Assessment of Artificial Intelligence and Remote Sensing-Based groundwater storage management workflow
DOI:
https://doi.org/10.60923/issn.2281-4485/23733Keywords:
Groundwater management, Artificial intelligence, Remote Sensing, Hydrology, Data Science processes, machine learning, Workflow, Explainability, XAIAbstract
Groundwater is a vital freshwater reserve that is increasingly threatened by climate change and mounting anthropogenic pressure on global water resources. While arti- ficial intelligence (AI) has shown promise in groundwater monitoring in conjunction with remote sensing (RS), its integration with traditional technology remains bound to outdated hydrological assumptions, limiting the adaptability of the integrated approach across diverse regions and conditions. Here, we develop a hydrology- independent workflow using an explainable AI framework based on satellite obser- vations to monitor and forecast groundwater storage dynamics. Tested in a case study on a dataset of Morocco, our approach measured groundwater storage variations with high accuracy, without relying on conventional drivers such as precipitation. Model performance revealed robust spatiotemporal scalability and interpretabil- ity, promising broader applications across data-scarce environments. These findings demonstrate that moving away from classical hydrological dependencies and inte- grating AI-based frameworks can enhance the flexibility of groundwater modeling, offering a pathway toward more adaptive and scalable groundwater management, especially under climate uncertainty.
References
ABBAS M., ARSHAD M., SHAHID M.A. (2025) Spatio- temporal zoning of groundwater quality in rechna doab: analysis of electrical conductivity (ec), sodium adsorption ratio (sar), and residual sodium carbonate (rsc) variability. EQA International Journal of Environmental Quality, 68:37–49. https://doi.org/10.6092/issn.2281-4485/21146
ABDELKAREEM M., ABDALLA F., ALSHEHRI F., PANDE C.B. (2023) Mapping groundwater prospective zo-nes using remote sensing and geographical information system techniques in wadi fatima, western saudi arabia. Sustainability, 15(21):15629. https://doi.org/10.3390/su152115629
AHMED M., AQNOUY M., EL MESSARI J.S. (2021) Sustain- ability of Morocco’s groundwater resources in response to natural and anthropogenic forces. Journal of Hydrol ogy, 603:126866. https://doi.org/10.1016/j.jhydrol.2021.126866
ALEX GOLDSTEIN J.B., ADAM KAPELNER A., PITKIN E. (2015) Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expec- tation. Journal of Computational and Graphical Statistics, 24(1):44–65. https://doi.org/10.1080/10618600.2014.907095
ALSHEHRI F., RAHMAN A. (2023) Coupling machine and deep learning with explainable artificial intelligence for improving prediction of groundwater quality and decision-making in arid region, Saudi Arabia. Water, 15(12):2298. https://doi.org/10.3390/w15122298
ALSHEHRI F., SULTAN M., KARKI S., ALWAGDANI E., ALSEFRY S., ALHARBI H., STURCHIO N. (2020) Mapping the distribution of shallow groundwater occurrences using remote sensing-based statistical modeling over south- west saudi arabia. Remote Sensing, 12(9):1361. https://doi.org/10.3390/rs12091361
AMAZIRH A., OUASSANOUAN Y., BOUIMOUASS H., BABA M.W., BOURAS E.H., RAFIK,A., CHEHBOUNI A. (2024) Remote sensing-based multiscale analysis of total and groundwater storage dynamics over semi-arid north african basins. Remote Sensing, 16(19): 3698. https://doi.org/10.3390/rs16193698
BAI T., WANG X.S., HAN P.F. (2024) Controls of ground water-dependent vegetation coverage in the yellow river basin, china: Insights from interpretable machine learning. Journal of Hydrology, 631: 130747. https://doi.org/10.1016/j.jhydrol.2024.130747
CHEN Y., CHEN W., CHANDRA PAL S., SAHA A., CHOWDHURI I., ADELI B., MOSAVI A. (2022) Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential. Geocarto International, 37(19):5564–5584. https://doi.org/10.1080/10106049.2021.1873828
CHRIATT M., FATH ALLAH R., FAKIH LANJRI A., AMMARI M., BEN ALLAL L. (2022) Artificial intelligence applica- tion in drought assessment, monitoring and forecasting using available remote sensed data. In International conference on advanced intelligent systems for sustain- able development (pp. 718–728). Springer. Conference.
CONGALTON, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1):35–46. https://doi.org/10.1016/0034-4257(91)90048-B
COOLEY R.L. (1977) A method of estimating parameters ters and assessing reliability for models of steady state groundwater flow: 1. theory and numerical proper- ties. Water Resources Research, 13(2):318–324. https://doi.org/10.1029/WR013i002p00318
COOLEY R. L. (1979) A method of estimating parame- ters and assessing reliability for models of steady state groundwater flow: 2. application of statistical analy- sis. Water Resources Research, 15(3): 603–617. https://doi.org/10.1029/WR015i003p00603
DENG H., CHEN Y. (2017) Influences of recent climate change and human activities on water storage variations in central asia. Journal of Hydrology, 544: 46–57. https://doi.org/10.1016/j.jhydrol.2016.11.026
DROGKOULA M., KOKKINOS K., SAMARAS N. (2023) A comprehensive survey of machine learning methodologies with emphasis in water resources management. Applied Sciences, 13(22):12147. https://doi.org/10.3390/app132212147
DUAN M., DUAN L. (2021) High spatial resolution remote sensing data classification method based on spectrum sharing. Scientific Programming, 2021, 4356957. https://doi.org/10.1155/2021/4356957
ED-DAOUDI R., ALAOUI A., ETTAKI B., ZEROUA-OUI J. (n.d.). Improving crop yield predictions in Morocco using machine learning algorithms. Journal of Ecological Engineering, 24(6)
FIGUEREDOA.J., WOLF P.S.A. (2009) Assortative pairing and life history strategy-a cross-cultural study. Human Natu-re, 20:317-330. https://doi.org/10.1007/s12110-009-9068-2
GAO, B.-C. (1996) Ndwi: A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3):257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
GIBBONS R.D. (1994) Statistical methods for groundwater monitoring. John Wiley & Sons.
GOKMEN M., VEKERDY Z., LUBCZYNSKI M.W., TIMMERMANS J., BATELAAN O., VERHOEF W. (2013) Assessing groundwater storage changes using remote sensing–based evapotranspiration and precipitation at a large semiarid basin scale. Journal of Hydrometeorology, 14(6): 1733–1753. https://doi.org/10.1175/JHM-D-12-0156.1
HAMAL S., BIST R., PANT B., ADHIKARI B. (2025) Water quality index and human health risk assessment for heavy metals in groundwater of bedkot municipality, kanchanpur, nepal: A cross-sectional study. Interna- tional EQA Journal of Environmental Quality, 69:73–86. https://doi.org/10.6092/issn.2281-4485/21769
HAO Z., AGHAKOUCHAK A., NAKHJIRI N., FARAHMAND A. (2014) Global integrated drought monitoring and pre- diction system (GIDMaPS) data sets. Figshare. http://dx.doi.org/10.6084/m9.figshare.853801
HOLLINGS T., BURGMAN M., VAN ANDEL M., GILBERT M., ROBINSON T., ROBINSON A. (2018) How do you find the green sheep? a critical review of the use of remotely sensed imagery to detect and count animals. Methods in Ecology and Evolution, 9(4):881–892. https://doi.org/10.1111/2041-210X.12943
HSSAISOUNE M., BOUCHAOU L., SIFEDDINE A., BOUIMETARHAN I., CHEHBOUNI A. (2020) Moroccan ground- water resources and evolution with global climate changes. Geosciences, 10(2):81. https://doi.org/10.3390/geosciences10020081
IBRAHIM A., WAYAYOK A., SHAFRI H.Z.M., TORIDI N.M. (2024) Remote sensing technologies for unlocking new groundwater insights: A comprehensive review. Journal of Hydrology: X(23):100175. https://doi.org/10.1016/j.hydroa.2024.100175
JARI A., BACHAOUI E.M., HAJAJ S., KHADDARI A., KHANDOUCH Y., EL HARTI A., NAMOUS M. (2023) Investigating machine learning and ensemble learn- ing models in groundwater potential mapping in arid region: case study from tan-tan water-scarce region, morocco. Frontiers in Water, 5:1305998. https://doi.org/10.3389/frwa.2023.1305998
JERBI M., TRABELSI F., ALI S.B.H. (2023) Remote sensing-based model for estimating groundwater pump- ing: Case study of irrigated perimeter of the medjerda lower valley, tunisia. In Proceedings of the 2023 interna- tional conference on earth observation and geo-spatial information (iceogi) (pp. 1–6).
JIANG X., JIANG Z.Y., LI Z.Y., SU J., TANG L.N., WU M.D., WANG Y.J. (2025) A framework for the construction of effective landscape ecological network with integrat- ing hydrological connectivity: A case study in dongjiang river basin, china. Journal of Environmental Management, 376. https://doi.org/10.1016/j.jenvman.2025.124509
JIANG Z., ZHANG H., TANG M., YANG X., YUAN L., YUAN Y., ZHONG M. (2025) Tracking california’s strik- ing water storage gains attributed to intensive atmospheric rivers. Journal of Hydrology, 653. https://doi.org/10.1016/j.jhydrol.2025.132804
KENDY E., GÉRARD-MARCHANT P., WALTER M. T., ZHANG Y., LIU C., STEENHUIS T.S. (2003) A soil-water-balance approach to quantify groundwater recharge from irri- gated cropland in the north china plain. Hydrological Processes, 17(10):2011–2031. https://doi.org/10.1002/hyp.1240
KHORRAMI B., GÜNDÜZ O. (2023) Remote sensing-based monitoring and evaluation of the basin-wise dynam- ics of terrestrial water and groundwater storage fluc- tuations. Environmental Monitoring and Assessment, 195(7):868. https://doi.org/10.1007/s10661-023-11480-7
LEE S., HYUN Y., LEE S., LEE,M.J. (2020) Groundwater potential mapping using remote sensing and gis-based machine learning techniques. Remote Sensing, 12(7):1200. https://doi.org/10.3390/rs12071200
LE PAGE M., BERJAMY B., FAKIR Y., BOURGIN F., JARLAN L., ABOURIDA,A. (2012) An integrated dss for groundwater management based on remote sensing. the case of a semi-arid aquifer in morocco. Water Resources Management, 26:3209–3230. https://doi.org/10.1007/s11269-012-0068-3
LUNDBERG S.M., LEE S.I. (2017) A unified approach to interpreting model predictions. In Advances in neural information processing systems (pp. 4765– 4774). Curran Associates, Inc. https://doi.org/10.5555/3295222.3295230
MACDONALD, A. M., LARK, R. M., TAYLOR, R. G., ABIYE, T., FALLAS, H. C., FAVREAU, G., WEST, C. (2021). Mapping groundwater recharge in africa from ground observations and implications for water security. Environmental Research. https://doi.org/10.1088/1748-9326/abd661
MACHIWAL, D., CLOUTIER, V., GÜLER, C., & KAZAKIS, N. (2018). A review of gis-integrated statistical tech- niques for groundwater quality evaluation and protec- tion. Environmental Earth Sciences, 77(19):681. https://doi.org/10.1007/s12665-018-7868-0
MAGAJI IBRAHIM, M., IBRAHIM, M., MUBI AISHA, M. (2024). Groundwater storage variability in west africa using gravity recovery and climate experiment (GRACE) and global land data assimilation system (GLDAS) data. Inter- national Journal of Environmental Quality, 64, 87–102. https://doi.org/10.6092/issn.2281-4485/20167
MAJUMDAR S., SMITH R., CONWAY B., BUTLER J., LAKSHMI V. (2020) Integrating remote sensing and machine learning for groundwater withdrawal estimation in Arizona. ESS Open Archive, November 25. https://doi.org/10.1002/essoar.10504981.1
MBO NKOULOU L.F., NGALLE H.B., CROS D., ADJE C.O.A., FASSINOU N.V.H., BELL J., ACHIGAN-DAKO E.G. (2022) Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in poly- ploid species. Frontiers in Plant Science, 13: 953133. https://doi.org/10.3389/fpls.2022.953133
MUSTAFA S.M.T., NOSSENT J., GHYSELS G., HUYS- MANS M. (2018) Estimation and impact assess- ment of input and parameter uncertainty in predict- ing groundwater flow with a fully distributed model. Water Resources Research, 54(9): 6585–6608. https://doi.org/10.1029/2018WR022619
NGUYEN H.D., NGUYEN V.H., DU Q.V.V., NGU-YEN C.T., DANG D.K., TRUONG Q.H., BUI Q.T. (2024) Application of hybrid model-based machine learning for groundwater potential prediction in the north central of vietnam. Earth Science Informatics, 17(2), 1569–1589. https://doi.org/10.1007/s12145-023-00998-3
PHONG T.V., PHAM B.T., TRINH P.T., LY H.B., VU Q.H., HO L.S., PRAKASH I. (2021) Groundwater poten- tial mapping using gis-based hybrid artificial intelli- gence methods. Groundwater, 59(5):745–760. https://doi.org/10.1111/gwat.13012
RAHIMI M., EBRAHIMI H. (2023) Data driven of under- ground water level using artificial intelligence hybrid algorithms. Scientific Reports, 13(1):10359. https://doi.org/10.1038/s41598-023-35255-9
SAFARI S., SHARGHI S., KERACHIAN R., NOORY H. (2023) A market-based mechanism for long-term ground-water management using remotely sensed data. Journal of Environmental Management, 332:117409. https://doi.org/10.1016/j.jenvman.2023.117409
SAHOUR H., SULTAN M., ABDELLATIF B., EMIL M., ABOTALIB A.Z., ABDELMOHSEN K., EL BASTA-WESY M. (2022) Identification of shallow groundwater in arid lands using multi-sensor remote sensing data and machine learning algorithms. Journal of Hydrology, 614: 128509. https://doi.org/10.1016/j.jhydrol.2022.128509
SINGH, D., SHARMA, V. (2024) Transformative Potential of AI and Remote Sensing in sustainable ground-dwater management. In: Nayak, R., Mittal, N., Kumar, M., Polkowski, Z., Khunteta, A. (eds) Recent Advancements in Artificial Intelligence . ICRAAI 2023. Innovations in Sustainable Technologies and Computing. Springer, Singapore. https://doi.org/10.1007/978-981-97-1111-6_11
SINGH P., MAHOR V., LAKSHMAIYA N., SHANKER K., KALIAPPAN S., MUTHUKANNAN M., RAJEN-DRAN G. (2024) Prediction of groundwater contamina-tion in an open landfill area using a novel hybrid clustering-based ai model. Environment Protection Engineering, 50(1). https://doi.org/10.37190/epe240105
SU Y., GUO B., ZHOU Z., ZHONG Y., MIN L. (2020) Spatiotemporal variations in groundwater revealed by grace and its driving factors in the huang-huai-hai plain, China. Sensors, 20(3):922. https://doi.org/10.3390/s20030922
WAGNER B. J. (1995) Recent advances in simulation- optimization groundwater management modeling. Reviews of Geophysics, 33(S2):1021–1028. https://doi.org/10.1029/95RG00414
WANG Y., HAN T., YANG Y., HAI Y., WEN Z., LI R., ZHENG H. (2024) Revealing the hidden conse- quences of increased soil moisture storage in green- ing drylands. Remote Sensing, 16(10):1819. https://doi.org/10.3390/rs16101819
YAN S.F., YU S.E., WU Y.B., PAN D-F., DONG J.G. (2018) Understanding groundwater table using a statis- tical model. Water Science and Engineering, 11(1):1–7. https://doi.org/10.1016/j.wse.2018.02.001
YASEEN Z.M. (2023) A new benchmark on machine learning methodologies for hydrological processes modelling: a comprehensive review for limitations and future research directions. Knowledge-Based Engineering and Sciences, 4(3):65–103. https://doi.org/10.1016/j.knosyseng.2023.100257
ZHOU Z., LU B., JIANG Z., ZHAO Y. (2024) Quanti- fying water storage changes and groundwater drought in the huaihe river basin using satellite gravimetry data. Sustainability, 16(19):8437. https://doi.org/10.3390/su16198437
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Azeddine Elhassouny
This work is licensed under a Creative Commons Attribution 4.0 International License.
