A deep learning based framework for enhanced reference evapotranspiration estimation: evaluating accuracy and forecasting strategies

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 30, 2025

Affordable and efficient agricultural methods enhance crop yield water management by optimizing resources. Precise irrigation relies on accurate estimation of reference evapotranspiration (ETo). Numerous analytical empirical exist to compute ETo but these are costlier, requires time perform poorly under limited availability meteorological data. This study first evaluated the performances three deep learning sequential models-Long short-term memory (LSTM), Neural Basis Expansion Analysis for Time Series (N-BEATS) and, Temporal Convolutional Network model (TCN), predicting daily possessing temporal characteristics. In this TCN is considered as baseline be compared with other models. results, performed better, so it further utilized evaluate two strategies prediction that makes second objective paper. approach, historic data used predict future using which standard method. And, in recursive predicted climatological computed. required better planning data-scarce situations. The results demonstrate provided satisfactory performance Nash-Sutcliffe Efficiency (NSE) = 0.99, Theil U2 0.005, RMSE 0.092 MAE 0.048. Also, strategy, values computed found more than approach. Thus, comparative among architecture revealed outperformed LSTM N-BEATS models an method time-series could also assist precise resources scarcity.

Language: Английский

Estimation of daily reference crop evapotranspiration in China based on time-space-LSTM model

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133430 - 133430

Published: May 1, 2025

Language: Английский