Academic Journal
View in EDS
Linked Full Text

Environmental geochemistry and quality assessment of springs water in the Sikkim Himalaya: an entropy-weighted & machine learning approach.

Bibliographic Details
Title: Environmental geochemistry and quality assessment of springs water in the Sikkim Himalaya: an entropy-weighted & machine learning approach.
Authors: Yadav SK; DST Centre of Excellence, Water Resource, Cryosphere, and Climate Change Studies, Sikkim University, Gangtok, Sikkim, 737102, India.; Department of Geology, Banaras Hindu University, Varanasi, 221005, India., Misra AK; DST Centre of Excellence, Water Resource, Cryosphere, and Climate Change Studies, Sikkim University, Gangtok, Sikkim, 737102, India.; Department of Geology, Sikkim University, Gangtok, Sikkim, 737102, India., Wanjari N; DST Centre of Excellence, Water Resource, Cryosphere, and Climate Change Studies, Sikkim University, Gangtok, Sikkim, 737102, India.; Department of Geology, Sikkim University, Gangtok, Sikkim, 737102, India., Ranjan RK; DST Centre of Excellence, Water Resource, Cryosphere, and Climate Change Studies, Sikkim University, Gangtok, Sikkim, 737102, India. rkranjan@cus.ac.in.; Department of Geology, Sikkim University, Gangtok, Sikkim, 737102, India. rkranjan@cus.ac.in., Rai SP; Department of Geology, Banaras Hindu University, Varanasi, 221005, India., Rai R; Department of Geology, Banaras Hindu University, Varanasi, 221005, India.
Source: Environmental geochemistry and health [Environ Geochem Health] 2026 May 25; Vol. 48 (8). Date of Electronic Publication: 2026 May 25.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Kluwer Academic Publishers Country of Publication: Netherlands NLM ID: 8903118 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2983 (Electronic) Linking ISSN: 02694042 NLM ISO Abbreviation: Environ Geochem Health Subsets: MEDLINE
Imprint Name(s): Publication: 1999- : Dordrecht : Kluwer Academic Publishers
Original Publication: Kew, Surrey : Science and Technology Letters, 1985-
MeSH Terms: Environmental Monitoring*/methods , Natural Springs*/chemistry , Water Quality* , Machine Learning*, Rivers/chemistry ; Sikkim ; Entropy ; Himalayas ; Seasons
Abstract: Himalayan rivers are crucial for freshwater supply, ecosystem services, and livelihoods across their course from mountains to floodplains, but climate change, human activities, and geological factors are increasingly degrading water quality. Given these challenges and the vulnerability of the Sikkim Himalayan ecosystem, this study provides a comprehensive assessment of water quality by employing hydrogeochemical analysis, entropy-weighted water quality index, and advanced machine learning models to develop an effective decision-support framework for water resource management. A total of 160 water samples were collected from households, springs, and rivers across different seasons, and it was found that spatial and seasonal variability significantly influence the key parameters. Carbonate and silicate weathering dominantly controlled the chemical constituents of water (60-80%), along with localised anthropogenic influences. Various water quality indices were applied to assess the suitability of water for agricultural and domestic uses, with majority of samples classified from excellent to suitable categories, however, certain locations, especially those in densely populated and near geothermal regions, exhibited poorer water quality. In addition, advanced algorithm-based models were employed to further forecast and validate water quality scenario. Among several tested models, the Ridge Regression demonstrates superior performance (R2 = 1) with the lowest prediction error (RMSE = 0.02) in the region. SHAP sensitivity analysis revealed that NO3- and K+ are the key pollutant exerting the most significant influence on the water quality. This study supports in the formulation of sustainable water management practices aligned with SDG 6, enhancing the long-term resilience of Sikkim's Himalayan water resources against climate change.
(© 2026. The Author(s), under exclusive licence to Springer Nature B.V.)
Competing Interests: Declarations. Competing interest: The authors declare no competing interests.
References: Ahmad, M. W., Mourshed, M., & Rezgui, Y. (2017). Trees vs neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, 147, 77–89. https://doi.org/10.1016/j.enbuild.2017.04.038. (PMID: 10.1016/j.enbuild.2017.04.038)
Ahmed, A. K. A., El-Rawy, M., Ibraheem, A. M., Al-Arifi, N., & Abd-Ellah, M. K. (2023). Forecasting of groundwater quality by using deep learning time series techniques in an arid region. Sustainability, 15(8), Article 6529. https://doi.org/10.3390/su15086529. (PMID: 10.3390/su15086529)
Aju, C. D., Achu, A. L., Mohammed, M. P., Raicy, M. C., Gopinath, G., & Reghunath, R. (2024). Groundwater quality prediction and risk assessment in Kerala, India: A machine-learning approach. Journal of Environmental Management, 370, Article 122616. https://doi.org/10.1016/j.jenvman.2024.122616. (PMID: 10.1016/j.jenvman.2024.122616)
Akakuru, O. C., Njoku, U. B., Obinna-Akakuru, A. U., Akudinobi, B. E. B., Obasi, P. N., Aigbadon, G. O., & Onyeanwuna, U. B. (2023). Non-carcinogenic health risk assessment and predicting of pollution indexing of groundwater around Osisioma, Nigeria, using artificial neural networks and multi-linear modeling principles. Stochastic Environmental Research and Risk Assessment, 37(7), 2413–2443. https://doi.org/10.1007/s00477-023-02398-0. (PMID: 10.1007/s00477-023-02398-0)
Amiri, V., Rezaei, M., & Sohrabi, N. (2014). Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran. Environmental Earth Sciences, 72(9), 3479–3490. https://doi.org/10.1007/s12665-014-3255-0. (PMID: 10.1007/s12665-014-3255-0)
Arora, S., & Keshari, A. K. (2021). Pattern recognition of water quality variance in Yamuna River (India) using hierarchical agglomerative cluster and principal component analyses. Environmental Monitoring and Assessment, 193(8), 494. https://doi.org/10.1007/s10661-021-09318-1. (PMID: 10.1007/s10661-021-09318-1)
Arya, P. C., Nambaje, C., Kiran, S., Satish-Kumar, M., & Sajeev, K. (2023). Himalayan magnesite records abrupt cyanobacterial growth that plausibly triggered the Neoproterozoic Oxygenation Event. Precambrian Research, 395, Article 107129. https://doi.org/10.1016/j.precamres.2023.107129. (PMID: 10.1016/j.precamres.2023.107129)
Ayyad, M., Hajj, M. R., & Marsooli, R. (2022). Machine learning-based assessment of storm surge in the New York metropolitan area. Scientific Reports, 12(1), Article 19215. https://doi.org/10.1038/s41598-022-23627-6. (PMID: 10.1038/s41598-022-23627-6)
Bahukhandi, K. D., Kushwaha, A., Devi, G., Goswami, L., Bhan, U., Oh, S., et al. (2024). Hydro-geochemistry of the Bindal River Catchment: Water suitability for irrigation and drinking in the Doon Valley outer Himalaya. International Journal of Environmental Analytical Chemistry. https://doi.org/10.1080/03067319.2024.2436604. (PMID: 10.1080/03067319.2024.2436604)
Baird, R., & Bridgewater, L. (2017). APHA, standard methods for the examination of water and wastewater, American Public Health Association. American Public Health Association.
Basu, S. K. (2013). Geology of Sikkim state and Darjeeling district of West Bengal. Geological Society of India.
Bedi, S., Samal, A., Ray, C., & Snow, D. (2020). Comparative evaluation of machine learning models for groundwater quality assessment. Environmental Monitoring and Assessment, 192(12), 776. https://doi.org/10.1007/s10661-020-08695-3. (PMID: 10.1007/s10661-020-08695-3)
BIS, Bureau of Indian Standards, Standard, I. (2012). Drinking water—Specification (Second Revision). IS, 10500, 2012. https://law.resource.org/pub/in/bis/S06/is.10500.2012.pdf (accessed 28<sup>th</sup> September 2025).
Bisht, H., Kotlia, B. S., Kumar, K., Taloor, A. K., Arya, P. C., Sah, S. K., et al. (2022). Hydrogeochemical analysis and identification of solute sources in the meltwater of Chaturangi glacier, Garhwal Himalaya India. Applied Water Science, 12(3), 29. https://doi.org/10.1007/s13201-021-01510-5. (PMID: 10.1007/s13201-021-01510-5)
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324. (PMID: 10.1023/A:1010933404324)
Census (2011). Census table, Census of India, Office of the Registrar General & Census Commissioner, India, Ministry of Home Affairs, Government of India. https://censusindia.gov.in/census.website/data/census-tables (accessed 2<sup>nd</sup> September 2025).
Chadha, D. K. (1999). A proposed new diagram for geochemical classification of natural waters and interpretation of chemical data. Hydrogeology Journal, 7(5), 431–439. https://doi.org/10.1007/s100400050216. (PMID: 10.1007/s100400050216)
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Presented at the KDD ’16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA: ACM. https://doi.org/10.1145/2939672.2939785.
Cheng, Y.-S., Yu, T.-T., & Son, N.-T. (2021). Random forests for landslide prediction in Tsengwen River Watershed, Central Taiwan. Remote Sensing, 13(2), 199. (PMID: 10.3390/rs13020199)
Chidambaram, S., Karmegam, U., Sasidhar, P., Prasanna, M. V., Manivannan, R., Arunachalam, S., et al. (2011). Significance of saturation index of certain clay minerals in shallow coastal groundwater, in and around Kalpakkam, Tamil Nadu India. Journal of Earth System Science, 120(5), 897–909. https://doi.org/10.1007/s12040-011-0105-2. (PMID: 10.1007/s12040-011-0105-2)
Chidambaram, S., Prasanna, M. V., Venkatramanan, S., Nepolian, M., Pradeep, K., Panda, B., et al. (2022). Groundwater quality assessment for irrigation by adopting new suitability plot and spatial analysis based on fuzzy logic technique. Environmental Research, 204, Article 111729. https://doi.org/10.1016/j.envres.2021.111729. (PMID: 10.1016/j.envres.2021.111729)
Correggia, M., Di Iorio, L., Bastianoni, A. B., Yucel, M., Cordone, A., & Giovannelli, D. (2024). Standard operating procedure for the analysis of major ions in hydrothermal fluids by ion chromatography. Open Research Europe, 3, 94. https://doi.org/10.12688/openreseurope.15605.3. (PMID: 10.12688/openreseurope.15605.3)
Dashora, M., Kumar, A., Kumar, S., Kumar, P., Kumar, A., & Singh, C. K. (2022). Geochemical assessment of groundwater in a desertic region of India using chemometric analysis and entropy water quality index (EWQI). Natural Hazards, 112(1), 747–782. https://doi.org/10.1007/s11069-021-05204-8. (PMID: 10.1007/s11069-021-05204-8)
Dhiman, A., Ramanthan, A., Macklin, M., Yadav, S., Kushwaha, S., Mudbhatkal, A., & Senapathi, V. (2023). Heavy metal distribution in various environmental matrices and their risk assessment in Ganga River Basin, India. Human and Ecological Risk Assessment: An International Journal, 29(2), 621–650. https://doi.org/10.1080/10807039.2022.2159785. (PMID: 10.1080/10807039.2022.2159785)
Doneen, L. D. (1975). Water quality for irrigated agriculture. In A. Poljakoff-Mayber & J. Gale (Eds.), Plants in saline environments (Vol. 15, pp. 56–76). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-80929-3&#95;5. (PMID: 10.1007/978-3-642-80929-3_5)
Dubey, S. K., Ranjan, R. K., Misra, A. K., Wanjari, N., & Vishwakarma, S. (2022). Variability of precipitation extremes and drought intensity over the Sikkim State, India, during 1950–2018. Theoretical and Applied Climatology, 148(1–2), 1–14. https://doi.org/10.1007/s00704-022-03931-x. (PMID: 10.1007/s00704-022-03931-x)
Dutt, V., & Sharma, N. (2022). Potable water quality assessment of traditionally used springs in a hilly town of Bhaderwah, Jammu and Kashmir, India. Environmental Monitoring and Assessment, 194(1), Article 30. https://doi.org/10.1007/s10661-021-09591-0. (PMID: 10.1007/s10661-021-09591-0)
Dutta, N., Thakur, B. K., Nurujjaman, M., Debnath, K., & Bal, D. P. (2022). An assessment of the water quality index (WQI) of drinking water in the Eastern Himalayas of South Sikkim, India. Groundwater for Sustainable Development, 17, Article 100735. https://doi.org/10.1016/j.gsd.2022.100735. (PMID: 10.1016/j.gsd.2022.100735)
El-Rawy, M., Wahba, M., Fathi, H., Alshehri, F., Abdalla, F., & El Attar, R. M. (2024). Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques. Marine Pollution Bulletin, 205, Article 116645. https://doi.org/10.1016/j.marpolbul.2024.116645. (PMID: 10.1016/j.marpolbul.2024.116645)
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics. https://doi.org/10.1214/aos/1013203451. (PMID: 10.1214/aos/1013203451)
Fu, T., Xu, C., Yang, L., Hou, S., & Xia, Q. (2022). Measurement and driving factors of grey water footprint efficiency in Yangtze River Basin. Science of the Total Environment, 802, Article 149587. https://doi.org/10.1016/j.scitotenv.2021.149587. (PMID: 10.1016/j.scitotenv.2021.149587)
Gibbs, R. J. (1970). Mechanisms controlling world water chemistry. Science, 170(3962), 1088–1090. https://doi.org/10.1126/science.170.3962.1088. (PMID: 10.1126/science.170.3962.1088)
Goldstein, B. A., Polley, E. C., & Briggs, F. B. S. (2011). Random forests for genetic association studies. Statistical Applications in Genetics and Molecular Biology. https://doi.org/10.2202/1544-6115.1691. (PMID: 10.2202/1544-6115.1691)
Gong, G., Mattevada, S., & O’Bryant, S. E. (2014). Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas. Environmental Research, 130, 59–69. https://doi.org/10.1016/j.envres.2013.12.005. (PMID: 10.1016/j.envres.2013.12.005)
Guppy, L., Uyttendaele, P., Villholth, K. G., & Smakhtin, V. U. (2018). Groundwater and sustainable development goals: Analysis of interlinkages. https://hdl.handle.net/10568/98576.
Haokip, P., Khan, Md. A., Choudhari, P., Kulimushi, L. C., & Qaraev, I. (2022). Identification of erosion-prone areas using morphometric parameters, land use land cover and multi-criteria decision-making method: Geo-informatics approach. Environment, Development and Sustainability, 24(1), 527–557. https://doi.org/10.1007/s10668-021-01452-7. (PMID: 10.1007/s10668-021-01452-7)
Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67. https://doi.org/10.1080/00401706.1970.10488634. (PMID: 10.1080/00401706.1970.10488634)
Huang, Y., Han, R., Qi, J., Duan, H., Chen, C., Lu, X., & Li, N. (2022). Health risks of industrial wastewater heavy metals based on improved grey water footprint model. Journal of Cleaner Production, 377, Article 134472. https://doi.org/10.1016/j.jclepro.2022.134472. (PMID: 10.1016/j.jclepro.2022.134472)
Ibrahim, M., Labaki, M., Giraudon, J.-M., & Lamonier, J.-F. (2020). Hydroxyapatite, a multifunctional material for air, water and soil pollution control: A review. Journal of Hazardous Materials, 383, Article 121139. https://doi.org/10.1016/j.jhazmat.2019.121139. (PMID: 10.1016/j.jhazmat.2019.121139)
Jacobson, A. D., Blum, J. D., & Walter, L. M. (2002). Reconciling the elemental and Sr isotope composition of Himalayan weathering fluxes: Insights from the carbonate geochemistry of stream waters. Geochimica Et Cosmochimica Acta, 66, 3417–3429. https://doi.org/10.1016/S0016-7037(02)00951-1. (PMID: 10.1016/S0016-7037(02)00951-1)
Karunanidhi, D., Aravinthasamy, P., Deepali, M., Subramani, T., & Sunkari, E. D. (2021). Appraisal of subsurface hydrogeochemical processes in a geologically heterogeneous semi-arid region of South India based on mass transfer and fuzzy comprehensive modeling. Environmental Geochemistry and Health, 43(2), 1009–1028. https://doi.org/10.1007/s10653-020-00676-2. (PMID: 10.1007/s10653-020-00676-2)
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., et al. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30. https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html . Accessed 24 May 2025.
Kelley, W. P. (1941). Permissible composition and concentration of irrigation water. Transactions of the American Society of Civil Engineers, 106(1), 849–855. https://doi.org/10.1061/TACEAT.0005384. (PMID: 10.1061/TACEAT.0005384)
Khan, M. Y. A., Gani, K. M., & Chakrapani, G. J. (2016). Assessment of surface water quality and its spatial variation. A case study of Ramganga River, Ganga Basin, India. Arabian Journal of Geosciences, 9(1), Article 28. https://doi.org/10.1007/s12517-015-2134-7. (PMID: 10.1007/s12517-015-2134-7)
Khan, M. Y. A., Gani, K. M., & Chakrapani, G. J. (2017). Spatial and temporal variations of physicochemical and heavy metal pollution in Ramganga River—a tributary of River Ganges, India. Environmental Earth Sciences, 76(5), Article 231. https://doi.org/10.1007/s12665-017-6547-3. (PMID: 10.1007/s12665-017-6547-3)
Khan, M. Y. A., & Tian, F. (2018). Understanding the potential sources and environmental impacts of dissolved and suspended organic carbon in the diversified Ramganga River, Ganges Basin, India. Proceedings of the International Association of Hydrological Sciences, 379, 61–66. https://doi.org/10.5194/piahs-379-61-2018. (PMID: 10.5194/piahs-379-61-2018)
Khan, M. A., & Wen, J. (2021). Evaluation of physicochemical and heavy metals characteristics in surface water under anthropogenic activities using multivariate statistical methods, Garra River, Ganges Basin, India. Environmental Engineering Research. https://doi.org/10.4491/eer.2020.280. (PMID: 10.4491/eer.2020.280)
Khouni, I., Louhichi, G., & Ghrabi, A. (2021). Use of GIS based inverse distance weighted interpolation to assess surface water quality: Case of Wadi El Bey, Tunisia. Environmental Technology & Innovation, 24, Article 101892. https://doi.org/10.1016/j.eti.2021.101892. (PMID: 10.1016/j.eti.2021.101892)
Kumar, M., Ramanathan, A. L., Ranjan, S., Singh, V. B., Kumar, N., Yadav, S. K., et al. (2018). Groundwater evolution and its utility in upper Ganges-Yamuna Alluvial Plain of Northern India, India: Evidence from solute chemistry and stable isotopes. Groundwater for Sustainable Development, 7, 400–409. https://doi.org/10.1016/j.gsd.2018.07.001. (PMID: 10.1016/j.gsd.2018.07.001)
Kumar, P., Sharma, K., Malu, A., Rajak, R., Gupta, A., Baruah, B., et al. (2023). Measurement report: Intra-annual variability of black/brown carbon and its interrelation with meteorological conditions over Gangtok, Sikkim. Egusphere, 2023, 1–24. https://doi.org/10.5194/acp-24-11585-2024. (PMID: 10.5194/acp-24-11585-2024)
Mingers, J. (1989). An empirical comparison of selection measures for decision-tree induction. Machine Learning, 3(4), 319–342. https://doi.org/10.1007/BF00116837. (PMID: 10.1007/BF00116837)
Pandey, A., Parashar, D., Palni, S., Sarkar, M. S., Mishra, A. P., Singh, A. P., et al. (2024). Spatiotemporal snowline status and climate variability impact assessment: A case study of Pindari River Basin, Kumaun Himalaya, India. Environmental Sciences Europe, 36(1), Article 104. https://doi.org/10.1186/s12302-024-00924-7. (PMID: 10.1186/s12302-024-00924-7)
Pant, N., Hagare, D., Maheshwari, B., Rai, S. P., Sharma, M., Dollin, J., et al. (2024). Rejuvenation of the springs in the Hindu Kush Himalayas through transdisciplinary approaches—A review. Water, 16(24), Article 3675. https://doi.org/10.3390/w16243675. (PMID: 10.3390/w16243675)
Pant, R. R., Qaiser, F. U. R., Wang, G., Adhikari, S., Bishwakarma, K., Baral, U., et al. (2021). Hydrochemical appraisal and solute acquisitions in Seti River Basin, Central Himalaya, Nepal. Environmental Monitoring and Assessment, 193(10), Article 656. https://doi.org/10.1007/s10661-021-09437-9. (PMID: 10.1007/s10661-021-09437-9)
Pant, R. R., Zhang, F., Rehman, F. U., Wang, G., Ye, M., Zeng, C., & Tang, H. (2018). Spatiotemporal variations of hydrogeochemistry and its controlling factors in the Gandaki River Basin, Central Himalaya, Nepal. Science of the Total Environment, 622–623, 770–782. https://doi.org/10.1016/j.scitotenv.2017.12.063. (PMID: 10.1016/j.scitotenv.2017.12.063)
Parween, S., Siddique, N. A., Diganta, M. T. M., Olbert, A. I., & Uddin, M. G. (2022). Assessment of urban river water quality using modified NSF water quality index model at Siliguri city, West Bengal, India. Environmental and Sustainability Indicators, 16, Article 100202. https://doi.org/10.1016/j.indic.2022.100202. (PMID: 10.1016/j.indic.2022.100202)
Piper, A. M. (1944). A graphic procedure in the geochemical interpretation of water-analyses. Transactions, American Geophysical Union, 25(6), 914. https://doi.org/10.1029/TR025i006p00914. (PMID: 10.1029/TR025i006p00914)
Qiao, P., Lei, M., Yang, S., Yang, J., Guo, G., & Zhou, X. (2018). Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing. Environmental Science and Pollution Research, 25(16), 15597–15608. https://doi.org/10.1007/s11356-018-1552-y. (PMID: 10.1007/s11356-018-1552-y)
Rai, K., Misra, A. K., Ranjan, R. K., Wanjari, N., Rajak, R., Yadav, S. K., et al. (2024). Assessment of heavy metal and E. coli contamination in water sources of the East and South Districts, Sikkim Himalaya, India. Water Conservation Science and Engineering, 9(1), Article 22. https://doi.org/10.1007/s41101-024-00257-9. (PMID: 10.1007/s41101-024-00257-9)
Raina, V. K., & Srivastava, D. (2008). Glacier atlas of India. GSI Publications, 7(1), Article 315. https://doi.org/10.3189/002214309790152357. (PMID: 10.3189/002214309790152357)
Raju, S. (2016). Geological survey of India. https://doi.org/10.16943/ptinsa/2016/48504.
Raju, N. J., Dey, S., Gossel, W., & Wycisk, P. (2012). Fluoride hazard and assessment of groundwater quality in the semi-arid Upper Panda River basin, Sonbhadra district, Uttar Pradesh, India. Hydrological Sciences Journal, 57(7), 1433–1452. https://doi.org/10.1080/02626667.2012.715748. (PMID: 10.1080/02626667.2012.715748)
Ranjan, R. K., Ramanathan, A. L., Parthasarathy, P., & Kumar, A. (2013). Hydrochemical characteristics of groundwater in the plains of Phalgu River in Gaya, Bihar, India. Arabian Journal of Geosciences, 6, 3257–3267. https://doi.org/10.1007/s12517-012-0599-1. (PMID: 10.1007/s12517-012-0599-1)
Rawat, K. S., Singh, S. K., & Gautam, S. K. (2018a). Assessment of groundwater quality for irrigation use: A peninsular case study. Applied Water Science, 8(8), Article 233. https://doi.org/10.1007/s13201-018-0866-8. (PMID: 10.1007/s13201-018-0866-8)
Rawat, S. S., Rai, S. P., Jose, P. G., Kumar, P., & Raina, G. (2018b). Hydrological evaluation of impact of springshed development programme: Case study of four springs from a Himalayan state of India. Hydrological Journal, 40(1–4), 76–86.
RMDD. (2017). Dhara Vikas Handbook “A User Manual for Springshed Development to Revive Himalayan Springs. https://www.undp.org/india/publications/dhara-vikas-handbook (accessed 5 October 2025).
Rodriguez-Galiano, V., Mendes, M. P., Garcia-Soldado, M. J., Chica-Olmo, M., & Ribeiro, L. (2014). Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain). Science of the Total Environment, 476, 189–206. https://doi.org/10.1016/j.scitotenv.2014.01.001. (PMID: 10.1016/j.scitotenv.2014.01.001)
Schoppa, L., Disse, M., & Bachmair, S. (2020). Evaluating the performance of random forest for large-scale flood discharge simulation. Journal of Hydrology, 590, Article 125531. https://doi.org/10.1016/j.jhydrol.2020.125531. (PMID: 10.1016/j.jhydrol.2020.125531)
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x. (PMID: 10.1002/j.1538-7305.1948.tb01338.x)
Sherpa, M. T., Das, S., & Thakur, N. (2013). Physicochemical analysis of hot water springs of Sikkim-Polok Tatopani, Borong Tatopani and Reshi Tatopani. Recent Research in Science and Technology, 5(1), 63–67. https://doi.org/10.3126/rrst.v6i2.7141. (PMID: 10.3126/rrst.v6i2.7141)
Singh, A. K., & Hasnain, S. I. (1998). Major ion chemistry and weathering control in a high altitude basin: Alaknanda River, Garhwal Himalaya, India. Hydrological Sciences Journal, 43(6), 825–843. https://doi.org/10.1080/02626669809492181. (PMID: 10.1080/02626669809492181)
Singh, A., Patel, A. K., Deka, J. P., & Kumar, M. (2020). Natural recharge transcends anthropogenic forcing that influences arsenic vulnerability of the quaternary alluviums of the Mid-Gangetic Plain. Npj Clean Water, 3(1), Article 27. https://doi.org/10.1038/s41545-020-0075-5. (PMID: 10.1038/s41545-020-0075-5)
Singh, R. K. (2021). Water resources in the Indian Himalayan Region: Issues, concerns and best practices for its conservation. International Journal of Environmental Sciences, 10(4), 95–101. https://doi.org/10.70322/rrd.2025.10011. (PMID: 10.70322/rrd.2025.10011)
Sonker, I., Tripathi, J. N., & Singh, A. K. (2021). Landslide susceptibility zonation using geospatial technique and analytical hierarchy process in Sikkim Himalaya. Quaternary Science Advances, 4, Article 100039. https://doi.org/10.1016/j.qsa.2021.100039. (PMID: 10.1016/j.qsa.2021.100039)
Sood, V., Gusain, H. S., Gupta, S., Taloor, A. K., & Singh, S. (2021). Detection of snow/ice cover changes using subpixel-based change detection approach over Chhota-Shigri glacier, Western Himalaya, India. Quaternary International, 575, 204–212. https://doi.org/10.1016/j.quaint.2020.05.016. (PMID: 10.1016/j.quaint.2020.05.016)
Srinivasamoorthy, K., Gopinath, M., Chidambaram, S., Vasanthavigar, M., & Sarma, V. S. (2014). Hydrochemical characterization and quality appraisal of groundwater from Pungar sub basin, Tamilnadu, India. Journal of King Saud University - ScienCe, 26(1), 37–52. https://doi.org/10.1016/j.jksus.2013.08.001. (PMID: 10.1016/j.jksus.2013.08.001)
Srivastava, T., Harris, N., Mottram, C., Joshi, K. B., & Wanjari, N. (2024). From source to emplacement: The origin of leucogranites from the Sikkim-Darjeeling Himalayas, India. Geoscience Frontiers, 15, Article 101733. https://doi.org/10.1016/j.gsf.2023.101733. (PMID: 10.1016/j.gsf.2023.101733)
Su, H., Kang, W., Xu, Y., & Wang, J. (2018). Assessing groundwater quality and health risks of nitrogen pollution in the Shenfu Mining Area of Shaanxi Province, Northwest China. Exposure and Health, 10(2), 77–97. https://doi.org/10.1007/s12403-017-0247-9. (PMID: 10.1007/s12403-017-0247-9)
Subba Rao, N., Sunitha, B., Adimalla, N., & Chaudhary, M. (2020). Quality criteria for groundwater use from a rural part of Wanaparthy District, Telangana State, India, through ionic spatial distribution (ISD), entropy water quality index (EWQI) and principal component analysis (PCA). Environmental Geochemistry and Health, 42(2), 579–599. https://doi.org/10.1007/s10653-019-00393-5. (PMID: 10.1007/s10653-019-00393-5)
Szabolcs, I. (1967). The influence of irrigation water of high sodium carbonate content on soils. https://www.cabidigitallibrary.org/doi/full/ https://doi.org/10.5555/19671901627 . Accessed 11 June 2025.
Szramek, K., Walter, L. M., Kanduč, T., & Ogrinc, N. (2011). Dolomite versus calcite weathering in hydrogeochemically diverse watersheds established on bedded carbonates (Sava and Soča Rivers, Slovenia). Aquatic Geochemistry, 17(4–5), 357–396. https://doi.org/10.1007/s10498-011-9125-4. (PMID: 10.1007/s10498-011-9125-4)
Talukdar, S., Bera, S., Naikoo, M. W., Ramana, G. V., Mallik, S., Kumar, P. A., & Rahman, A. (2024). Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak. Journal of Environmental Management, 351, Article 119866. https://doi.org/10.1016/j.jenvman.2023.119866. (PMID: 10.1016/j.jenvman.2023.119866)
Tambe, S., Arrawatia, M. L., & Sharma, N. (2011). Assessing the priorities for sustainable forest management in the Sikkim Himalaya, India: A remote sensing based approach. Journal of the Indian Society of Remote Sensing, 39(4), 555–564. https://doi.org/10.1007/s12524-011-0110-6. (PMID: 10.1007/s12524-011-0110-6)
Tan, Y., & Wang, J. (2004). A support vector machine with a hybrid kernel and minimal Vapnik-Chervonenkis dimension. IEEE Transactions on Knowledge and Data Engineering, 16(4), 385–395. https://doi.org/10.1109/TKDE.2004.1269664. (PMID: 10.1109/TKDE.2004.1269664)
Thanh, N. N., Chotpantarat, S., Ngu, N. H., Thunyawatcharakul, P., & Kaewdum, N. (2024). Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand. Environmental Research, 252, Article 118952. https://doi.org/10.1016/j.envres.2024.118952. (PMID: 10.1016/j.envres.2024.118952)
Tiwari, A. K., Singh, A. K., Giri, S., & Mahato, M. K. (2021). Major ion chemistry and hydrochemical processes controlling water composition of Teesta River catchment, Sikkim Himalaya, India. International Journal of Environmental Analytical Chemistry. https://doi.org/10.1080/03067319.2021.1992402. (PMID: 10.1080/03067319.2021.1992402)
Tsering, T., Wahed, M. S. A., Iftekhar, S., & Sillanpää, M. (2019). Major ion chemistry of the Teesta River in Sikkim Himalaya, India: Chemical weathering and assessment of water quality. Journal of Hydrology: Regional Studies, 24, Article 100612. https://doi.org/10.1016/j.ejrh.2019.100612. (PMID: 10.1016/j.ejrh.2019.100612)
USSL Salinity Laboratory (1954). Diagnosis and Improvement of Saline and Alkaline Soils. US Department of Agriculture Handbook, No. 60, 160 p.
UN.ESCAP. (2016). Sustainable Development Goal 6: ensure availability and sustainable management of water and sanitation for all. Retrieved from: https://hdl.handle.net/20.500.12870/4173 .
Vapnik, V. (1998). The support vector method of function estimation. In J. A. K. Suykens & J. Vandewalle (Eds.), Nonlinear modeling (pp. 55–85). Springer, US. (PMID: 10.1007/978-1-4615-5703-6_3)
World Health Organization. (2017). Guidelines for drinking-water quality: first addendum to the fourth edition. https://apps.who.int/iris/bitstream/handle/10665/254636/9789241550017-eng.pdf . Accessed 16<sup>th</sup> September 2025.
Wu, C., Fang, C., Wu, X., & Zhu, G. (2020a). Health-risk assessment of arsenic and groundwater quality classification using Random Forest in the Yanchi Region of Northwest China. Exposure and Health, 12(4), 761–774. https://doi.org/10.1007/s12403-019-00335-7. (PMID: 10.1007/s12403-019-00335-7)
Wu, T., Zhang, W., Jiao, X., Guo, W., & Hamoud, Y. A. (2020b). Comparison of five boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables. PLoS ONE, 15(6), Article e0235324. https://doi.org/10.1371/journal.pone.0235324. (PMID: 10.1371/journal.pone.0235324)
Wu, Z., Wang, X., Chen, Y., Cai, Y., & Deng, J. (2018). Assessing river water quality using water quality index in Lake Taihu Basin, China. Science of the Total Environment, 612, 914–922. https://doi.org/10.1016/j.scitotenv.2017.08.293. (PMID: 10.1016/j.scitotenv.2017.08.293)
Wu, Z., Wu, Y., Yu, Y., Wang, L., Qi, P., Sun, Y., et al. (2024). Assessment of groundwater quality variation characteristics and influencing factors in an intensified agricultural area: An integrated hydrochemical and machine learning approach. Journal of Environmental Management, 371, Article 123233. https://doi.org/10.1016/j.jenvman.2024.123233. (PMID: 10.1016/j.jenvman.2024.123233)
Yadav, S. K., Baruah, B., Dutta, K., Rai, K., Rai, A., Rajak, R., et al. (2025). Exploring the multi-faceted health risks of heavy metals exposure in Sikkim’s unique Himalayan ecosystem, India. Environmental Monitoring and Assessment, 197(3), Article 286. https://doi.org/10.1007/s10661-025-13745-9. (PMID: 10.1007/s10661-025-13745-9)
Yadav, S. K., Ramanathan, A. L., Sabarathinam, C., Kumar, A., Kumar, M., & Dhiman, A. (2024). Understanding arsenic behavior in alluvial aquifers: Evidence from sediment geochemistry, solute chemistry and environmental isotopes. Geoscience Frontiers. https://doi.org/10.1016/j.gsf.2024.101844. (PMID: 10.1016/j.gsf.2024.101844)
Zimik, H. V., Angchuk, T., Misra, A. K., Ranjan, R. K., Wanjari, N., & Basnett, S. (2022). GIS-based identification of potential watershed recharge zones using analytic hierarchy process in Sikkim Himalayan region. Applied Water Science, 12(11), Article 248. https://doi.org/10.1007/s13201-022-01758-5. (PMID: 10.1007/s13201-022-01758-5)
Zimik, H. V., Misra, A. K., Ranjan, R. K., Wanjari, N., Basnett, S., Roy, G., et al. (2023). Evaluation of heavy metal and microbial contamination in various water resources of West and North Sikkim, India. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-023-03044-z. (PMID: 10.1007/s10668-023-03044-z)
Grant Information: DST/CCP/CoE/186/2019 (G) Department of Science and Technology, Ministry of Science and Technology, India
Contributed Indexing: Keywords: Deep neural network; Entropy-weighted water quality index; Hydrogeochemical analysis; Sensitivity analysis; Sikkim Himalaya; Water quality zonation
Entry Date(s): Date Created: 20260525 Date Completed: 20260717 Latest Revision: 20260717
Update Code: 20260717
DOI: 10.1007/s10653-026-03263-z
PMID: 42183891
Database: MEDLINE
Description
ISSN:1573-2983
DOI:10.1007/s10653-026-03263-z