

S. Wei, T. Xu, G. Y. Niu, and R. Zeng, Estimating Irrigation Water Consumption Using Machine Learning and Remote Sensing Data in Kansas High Plains," Remote Sensing, vol. 14, no. 13, Jan. 2022, Art. no. 3004.

F. B. Banadkooki et al., "Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm," Natural Resources Research, vol. 29, no. 5, pp. 3233–3252, Oct. 2020.

H. Cai, H. Shi, S. Liu, and V. Babovic, "Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States," Journal of Hydrology: Regional Studies, vol. 37, Oct. 2021, Art. no. 100930.

D. Kumar, T. Roshni, A. Singh, M. K. Jha, and P. Samui, "Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study," Earth Science Informatics, vol. 13, no. 4, pp. 1237–1250, Dec. 2020.

H. Kardan Moghaddam, S. Ghordoyee Milan, Z. Kayhomayoon, Z. Rahimzadeh kivi, and N. Arya Azar, "The prediction of aquifer groundwater level based on spatial clustering approach using machine learning," Environmental Monitoring and Assessment, vol. 193, no. 4, Mar. 2021, Art. no. 173.

H. A. Afan et al., "Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques," Engineering Applications of Computational Fluid Mechanics, vol. 15, no. 1, pp. 1420–1439, Jan. 2021.

A. T. M. S. Rahman, T. Hosono, J. M. Quilty, J. Das, and A. Basak, "Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms," Advances in Water Resources, vol. 141, Jul. 2020, Art. no. 103595.

M. Sapitang, W. M. Ridwan, A. N. Ahmed, C. M. Fai, and A. El-Shafie, "Groundwater level as an input to monthly predicting of water level using various machine learning algorithms," Earth Science Informatics, vol. 14, no. 3, pp. 1269–1283, Sep. 2021.

W. Liu, H. Yu, L. Yang, Z. Yin, M. Zhu, and X. Wen, "Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas," Water, vol. 13, no. 18, 2021.

W. Li, M. M. Finsa, K. B. Laskey, P. Houser, and R. Douglas-Bate, "Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions," Water, vol. 15, no. 19, Jan. 2023, Art. no. 3473.

F. Mlawa, E. Mkoba, and N. Mduma, "A Machine Learning Model for detecting Covid-19 Misinformation in Swahili Language," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10856–10860, Jun. 2023.

M. Sipper and J. H. Moore, "AddGBoost: A gradient boosting-style algorithm based on strong learners," Machine Learning with Applications, vol. 7, Mar. 2022, Art. no. 100243.

K. Theofilatos, S. Likothanassis, and A. Karathanasopoulos, "Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques," Engineering, Technology & Applied Science Research, vol. 2, no. 5, pp. 269–272, Oct. 2012.

S. Benítez-Peña, R. Blanquero, E. Carrizosa, and P. Ramírez-Cobo, "Cost-sensitive probabilistic predictions for support vector machines," European Journal of Operational Research, vol. 314, no. 1, pp. 268–279, Apr. 2024.

R. G. Siqueira et al., "Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica," CATENA, vol. 235, Feb. 2024, Art. no. 107677.

X. C. Nguyen et al., "Estimating ammonium changes in pilot and full-scale constructed wetlands using kinetic model, linear regression, and machine learning," Science of The Total Environment, vol. 907, Jan. 2024, Art. no. 168142.

S. Mondal, S. Ghosh, and A. Nag, "Brain stroke prediction model based on boosting and stacking ensemble approach," International Journal of Information Technology, vol. 16, no. 1, pp. 437–446, Jan. 2024.


How to Cite

R. A. Ramadan and S. Boubaker, “Predictive Modeling of Groundwater Recharge under Climate Change Scenarios in the Northern Area of Saudi Arabia”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13578–13583, Apr. 2024.


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