Abstract
Conventional hydrological modeling is highly tedious for a data-sparse tropical river basin heavily loaded by hydraulic structures. The application of promising machine learning algorithms in this context is not explored much. This study investigates the suitability of two machine learning approaches—random forest regression model (RF) and gradient boosting regression model (GBM) in predicting the hydrological drought indices—standardized streamflow index (SSI) and streamflow drought index (SDI) based on the predicted meteorological drought indices—standardized precipitation index (SPI) and standardized precipitation evapotranspiration index under the future climate Representative Concentration Pathway (RCP) scenarios RCP 4.5 and RCP 8.5. The study predicts long-term hydrological drought indices SSI and SDI from 2006 to 2099 based on global climate models projections. The future drought years were extracted using the run theory. The study reveals that the river basin was prone to hydrological drought occurrences 50–65% of the time during the historic period (1988–2005) and 32–70% during the future period (2006–2099). It is evident from the results that both RF and GBM models follow a similar pattern of dry events in the future, which implies the prediction to be accurate. The study recommends the RF model for the study area, considering the model performances.
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Acknowledgements
The authors would like to express gratitude to the anonymous reviewers, editor, and associate editor of the journal. The first author would like to thank the Ministry of Education, India, for supporting the work in the form of a Ph.D. fellowship. We also thank the Indian Meteorological Department (IMD), Pune, for providing rainfall and temperature data, and Central Water Commission of India for providing the streamflow data.
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M.A.J.R. was involved in the data collection, investigation, methodology, formal analysis, model development, software, and writing—original draft; C.N.R. contributed to the investigation, supervision, and writing—review and editing, data curation, and validation.
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Rose, M.A.J., Chithra, N.R. Tree-based ensemble model prediction for hydrological drought in a tropical river basin of India. Int. J. Environ. Sci. Technol. 20, 4973–4990 (2023). https://doi.org/10.1007/s13762-022-04208-6
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DOI: https://doi.org/10.1007/s13762-022-04208-6