Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh

Md Mahabub Rahman; Md Emran Biswas

doi:10.31127/tuje.1808553

Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh

Abstract

Accurate prediction of groundwater levels is a key concern for environmental monitoring and sustainable water resources management. Inspired by a Random Forest (RF) model trained with three popular hyperparameter optimizations—Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Genetic Algorithm (GA)—this research proposes a new method for flattening groundwater level forecasting. The study dataset includes groundwater table (GWT) information and two independent variables, latitude and longitude, collected as a single-time measurement from 335 observation wells in Bangladesh’s Bogura district. The performance of models was measured using some metric form, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R2, and Mean Absolute Error (MAE). The PSO-based optimization (PSO-RF) was more effective than GA and SA, observing the prediction ability, as reported in the study. The PSO-RF model demonstrated better generalization on the test data by achieving a Test MSE of 0.42561, a Test RMSE of 0.65239, a Test R² of 0.84093, and a Test MAE of 0.49997, which were lower than those from all used models. This paper demonstrates the significance of hyperparameter optimization in enhancing the performance of machine learning models for environmental predictions. 2D and 3D spatial GWT distribution maps were carried out with the derived data set, consequently established by ArcGIS software. The proposed PSO-RF model is promising for the prediction of groundwater levels in Bangladesh. It can be further extended to other regions for similar applications in environmental problem prediction and water resource management

Keywords

References

Konikow, L. F., & Kendy, E. (2005). Groundwater depletion: A global problem. Hydrogeology journal, 13(1), 317-320. Erişim adresi: https://www.usgs.gov/publications/groundwater-depletion-a-global-problem
Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O., & Lee, K. K. (2011). A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology, 396(1-2), 128-138. Erişim adresi: https://www.sciencedirect.com/science/article/abs/pii/S0022169410006761
Ranjbar, A., & Mahjouri, N. (2020). Multi-objective freshwater management in coastal aquifers under uncertainty in hydraulic parameters. Natural Resources Research, 29(4), 2347-2368. Erişim adresi: https://inis.iaea.org/records/hjcfx-arg56
Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28-40. Erişim adresi: https://www.sciencedirect.com/science/article/abs/pii/S0022169411004100
Koch, J., Berger, H., Henriksen, H. J., & Sonnenborg, T. O. (2019). Modelling of the shallow water table at high spatial resolution using random forests. Hydrology and Earth System Sciences, 23(11), 4603-4619. Erişim adresi: https://hess.copernicus.org/articles/23/4603/2019/hess-23-4603-2019.html
Rahman, A. S., Hosono, T., Quilty, J. M., Das, J., & Basak, A. (2020). Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms. Advances in Water Resources, 141, 103595. Erişim adresi: https://www.sciencedirect.com/science/article/abs/pii/S0309170819312618
Prinos, S. T., Lietz, A. C., & Irvin, R. B. (2002). Design of a real-time ground-water level monitoring network and portrayal of hydrologic data in southern Florida (No. 2001-4275). US Geological Survey. Erişim adresi: https://pubs.usgs.gov/publication/wri20014275#main-content
Barzegar, R., Fijani, E., Moghaddam, A. A., & Tziritis, E. (2017). Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. Science of the Total Environment, 599, 20-31. Erişim adresi: https://www.sciencedirect.com/science/article/abs/pii/S0048969717310343

Lee, S., Lee, K. K., & Yoon, H. (2019). Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors. Hydrogeology Journal, 27(2), 567-579. Erişim adresi: https://link.springer.com/article/10.1007/s10040-018-1866-3
Roy, D. K., Biswas, S. K., Saha, K. K., & Murad, K. F. I. (2021). Groundwater level forecast via a discrete space-state modelling approach as a surrogate to complex groundwater simulation modelling. Water Resources Management, 35(6), 1653-1672. Erişim adresi: https://link.springer.com/article/10.1007/s11269-021-02787-6
Zhao, W. G., Wang, H., & Wang, Z. J. (2010). Groundwater level forecasting based on support vector machine. Appl. Mech. Mater, 44, 1365–1369. Erişim adresi: https://www.scientific.net/AMM.44-47.1365
Hsu, K. L., Gupta, H. V., & Sorooshian, S. (1995). Artificial neural network modeling of the rainfall‐runoff process. Water resources research, 31(10), 2517-2530. Erişim adresi: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/95WR01955
Nourani, V., Baghanam, A. H., Adamowski, J., & Kisi, O. (2014). Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, 358-377. Erişim adresi: https://www.sciencedirect.com/science/article/abs/pii/S002216941400242X
Kumar, D., Roshni, T., Singh, A., Jha, M. K., & Samui, P. (2020). Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study. Earth Science Informatics, 13(4), 1237-1250. Erişim adresi: https://link.springer.com/article/10.1007/s12145-020-00508-y
Panahi, M., Sadhasivam, N., Pourghasemi, H. R., Rezaie, F., & Lee, S. (2020). Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). Journal of Hydrology, 588, 125033. Erişim adresi: sciencedirect.com/science/article/pii/S0022169420304935
Sesugh, T., Onyia, M., & Fidelis, O. (2024). Predicting the compressive strength of self-compacting concrete using artificial intelligence techniques: A review. Turkish Journal of Engineering, 8(3), 537-550. Erişim adresi: https://dergipark.org.tr/en/pub/tuje/article/1422225
Erdem, F. (2022). Risk assessment with the fuzzy logic method for Ankara OIZ environmental waste water treatment plant. Turkish Journal of Engineering, 6(4), 268 275. Erişim adresi: https://dergipark.org.tr/en/pub/tuje/article/975623
Rahman, M. M., Hossain, M. B., & Sayed, A. (2025). Prediction of soil liquefaction using machine learning approaches. Engineering Transactions, 73(2), 1-22. Erişim adresi: https://et.ippt.pan.pl/index.php/et/article/view/3360
Sayed, A., & Rahman, M. M. (2025). M5 soft computing techniques for assessment of soil liquefaction. Journal of Rehabilitation in Civil Engineering, 13(3), 199-214. Erişim adresi: https://civiljournal.semnan.ac.ir/article_9317.html
Rahman, M. M., & Sayed, M. A. (2025). Application of cross validation approaches to examine soft computing systems for forecasting soil liquefaction. Big Data and Earth System, 1(3), 100038. Erişim adresi: https://www.sciencedirect.com/science/article/pii/S3050740525000388
Mogaraju, J. K. (2023). Application of machine learning algorithms in the investigation of groundwater quality parameters over YSR district, India. Turkish Journal of Engineering, 7(1), 64-72. Erişim adresi: https://dergipark.org.tr/en/pub/tuje/article/1032314
Demirtop, A., & Sevli, O. (2024). Wind speed prediction using LSTM and ARIMA time series analysis models: A case study of Gelibolu. Turkish Journal of Engineering, 8(3), 524-536. Erişim adresi: https://dergipark.org.tr/en/pub/tuje/article/1431629
Durap, A. (2025). Machine learning-based wind speed prediction using random forest: a cross validated analysis for renewable energy applications. Turkish Journal of Engineering, 9(3), 508-518. Erişim adresi: https://dergipark.org.tr/en/pub/tuje/article/1624354
Uzer, A. U. (2024). Efficient prediction of compressive strength in geotechnical engineering using artificial neural networks. Turkish Journal of Engineering, 8(3), 457-468. Erişim adresi: https://dergipark.org.tr/en/pub/tuje/article/1415931
Othman, M. M. (2023). Modeling of daily groundwater level using deep learning neural networks. Turkish Journal of Engineering, 7(4), 331-337. Erişim adresi: https://dergipark.org.tr/en/pub/tuje/article/1169908
Ramasamy, V., Alotaibi, Y., Khalaf, O. I., Samui, P., & Jayabalan, J. (2022). Prediction of groundwater table for Chennai Region using soft computing techniques. Arabian Journal of Geosciences, 15(9), 827. Erişim adresi: https://link.springer.com/article/10.1007/s12517-022-09851-4
Gaffoor, Z., Pietersen, K., Jovanovic, N., Bagula, A., Kanyerere, T., Ajayi, O., & Wanangwa, G. (2022). A comparison of ensemble and deep learning algorithms to model groundwater levels in a data-scarce aquifer of Southern Africa. Hydrology, 9(7), 125. Erişim adresi: https://www.mdpi.com/2306-5338/9/7/125
Igwebuike, N., Ajayi, M., Okolie, C., Kanyerere, T., & Halihan, T. (2025). Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa. Earth Science Informatics, 18(1), 6. Erişim adresi: https://link.springer.com/article/10.1007/s12145-024-01623-w
Wei, A., Chen, Y., Li, D., Zhang, X., Wu, T., & Li, H. (2022). Prediction of groundwater level using the hybrid model combining wavelet transform and machine learning algorithms. Earth Science Informatics, 15(3), 1951-1962. Erişim adresi: https://link.springer.com/article/10.1007/s12145-022-00853-0
Nourani, V., Ejlali, R. G., & Alami, M. T. (2011). Spatiotemporal groundwater level forecasting in coastal aquifers by hybrid artificial neural network-geostatistics model: a case study. Environmental Engineering Science, 28(3), 217-228. Erişim adresi: https://journals.sagepub.com/doi/abs/10.1089/ees.2010.0174
Sahoo, S., Russo, T. A., Elliott, J., & Foster, I. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US. Water Resources Research, 53(5), 3878-3895. Erişim adresi: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016WR019933
Kalu, I., Ndehedehe, C. E., Okwuashi, O., Eyoh, A. E., & Ferreira, V. G. (2022). A new modelling framework to assess changes in groundwater level. Journal of Hydrology: Regional Studies, 43, 101185. Erişim adresi: https://www.sciencedirect.com/science/article/pii/S2214581822001987
Kayhomayoon, Z., Babaeian, F., Ghordoyee Milan, S., Arya Azar, N., & Berndtsson, R. (2022). A combination of metaheuristic optimization algorithms and machine learning methods improves the prediction of groundwater level. Water, 14(5), 751. Erişim adresi: https://www.mdpi.com/2073-4441/14/5/751
Biswas, M. E., Hossain, M. D., Huh, E. N., & Sultana, T. (2025). Spatiotemporal Data-Driven Hourly Bike-Sharing Demand Prediction Using ApexBoost Regression. International Journal of Data Science and Analytics, 1-24. Erişim adresi: https://link.springer.com/article/10.1007/s41060-025-00820-0
Tian, D., Lang, Z. Q., Zhang, D., & Anumba, D. O. (2023). A filter-predictor polynomial feature based machine learning approach to predicting preterm birth from cervical electrical impedance spectroscopy. Biomedical Signal Processing and Control, 80, 104345. Erişim adresi: https://www.sciencedirect.com/science/article/abs/pii/S1746809422007996
Liberti, L., & Lavor, C. (2017). Euclidean distance geometry. An Introduction. Erişim adresi: https://link.springer.com/book/10.1007/978-3-319-60792-4
Young, C. Y. (2017). Algebra and trigonometry. John Wiley & Sons. Erişim adresi: https://www.softouch.on.ca/kb/data/Algebra%20and%20Trigonometry%204E.pdf
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178-210. Erişim adresi: https://www.sciencedirect.com/science/article/abs/pii/S0020025522014633
Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). Pearson correlation coefficient. In Noise reduction in speech processing (pp. 1-4). Berlin, Heidelberg: Springer Berlin Heidelberg. Erişim adresi: https://link.springer.com/chapter/10.1007/978-3-642-00296-0_5
Biswas, M. E., Sultana, T., Mandal, A. K., Golam Morshed, M., & Hossain, M. D. (2024). Spatio-temporal feature engineering and selection-based flight arrival delay prediction using deep feedforward regression network. Electronics, 13(24), 4910. Erişim adresi: https://www.mdpi.com/2079-9292/13/24/4910
Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). Erişim adresi: https://dl.acm.org/doi/abs/10.1145/2939672.2939785
Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21. Erişim adresi: https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2013.00021/full?utm_source=pocket_shared
Rigatti, S. J. (2017). Random forest. Journal of insurance medicine, 47(1), 31-39. Erişim adresi: https://jim.kglmeridian.com/view/journals/insm/47/1/article-p31.xml
Sutton, C. D. (2005). Classification and regression trees, bagging, and boosting. Handbook of statistics, 24, 303-329. Erişim adresi: https://www.sciencedirect.com/science/chapter/handbook/abs/pii/S0169716104240111
Schapire, R. E. (2013). Explaining adaboost. In Empirical inference: festschrift in honor of vladimir N. Vapnik (pp. 37-52). Berlin, Heidelberg: Springer Berlin Heidelberg. Erişim adresi: https://link.springer.com/chapter/10.1007/978-3-642-41136-6_5
Marini, F., & Walczak, B. (2015). Particle swarm optimization (PSO). A tutorial. Chemometrics and intelligent laboratory systems, 149, 153-165. Erişim adresi: https://www.sciencedirect.com/science/article/abs/pii/S0169743915002117
Coello, C. A. (2000). An updated survey of GA-based multiobjective optimization techniques. ACM Computing Surveys (CSUR), 32(2), 109-143. Erişim adresi: https://dl.acm.org/doi/abs/10.1145/358923.358929
Chopard, B., & Tomassini, M. (2026). Simulated annealing. In An introduction to metaheuristics for optimization (pp. 81-106). Singapore: Springer Nature Singapore. Erişim adresi: https://link.springer.com/chapter/10.1007/978-981-95-6216-9_5
Hodson, T. O. (2022). Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development Discussions, 2022, 1-10. Erişim adresi: https://gmd.copernicus.org/articles/15/5481/2022/gmd-15-5481-2022-discussion.html
Köksoy, O. (2006). Multiresponse robust design: Mean square error (MSE) criterion. Applied Mathematics and Computation, 175(2), 1716-1729. Erişim adresi: https://www.sciencedirect.com/science/article/abs/pii/S0096300305007332
Durap, A. (2024). Data-driven models for significant wave height forecasting: Comparative analysis of machine learning techniques. Results in Engineering, 24, 103573. Erişim adresi: https://www.sciencedirect.com/science/article/pii/S2590123024018164
Durap, A. (2025). Beachface steepness modulates erosion but not recovery: Multi-decadal spatiotemporal shoreline evidence across 390 transects. Journal of Sea Research, 102644. Erişim adresi: https://www.sciencedirect.com/science/article/pii/S1385110125000838

Details

Primary Language

English

Subjects

Water Resources Engineering

Journal Section

Research Article

Authors

Md Mahabub Rahman ^*
0000-0003-1580-483X
Bangladesh

Md Emran Biswas
0009-0007-9331-7531
Bangladesh

Publication Date

July 6, 2026

Submission Date

October 22, 2025

Acceptance Date

February 17, 2026

Published in Issue

Year 2026 Volume: 10 Number: 3

DOI

https://doi.org/10.31127/tuje.1808553

IZ

https://izlik.org/JA27TF87TF

Cite

RIS / Bibtex

APA

Rahman, M. M., & Biswas, M. E. (2026). Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh. Turkish Journal of Engineering, 10(3), 936-947. https://doi.org/10.31127/tuje.1808553

AMA

1.Rahman MM, Biswas ME. Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh. TUJE. 2026;10(3):936-947. doi:10.31127/tuje.1808553

Chicago

Rahman, Md Mahabub, and Md Emran Biswas. 2026. “Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh”. Turkish Journal of Engineering 10 (3): 936-47. https://doi.org/10.31127/tuje.1808553.

EndNote

Rahman MM, Biswas ME (July 1, 2026) Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh. Turkish Journal of Engineering 10 3 936–947.

IEEE

[1]M. M. Rahman and M. E. Biswas, “Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh”, TUJE, vol. 10, no. 3, pp. 936–947, July 2026, doi: 10.31127/tuje.1808553.

ISNAD

Rahman, Md Mahabub - Biswas, Md Emran. “Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh”. Turkish Journal of Engineering 10/3 (July 1, 2026): 936-947. https://doi.org/10.31127/tuje.1808553.

JAMA

1.Rahman MM, Biswas ME. Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh. TUJE. 2026;10:936–947.

MLA

Rahman, Md Mahabub, and Md Emran Biswas. “Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh”. Turkish Journal of Engineering, vol. 10, no. 3, July 2026, pp. 936-47, doi:10.31127/tuje.1808553.

Vancouver

1.Md Mahabub Rahman, Md Emran Biswas. Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh. TUJE. 2026 Jul. 1;10(3):936-47. doi:10.31127/tuje.1808553