Deep learning models analyzed for agricultural weather forecasting

A new research paper explores the effectiveness of deep learning models for meteorological forecasting in agriculture. The study compares recurrent architectures like GRU and LSTM with hybrid models such as 1D-CNN-GRU and 1D-CNN-LSTM. Using data from Ioannina, Greece, the models were evaluated for their ability to predict evapotranspiration, vapor pressure deficit, and wind speed for both 24-hour and 168-hour horizons. Hybrid CNN-GRU models showed the highest performance, particularly for short-term forecasting. AI

IMPACT This research could lead to more accurate weather predictions for agricultural planning and resource management.

RANK_REASON Research paper detailing a comparative analysis of deep learning models for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• 1D-CNN-GRU
• 1D-CNN-LSTM
• ERA5
• gated recurrent unit
• Ioannina, Greece
• long short-term memory
• Open-Meteo
• Sotirios Kontogiannis

• paper
• product

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