3066-280XISSN (Online)
Volume 5 , Issue 1 , PP: 26-43, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Asifa Iqbal 1 *
Doi: https://doi.org/10.54216/MOR.050102
Groundwater sources can significantly meet the agricultural, industrial, and domestic demands especially in the arid and semi-arid areas. Nonetheless, ground water has depleted and its quality has declined greatly due to over-pumping, climate fluctuation and ever-growing population pressure. High quality modeling and optimiza-tion techniques that are able to address the complexity and uncertainty of the groundwater system are needed to efficiently manage and provide sustainable use of these resources. In many cases whenever handling nonlin-earity, high dimensionality and multiple competing objectives properties of many groundwater problems, the traditional deterministic or gradient based methods are insufficient. In this respect, metaheuristic optimization algorithms have become an effective tool in groundwater management tasks in general. This paper will show a detailed usage of metaheuristic optimization methods to solve some important problems in ground water mod-eling and management such as well location, optimal pumping rate optimization, ground water contamination, and aquifer parameter estimation. Metaheuristics such as Genetic Algorithms (GA), Particle Swarm Opti-mization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO) have demonstrated their effectiveness in exploring large and complex search spaces and avoiding local optima. These algorithms are combined with computer modeling of groundwater flow and transport (e.g., MODFLOW and MT3DMS) so as to simulate the dynamics of the system and test solutions generated by the algorithms iteratively, and within a feedback environment. The hybridization of metaheuristic methods with surrogate modeling approaches, including artificial neural networks (ANNs) and support vector machines (SVMs), is also explored to reduce computational burdens associated with repeated model evaluations. By integrating optimization algorithms together with data-driven models, the framework produces a tradeoff between the accuracy of the solution and efficiency o f c alculation. I n a ddition, multiple o bjective o ptimization is a pplied i n o rder t o h ave trade-offs between competing objectives e.g. minimizing cost and maximizing aquifer sustainability or minimizing the contaminant spreading and maximizing water delivery. To illustrate the generality and validity of the suggested method, a real-word example of an aquifer system is applied. Findings reveal that metaheuristic approaches are better alternatives to conventional methods regarding the quality of solution, the rate of convergence, and the flexibility to uncertain or incomplete d ata. The framework has the potential of providing the optimized man-agement methods that can help the decision-makers come up with such policies that can be acted upon where the use of groundwater will be sustainable. On balance, the current study informs the current knowledge on intelligent water resources management by ensuring that the power/flexibility of metaheuristic optimization in groundwater context goes into record. The results provide a clear rationale in why synergizing computational intelligence with hydrological science to a groundwater sustainability challenge is important.
Genetic Algorithms , Particle Swarm Optimization , Differential Evolution , Artificial neural networks , Support vector machines
[1] A’kif Al-Fugara, Mohammad Ahmadlou, Rania Shatnawi, Saad AlAyyash, Rida Al-Adamat, Abdel Al-Rahman AlShabeeb, and Sangeeta Soni. Novel hybrid models combining meta-heuristic algorithms with support vector regression (SVR) for groundwater potential mapping. Geocarto International, 37(9):2627–2646, May 2022. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10106049.2020.1831622.
[2] Rana Muhammad Adnan, Hong-Liang Dai, Reham R. Mostafa, Abu Reza Md. Towfiqul Islam, Ozgur Kisi, Salim Heddam, and Mohammad Zounemat-Kermani. Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data. Geocarto International, 38(1):2158951, December 2023. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10106049.2022.2158951.
[3] Ritam Guha, Soulib Ghosh, Kushal Kanti Ghosh, Erik Cuevas, Marco Perez-Cisneros, and Ram Sarkar. Groundwater Flow Algorithm: A Novel Hydro-Geology Based Optimization Algorithm. IEEE Access, 10:132193–132211, 2022.
[4] Seyed Vahid Razavi Termeh, Khabat Khosravi, Majid Sartaj, Saskia Deborah Keesstra, Frank T.-C. Tsai, Roel Dijksma, and Binh Thai Pham. Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping. Hydrogeology Journal, 27(7):2511–2534, November 2019.
[5] Mohammad Mirzavand and Julien Walter. Delineating the mechanisms controlling groundwater salinization using chemo-isotopic data and meta-heuristic clustering algorithms (case study: Saguenay-Lac-Saint-Jean region in the Canadian Shield, Quebec, Canada). Environmental Science and Pollution Research, 31(29):42406–42427, June 2024.
[6] Fatemeh Barzegari Banadkooki and Ali Torabi Haghighi. Groundwater Level Modeling Using Multiobjective Optimization with Hybrid Artificial Intelligence Methods. Environmental Modeling & Assessment, 29(1):45–65, February 2024.
[7] Javed Mallick, Swapan Talukdar, Majed Alsubih, Mohd. Ahmed, Abu Reza Md Towfiqul Islam, Shahfahad, and Nguyen Viet Thanh. Proposing receiver operating characteristic-based sensitivity analysis with introducing swarm optimized ensemble learning algorithms for groundwater potentiality modelling in Asir region, Saudi Arabia. Geocarto International, 37(15):4361–4389, August 2022. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10106049.2021.1878291.
[8] Tang Wen, Wang Tiewang, Alireza Arabameri, Omid Asadi Nalivan, Subodh Chandra Pal, Asish Saha, and Romulus Costache. Land-subsidence susceptibility mapping: assessment of an adaptive neuro-fuzzy inference system–genetic algorithm hybrid model. Geocarto International, 37(26):12194–12218, December 2022. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10106049.2022.2066198.
[9] Sourav Choudhary, Santosh Murlidhar Pingale, and Deepak Khare. Delineation of groundwater potential zones of upper Godavari sub-basin of India using bi-variate, MCDM and advanced machine learning algorithms. Geocarto International, 37(27):15063–15093, December 2022. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10106049.2022.2093992.
[10] Mohammed Falah Allawi, Yasir Al-Ani, Arkan Dhari Jalal, Zainab Malik Ismael, Mohsen Sherif, and Ahmed El-Shafie. Groundwater quality parameters prediction based on data-driven models. Engineering Applications of Computational Fluid Mechanics, 18(1):2364749, December 2024. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/19942060.2024.2364749.
[11] Yawei Zhang, Junpei Chen, Defang Zhang, Xincen Dong, Huimeng Su, Zhipeng Li, and Wenfang Chen. Based on AHP–VW–PSR for groundwater vulnerability assessment and pollution prevention in industrial–agricultural regions: a case study of the Zhongyuan oilfield. Geocarto International, 40(1):2509138, December 2025. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10106049.2025.2509138.
[12] Krishnagopal Halder, Amit Kumar Srivastava, Anitabha Ghosh, Ranajit Nabik, Subrata Pan, Uday Chatterjee, Dipak Bisai, Subodh Chandra Pal, Wenzhi Zeng, Frank Ewert, Thomas Gaiser, Chaitanya Baliram Pande, Abu Reza Md. Towfiqul Islam, Edris Alam, and Md Kamrul Islam. Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India. Environmental Sciences Europe, 36(1):155, September 2024.
[13] Mohamed Haythem Msaddek, Yahya Moumni, Alaeddine Ayari, Moufida El May, and Ismail Chenini. Artificial intelligence modelling framework for mapping groundwater vulnerability of fractured aquifer. Geocarto International, 37(25):10480–10510, December 2022. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10106049.2022.2037729.
[14] Ebenezer K. Siabi, Yihun Taddele Dile, Amos T. Kabo-Bah, Mark Amo-Boateng, Geophery K. Anornu, Komlavi Akpoti, Christopher Vuu, Peter Donkor, Samuel K. Mensah, Awo B. M. Incoom, Emmanuel K. Opoku, and Thomas Atta-Darkwa. Machine learning based groundwater prediction in a data-scarce basin of Ghana. Applied Artificial Intelligence, 36(1):2138130, December 2022. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/08839514.2022.2138130.
[15] Subramani Ravi and Karuppasamy Sudalaimuthu. Delineation of groundwater potential zone by integrating groundwater quality parameters using geospatial techniques and multi-criteria decision analysis – a case study on Chennai coastal watershed, Tamil Nadu, India. Geocarto International, 37(27):16736–16768, December 2022. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10106049.2022.2115152.
[16] Tianfeng Chai and Roland R Draxler. Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature. Geoscientific Model Development, 7(3):1247–1250, 2014. [17] Cort J Willmott and Kenji Matsuura. Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate research, 30(1):79–82, 2005.
[18] Peter Krause, Douglas P Boyle, and Frank Bäse. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5:89–97, 2005.
[19] Marina Sokolova and Guy Lapalme. A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4):427–437, 2009.
[20] Vahid Nourani, Sana Maleki, Hessam Najafi, and Aida Hosseini Baghanam. A fuzzy logic-based approach for groundwater vulnerability assessment. Environmental Science and Pollution Research, 31(12):18010–18029, March 2024.
[21] Md Masroor, Seyed Vahid Razavi-Termeh, Md Hibjur Rahaman, Pandurang Choudhari, Luc Cimusa Kulimushi, and Haroon Sajjad. Adaptive neuro fuzzy inference system (ANFIS) machine learning algorithm for assessing environmental and socio-economic vulnerability to drought: a study in Godavari middle sub-basin, India. Stochastic Environmental Research and Risk Assessment, 37(1):233–259, January 2023.
[22] Tuong Vi Tran, Aaron Peche, Robert Kringel, Katrin Brömme, and Sven Altfelder. Machine Learning-Based Reconstruction and Prediction of Groundwater Time Series in the Allertal, Germany. | EBSCOhost, February 2025. ISSN: 2073-4441 Issue: 3 Pages: 433 Volume: 17.
[23] Fuzieah Subari, Hafni Fatini Harisson, Nor Hazelah Kasmuri, Zalizawati Abdullah, and Suhaiza Hanim Hanipah. An overview of the biological ammonia treatment, model prediction, and control strategies in water and wastewater treatment plant / Fuzieah Subari . . . [et al.]. Malaysian Journal of Chemical Engineering and Technology (MJCET), 5(1):8–28, April 2022. Number: 1 Publisher: Universiti Teknologi MARA.
[24] Diriba Worku Doyo, Sirak Tekleab Gebrekristos, Ayalew Shura Kasa, and Samuel Dagalo Hatiye. Groundwater prospective mapping using remote sensing and GIS techniques: the case of Meki watershed in Central Rift Valley, Ethiopia. Sustainable Water Resources Management, 8(6):184, October 2022.
[25] Md Moniruzzaman Monir, Subaran Chandra Sarker, Rathindra Nath Biswas, and Md Nazrul Islam. Assessment of the environmental impacts of regional groundwater flow path fluctuations in the water-stressed drought prone Northern Region of Bangladesh. Scientific Reports, 15(1):4861, February 2025. Publisher: Nature Publishing Group.
[26] M. M. Gobashy, A. M. Metwally, M. Abdelazeem, K. S. Soliman, and A. Abdelhalim. Geophysical Exploration of Shallow Groundwater Aquifers in Arid Regions: A Case Study of Siwa Oasis, Egypt. Natural Resources Research, 30(5):3355–3384, October 2021.
[27] Hamid Vahdat-Aboueshagh, Frank T.-C. Tsai, Dependra Bhatta, and Krishna P. Paudel. Irrigation-Intensive Groundwater Modeling of Complex Aquifer Systems Through Integration of Big Geological Data. Frontiers in Water, 3, April 2021. Publisher: Frontiers.
[28] Caroline Rosello, Sondoss Elsawah, Joseph Guillaume, and Anthony Jakeman. A Century of Evolution of Modeling for River Basin Planning to the Next Generation of Models, Methods, and Concepts. In Oxford Research Encyclopedia of Environmental Science. December 2022.
[29] Jessica Bullock and Veera Gnaneswar Gude. Economics of brine desalination for communities near the Salton Sea Geothermal Field, California, USA. Environmental Earth Sciences, 82(1):3, December 2022.
[30] Babak Ghazi, Esmaeil Jeihouni, Ozgur Kisi, Quoc Bao Pham, and Bojan Ðurin. Estimation of Tasuj aquifer response to main meteorological parameter variations under Shared Socioeconomic Pathways scenarios. Theoretical and Applied Climatology, 149(1):25–37, July 2022.
[31] A machine learning approach to dental fluorosis classification | Arabian Journal of Geosciences.
[32] Aysegul Demir Yetis, Mehmet Irfan Yesilnacar, and Musa Atas. A machine learning approach to dental fluorosis classification. Arabian Journal of Geosciences, 14(2):95, January 2021.
[33] Vijaya Bathini and K. Usha Rani. A Review of Analyzing Different Agricultural Crop Yields Using Artificial Intelligence. | EBSCOhost, January 2025. ISSN: 2158-107X Issue: 1 Pages: 1278 Volume: 16.
[34] Anurag Satpathi, Abhishek Danodia, Ajeet Singh Nain, Makrand Dhyani, Dinesh Kumar Vishwakarma, Ahmed Z. Dewidar, and Mohamed A. Mattar. Estimation of crop evapotranspiration using statistical and machine learning techniques with limited meteorological data: a case study in Udham Singh Nagar, India. Theoretical and Applied Climatology, 155(6):5279–5296, June 2024.
[35] Muhammad Fulki Fadhillah, Wahyu Luqmanul Hakim, Sung-Jae Park, and Chang-Wook Lee. Integrating SAR and Optical Imagery Analysis for Liquefaction Phenomenon Identification of Post-Pohang Earthquake 2017, South Korea, Utilizing a Hybrid Deep-Learning Approach. IEEE Transactions on Geoscience and Remote Sensing, 63:1– 14, 2025.
[36] On the Development of State-of-the-Art Computational Decision Support Systems for Efficient Water Quality Management: Prospects and Opportunities in a Climate Changing World - Festus Oluwadare Fameso, Julius Musyoka Ndambuki, Williams Kehinde Kupolati, Jacques Snyman, 2024.
[37] Festus Oluwadare Fameso, Julius Musyoka Ndambuki, Williams Kehinde Kupolati, and Jacques Snyman. On the Development of State-of-the-Art Computational Decision Support Systems for Efficient Water Quality Management: Prospects and Opportunities in a Climate Changing World. Air, Soil and Water Research, 17:11786221241259949, May 2024. Publisher: SAGE Publications Ltd STM.
