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Forecasting land-use and land-cover change for groundwater sustainability in the Muvattupuzha basin using CA-Markov (2033–2050)
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  • Published: 13 February 2026

Forecasting land-use and land-cover change for groundwater sustainability in the Muvattupuzha basin using CA-Markov (2033–2050)

  • Alagulakshmi K1,
  • Sneha Gautam1,2,3,
  • G. Prince Arulraj1,
  • Suneel Kumar Joshi4 &
  • …
  • Chang-Hoi Ho3 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Environmental sciences
  • Hydrology

Abstract

Rapid urbanization and land use and land cover (LULC) change have affected groundwater dynamics and its quality in many river basins. The present study uses an integrated framework combining multi-temporal Landsat imagery, geospatial analysis, multivariate statistics, and Machine Learning (ML) approaches to understand LULC changes and groundwater dynamics and its quality degradation.  The supervised classification was used in the present study, which shows that built-up land increased significantly from 12.3% (329.13 km2) in 2003 to 44.4% (1,187.11 km2) in 2023, mainly due to the conversion of agricultural and forested land. Furthermore, future LULC dynamics by the CA-Markov model indicate continuous landscape transformation, with net conversions into built-up and forested areas during the periods 2023–2033 and 2033–2043, respectively, while there is a decline in water bodies and agricultural land use, and their rates of change stabilize over the periods approaching 2043–2050. Multivariate statistical analyses, such as correlation analysis, Principal Component Analysis (PCA), and Cluster Analysis, identify both geogenic processes and human activities as dominant determinants of groundwater hydrochemistry. To investigate the relationships between physicochemical parameters and nitrate variability, 3 ML models were employed: Random Forest (RF), Support Vector Regression (SVR), and XGBoost. Model interpretation using SHapley Additive exPlanations (SHAP) showed that Mg2+, Ca2+, and alkalinity are the significant factors influencing nitrate distribution, reflecting buffering reactions and redox-controlled processes. An integrated framework combining LULC, hydrogeochemical, and ML techniques provides a strong foundation for assessing groundwater. It offers insights into sustainable land-use planning and groundwater management in rapidly urbanizing tropical basins.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

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Acknowledgements

The authors would like to acknowledge Karunya Institute of Technology and Sciences for providing the required facilities and logistical support during this research. We are very grateful to the anonymous reviewers for their comments and time on our paper.

Funding

The work of C-HH was supported by the National Research Foundation of Korea (NRF) funded by the Korean Government (MSIT; RS-2025-00555756) and the Ministry of Education (RS-2018-NR031078).

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Authors and Affiliations

  1. Division of Civil Engineering, Karunya Institute of Technology and Sciences Deemed University, Karunya Nagar, Coimbatore, Tamil Nadu, 641114, India

    Alagulakshmi K, Sneha Gautam & G. Prince Arulraj

  2. Water Institute, A Centre of Excellence Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, 641 114, India

    Sneha Gautam

  3. Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul, Republic of Korea

    Sneha Gautam & Chang-Hoi Ho

  4. Geo Climate Risk Solutions Pvt. Ltd, Visakhapatnam, Andhra Pradesh, 530048, India

    Suneel Kumar Joshi

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Contributions

A.K. designed the study, carried out the data collection, performed LULC classification, groundwater quality analysis, and prepared the initial draft of the manuscript.S.G. supervised the research, contributed to the conceptualization, methodology development, geospatial and hydrochemical interpretation, and thoroughly revised and edited the manuscript.G.P.A. assisted in data interpretation, CA–Markov modelling, and contributed to the refinement of the results and discussion.S.K.J. supported the statistical analyses, machine-learning modelling, and validation procedures.C.H.H. contributed to the methodological framework, interpretation of findings, and critical revision of the manuscript for intellectual content.All authors reviewed the manuscript and approved the final version.

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Correspondence to Sneha Gautam or Chang-Hoi Ho.

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K, A., Gautam, S., Prince Arulraj, G. et al. Forecasting land-use and land-cover change for groundwater sustainability in the Muvattupuzha basin using CA-Markov (2033–2050). Sci Rep (2026). https://doi.org/10.1038/s41598-026-38961-2

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  • Received: 18 November 2025

  • Accepted: 02 February 2026

  • Published: 13 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-38961-2

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Keywords

  • Muvattupuzha basin
  • Land use change
  • CA–Markov modelling
  • Groundwater quality
  • Machine learning
  • SHAP model
  • Nitrate contamination
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