AI-based Deep Soil Moisture Prediction to Assess Past and Future Consistency of NOAA Data

• Guillermo Montilla Yttrium-Technology Corp., Panama City, Panama https://orcid.org/0000-0002-1777-6353
• Sergio Villazana Centre of Image Processing, Faculty of Engineering of the University of Carabobo, Valencia, Venezuela https://orcid.org/0000-0002-4495-1479
• Vicente Torres National Academy of Research and Development, Cuernavaca, Mexico. Faculty of Engineering, DICT, National Autonomous University of Mexico https://orcid.org/0009-0006-1888-8740
• Egilda Pérez Centre of Image Processing, Faculty of Engineering of the University of Carabobo, Valencia, Venezuela https://orcid.org/0000-0002-0633-8200
• Héctor Reverón Yttrium-Technology Corp., Panama City, Panama https://orcid.org/0009-0009-6142-4900
• César Seijas Centre of Image Processing, Faculty of Engineering of the University of Carabobo, Valencia, Venezuela https://orcid.org/0000-0001-6050-4436

Keywords: deep soil moisture content, soil properties, artificial intelligence

Abstract

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.

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