Abstract
The estimation of evaporation has been under surveillance, which is being carried out by many researchers toward applications in the fields related to hydrology and water resources management. Due to complexities associated with its estimation, research has employed several modes via direct and indirect methods to estimate. Accurate estimations are still the thrust area of research in these fields. The pan evaporation estimations with the help of data modeling techniques have provided better results in the recent past. The advancement in the field of data modeling has introduced several techniques which can best fit the data type and provide accurate estimations. The novel gamma test (GT) was used to decide the best input–output combination. Parameter optimization was carried out by grid search. The developed models gave better estimations of pan evaporation, but exhibited some limitations with nonlinearity, and sparse and noisy data. These limitations paved way for data pre-processing techniques such as wavelet transform. This study made an attempt to explore hybrid modeling using discrete wavelet transform (DWT) and support vector machines (SVR) for pan evaporation estimation. Two stations representing contrasting climatic zones namely ‘Bajpe’ and ‘Bangalore’ located in the state of Karnataka, India, are selected in this study. The meteorological datasets recorded at these stations are analyzed using gamma test and grid search to use the best input–output combinations for the models. The modeled pan evaporation estimations are very promising toward ever demanding accuracy expected in the associated fields.
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The authors are grateful to Engineer in-charge and staff members of meteorological station near Bajpe for their courtesy to provide access to data for the research work.
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Pammar, L., Deka, P.C. Daily pan evaporation modeling in climatically contrasting zones with hybridization of wavelet transform and support vector machines. Paddy Water Environ 15, 711–722 (2017). https://doi.org/10.1007/s10333-016-0571-x
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DOI: https://doi.org/10.1007/s10333-016-0571-x