ETo is forecasted up to seven days ahead using deep learning and machine learning. Three forecasting strategies (iterated, direct and MIMO) are assessed.

This study assesses the potential of deep learning (long short-term memory (LSTM), one-dimensional convolutional neural network (1D CNN) and a combination of ...

In general, MIMO was the best forecasting strategy, offering good performance and lower computational cost. The deep learning models performed slightly better ...

In this study, daily ETo is forecasted for multi-step (ie, 1-, 3-, 7-, and 10-day) ahead at 30 sites across the contiguous United States (CONUS) by three ...

Multi-step ahead forecasting of daily reference evapotranspiration using deep learning. https://doi.org/10.1016/j.compag.2020.105728 ·. Journal: Computers and ...

The daily reference evapotranspiration (ETo) must be accurately forecasted to improve real-time irrigation scheduling and decision-making for water ...

Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China. Article. Nov 2023 ...

This study provides daily prediction and multi-step forward forecasting of ET 0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi ...

According to the testing results, the hybrid MVMD-RR-KELM models had superior performance than other AI models for forecasting three and seven days ahead ETo.

Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the ...