Volume 9 • Issue 1 • PP: 08–14 • 2025
Predictive Analysis of Groundwater Resources Using Random Forest Regression
Open Access & Copyright
© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Editorial responsibility:
This article was handled by an independent editor. The author serving on the Editorial Board had no involvement in the peer-review process or the final editorial decision.
View PDF Abstract
The lack of water is one of the most crucial problems of our day; therefore, optimized water resource management and predictions gathered by patrons are of utmost importance. In the turmoil of a country like India, which lives a variety of lifestyles and has a complicated network of rivers, the urgent need for an active point of view to take care of water shortages becomes exceptionally vital. In this study, India’s groundwater, available at the district level for the year 2017, was the area of focus, with this analysis utilizing a dataset of 689 rows, each representing a district, and 16 columns describing the various groundwater extraction and recharge metrics. The study involves five regression models adapting RandomForestRegressor, DecisionTreeRegressor, MLPRegressor, KNeighborsRegressor, and SupportVectorRegression for water resource evaluation and prediction. Every model is appraised by using a thorough metrics set where we incorporate Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Explained Variance Score (EVS), Max Error, Median Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R-squared (R2), among others. Our results put the spotlight on RandomForestRegressor, making MSE measures the same as 0.000206624, endorsing its better performance versus the criteria considered. The approach used in this model provides us with an ensemble effect that makes it more robust in the sense that we can capture the interrelationships within the dataset in a comprehensive way. DecisionTreeRegressor also provides nice options for precision and transparency. The use of such models depicts the potential value of predictive analytics, which has the role of improving resource management and planning because we can all agree that the impending water crisis is also a fact. These research outcomes provide us with important data for well-informed decision-making and strategic management of water reserves through all avenues and most affected areas to air most of the impact of water scarcity.
Keywords
References
[1] E. M. Almetwally and M. A. Meraou, “Application of environmental data with new extension of nadarajah-haghighi distribution,” Computational Journal of Mathe-matical and Statistical Sciences, vol. 1, no. 1, pp. 26–41, 2022.
[2] R. Haggerty, J. Sun, H. Yu, and Y. Li, “Application of machine learning in groundwater quality modeling: A comprehensive review,” Water Research, vol. 233, p. 119745, 2023.
[3] M. Kumar, P. Singh, and P. Singh, “Machine learning and GIS-RS-based algorithms for mapping the ground-water potentiality in the Bundelkhand region, India,” Ecological Informatics, vol. 74, p. 101980, 2023.
[4] K. M. Hamdia, X. Zhuang, and T. Rabczuk, “An ef-ficient optimization approach for designing machine learning models based on genetic algorithm,” Neural Computing and Applications, vol. 33, no. 6, pp. 1923–1933, Mar. 2021.
[5] Y. Ma, R. Han, and W. Wang, “Portfolio optimization with return prediction using deep learning and machine learning,” Expert Systems with Applications, vol. 165, p. 113973, Mar. 2021.
[6] J. Li, X. Du, and J. R. R. A. Martins, “Machine learn-ing in aerodynamic shape optimization,” Progress in Aerospace Sciences, vol. 134, p. 100849, Oct. 2022.
[7] M. A. Yassin, A. G. Usman, S. I. Abba, D. U. Ozsahin, and I. H. Aljundi, “Intelligent learning algorithms inte-grated with feature engineering for sustainable ground-water salinization modelling: Eastern Province of SaudiArabia,” Results in Engineering, vol. 20, p. 101434, 2023.
[8] A. T. M. S. Rahman, T. Hosono, J. M. Quilty, J. Das, and A. Basak, “Multiscale groundwater level forecast-ing: Coupling new machine learning approaches with wavelet transforms,” Advances in Water Resources, vol. 141, p. 103595, 2020.
[9] H. R. Pourghasemi, N. Sadhasivam, S. Yousefi, S. Ta-vangar, H. Ghaffari Nazarlou et al., “Using machine learning algorithms to map the groundwater recharge potential zones,” Journal of Environmental Manage-ment, vol. 265, p. 110525, 2020.
[10] R. Barzegar, S. Razzagh, J. Quilty, J. Adamowski, H. Kheyrollah Pour et al., “Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models,” Journal of Hydrology, vol. 598, p. 126370, 2021.
[11] P. Malakar, S. Sarkar, A. Mukherjee, S. Bhanja, and A. Y. Sun, “Use of machine learning and deep learning methods in groundwater,” in Developments in Earth Surface Processes, Elsevier, ch. 40, pp. 545–557, 2021.
[12] K. A., A. Nair, and G. Singh, “An improved hybrid-coupled model for delineation of groundwater potential zones using surface and climatological factors,” The-oretical and Applied Climatology, vol. 151, no. 3, pp. 2001–2022, 2023.
[13] A. Zahid, M. Q. Hassan, and K. M. U. Ahmed, “Simula-tion of flowpaths and travel time of groundwater through arsenic-contaminated zone in the multi-layered aquifer system of bengal basin,” Environmental Earth Sciences, vol. 73, no. 3, pp. 979–991, 2015.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.