AI-based Deep Soil Moisture Prediction to Assess Past and Future Consistency of NOAA Data - Montilla, Guillermo; Villazana, Sergio; Torres, Vicente; Pérez, Egilda; Reverón, Héctor; Seijas, César (Revista de la Facultad de Agronomía de la Universidad del Zulia, 2025)
Accurate deep soil moisture modeling is essential for agriculture, especially in regions with unpredictable precipitation impacting crop health and yield. Understanding these dynamics is crucial for assessing drought vulnerability and promoting sustainable agricultural practices. This study evaluated the consistency of NOAA climate data over five years using an AI-developed deep soil moisture model. The objectives were to assess historical and future NOAA data reliability and predict soil moisture at 40 cm and 100 cm depths in Panama’s western region. The CNN-BiLSTM regression model integrated meteorological and soil property data (clay, silt, sand) from NOAA and the International Soil Reference and Information Centre. It transformed data into spatial and temporal features, with training, validation, and testing sets using 2021 data. The generalization capability of the model was assessed using data from 2019, 2020, 2022, and 2023, validating predictions with two preceding and two subsequent years. Results show the 40 cm model achieved MAE, RMSE, MAPE, and R2 of 0.007112 m3.m-3, 0.012662 m3.m-3, 3.55 %, and 0.97, respectively. The 100 cm model recorded 0.011019 m3.m-3, 0.017334 m3.m-3, 4.92 %, and 0.95, respectively. The model demonstrated coherence over five years, confirming NOAA data consistency.