TL;DR
The findings demonstrate the potential of combining convolution-based feature extraction with attention-based modeling for accurate one-month-ahead monthly ET0 forecasting under the climatic conditions investigated in this study.
Precision irrigation has emerged as a critical adaptation strategy for alleviating global water scarcity under climate change, relying heavily on the accurate estimation of reference evapotranspiration (ET 0 ). Although physically based models, such as the FAO-56 Penman-Monteith equation, serve as the global standa...
Reference evapotranspiration (ETo) is a fundamental parameter for irrigation scheduling and efficient water resources management. This study aimed to compare the performance of Linear Regression (LR), second-order Polynomial Regression (PR), Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), and A...
Precipitation forecast is crucial for water resource management, agriculture, and climate disaster mitigation. This study investigates the use of artificial intelligence (AI) to predict seasonal precipitation in the state of Minas Gerais, Brazil. Utilizing neural networks trained on high temporal and spatial resolution...
Analyzing the performance of several forecasting models applied to Standardized Precipitation Index time series for the 1967–2021 period revealed a clear scale-dependent pattern: absolute forecast accuracy increased with the SPI aggregation scale—alongside more pronounced temporal persistence—though this did not necess...
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