Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)

نویسندگان

چکیده

Lakes are major surface water resource in semi-arid regions, providing for agriculture and domestic use. Prediction of future availability lakes regions is important as they highly sensitive to climate variability. This study examine the level fluctuations Pakhal Lake, Telangana, India using a combination process-based hydrological model machine learning technique under change scenarios. an artificial lake built meet irrigation requirements region. Predictions can help with effective planning management resources. In this study, integrated approach adopted predict Lake response potential change. makes use NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset which contains 21 Climate Models (GCMs) at resolution 0.25 × 0.25° used study. The Reliability Ensemble Averaging (REA) method applied models create ensemble model. outputs from Soil Water Assessment Tool (SWAT) develop machine-learning based Support Vector Regression (?-SVR) predicting levels Lake. scores three metrics, correlation coefficient (R2), RMSE MEA 0.79, 0.018 m, 0.13 respectively training period. values validation periods 0.72, 0.6, indicating that captures observed trends satisfactorily. SWAT simulation results showed decrease runoff Representative Concentration Pathways (RCP) 4.5 scenario increase RCP 8.5 scenario. Further, ?-SVR time period indicate during crop growth seasons. aids necessary options With limited datasets, be easily extended other systems.

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ژورنال

عنوان ژورنال: Sustainability

سال: 2022

ISSN: ['2071-1050']

DOI: https://doi.org/10.3390/su14126974