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Multifactor prediction of the central Himalayan spring high-flows using machine learning classifiers


Year-round flow consistencies of the central Himalayan springs are extremely important for addressing rural water demand. As the prediction of Himalayan spring high-flows is expected to provide better opportunities for the management of excess runoff, this study aims to develop a data-driven model for predicting joint-fracture and depression type spring high-flows of the Kosi watershed of central Himalaya, India. Five machine learning algorithms are used with combinations of predictors, such as standardized anomaly of rainfall, pH, electrical conductivity and water quality index of spring water. The discharge and predictor parameters are used from a total of 06 springs distributed across the watershed, and monitored during January, 2019 to December, 2020 at monthly interval. Due to asymmetric relationships between model predictors and spring discharge, model performances are tested for the predictor time lags of 0–2 (= 60 days). A total of ten experiments are carried out, and model performances during training and testing are evaluated using receiver operator characteristics. The discriminant analysis classifier, in combination with rainfall and electrical conductivity as predictors, is found to be the best model for predicting spring high-flows.

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Data availability

The spring discharge and chemistry data are available with SM. The data sharing needs approval from the competent authority of GBPNIHE, Almora, India.

Code availability

The classifier codes are available with SM. The code sharing needs approval from the competent authority of GBPNIHE, Almora, India.


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Research funding of NMHS, MoEFCC, GoI, is acknowledged (NMHS-2017-18/MG-02/478) for completing this work. Mr. Vinod Kanwal is highly acknowledged for collecting monthly spring discharge data and water samples. The Central Laboratory of GBPNIHE, Uttarakhand, India, is acknowledged for chemical analysis of the water samples. Mr. Sourab Singh and Kunal Joshi are acknowledged for preparing the spring land cover maps and geological information. The Director of GBPNIHE, Uttarakhand, India, is acknowledged for providing the computational facilities.


Research funding of NMHS, MoEFCC, GoI, is acknowledged (NMHS-2017–18/MG-02/478).

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Concept, data analyses, and writing: SM; concept and writing: SS; supervision and editing: KK. All authors agree with this version of the manuscript.

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Correspondence to Sandipan Mukherjee.

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Mukherjee, S., Sen, S. & Kumar, K. Multifactor prediction of the central Himalayan spring high-flows using machine learning classifiers. Environ Earth Sci 82, 85 (2023).

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