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Hydrology - Deep & ML by Pranjal Kulkarni is a document available to read on EtoBox.

The study evaluates the effectiveness of deep learning (DL) models, specifically deep neural networks (DNN), temporal convolution networks (TCN), and long short-term memory networks (LSTM), for estimating daily reference evapotranspiration (ETo) using limited meteorological data in Northeast China. The results indicate that TCN and LSTM outperformed classical machine learning models and empirical equations in both single weather station tests and grouped station tests. Overall, the research highlights the p

Author

Pranjal Kulkarni

Language

EN