Modelos inteligentes para la mejora de estimaciones y predicciones agrometeorológicas

Resumen

1. Introducción o motivación de la tesis: La población mundial, en continuo crecimiento, alcanzará de forma estimada los 9,7 mil millones de habitantes en el 2050. Este incremento, combinado con el aumento en los estándares de vida y la situación de emergencia climática (aumento de la temperatura, intensificación del ciclo del agua, etc.) nos enfrentan al enorme desafío de gestionar de forma sostenible los cada vez más escasos recursos disponibles. El sector agrícola tiene que afrontar retos tan importantes como la mejora en la gestión de los recursos naturales, la reducción de la degradación medioambiental o la seguridad alimentaria y nutricional. Todo ello condicionado por la escasez de agua y las condiciones de aridez: factores limitantes en la producción de cultivos. Para garantizar una producción agrícola sostenible bajo estas condiciones, es necesario que todas las decisiones que se tomen estén basadas en el conocimiento, la innovación y la digitalización de la agricultura de forma que se garantice la resiliencia de los agroecosistemas, especialmente en entornos áridos, semi-áridos y secos sub-húmedos en los que el déficit de agua es estructural. Por todo esto, el presente trabajo se centra en la mejora de la precisión de los actuales modelos agrometeorológicos, aplicando técnicas de inteligencia artificial. Estos modelos pueden proporcionar estimaciones y predicciones precisas de variables clave como la precipitación, la radiación solar y la evapotranspiración de referencia. A partir de ellas, es posible favorecer estrategias agrícolas más sostenibles, gracias a la posibilidad de reducir el consumo de agua y energía, por ejemplo. Además, se han reducido el número de mediciones requeridas como parámetros de entrada para estos modelos, haciéndolos más accesibles y aplicables en áreas rurales y países en desarrollo que no pueden permitirse el alto costo de la instalación, calibración y mantenimiento de estaciones meteorológicas automáticas completas. Este enfoque puede ayudar a proporcionar información valiosa a los técnicos, agricultores, gestores y responsables políticos de la planificación hídrica y agraria en zonas clave. 2.Contenido de la investigación: Esta tesis doctoral ha desarrollado y validado nuevas metodologías basadas en inteligencia artificial que han ser vido para mejorar la precision de variables cruciales en al ámbito agrometeorológico: precipitación, radiación solar y evapotranspiración de referencia. En particular, se han modelado sistemas de predicción y rellenado de huecos de precipitación a diferentes escalas utilizando redes neuronales. También se han desarrollado modelos de estimación de radiación solar utilizando exclusivamente parámetros térmicos y validados en zonas con características climáticas similares a lugar de entrenamiento, sin necesidad de estar geográficamente en la misma región o país. Analógamente, se han desarrollado modelos de estimación y predicción de evapotranspiración de referencia a nivel local y regional utilizando también solamente datos de temperatura para todo el proceso: regionalización, entrenamiento y validación. Y finalmente, se ha creado una librería de Python de código abierto a nivel internacional (AgroML) que facilita el proceso de desarrollo y aplicación de modelos de inteligencia artificial, no solo enfocadas al sector agrometeorológico, sino también a cualquier modelo supervisado que mejore la toma de decisiones en otras áreas de interés. 3.Conclusión: Durante esta tesis doctoral se han desarrollado varias estrategias para superar las estimaciones y predicciones agrometeorológicas, enfocándose no solo en mejorar la precisión sino también en minimizar el número de parámetros necesarios. Así, solo se han evaluado enfoques basados en la temperatura, ya que la temperatura es la métrica más utilizada en las estaciones meteorológicas, además de ser la medida más barata y confiable, incluso cuando se mide con dispositivos de bajo coste. Debido a su importancia en sectores como la agronomía, la hidrología o el sector energético, entre otros, las variables agrometeorológicas modeladas han sido la precipitación, la radiación solar y la evapotranspiración de referencia En segundo lugar, se han probado diferentes modelos de aprendizaje automático en diferentes escenarios, como MLP, ELM, SVM, RF y XGBoost, entre otros. Existen diferencias en el rendimiento dependiendo de la naturaleza de la variable objetivo (precipitación, radiación solar y evapotranspiración de referencia) y otros factores como el índice de aridez o la proximidad al mar. En términos generales, se podría afirmar que MLP parece obtener los resultados más precisos para las estimaciones de precipitación, SVM generalmente supera a MLP al estimar la radiación solar y MLP y ELM son los más adecuados para predecir y pronosticar ET0. Vale la pena destacar que modelos como RF, XGBoost y modelos basados en transformaciones, a pesar de tener un coste computacional muy alto, no mejoran el rendimiento del resto de los modelos, obteniendo incluso peores resultados en algunos casos. En tercer lugar, se han evaluado diferentes enfoques en términos de entrenamiento. Por un lado, un algoritmo de calibración local obtiene los mejores resultados en general ya que los modelos se entrenan en las mismas condiciones climáticas y en el mismo sitio. Esto puede dar lugar al problema de falta de generalización en los modelos, ya que estos modelos están diseñados para ser utilizados en una ubicación específica. Sin embargo, su implementación en ubicaciones nuevas y no vistas con características geoclimáticas similares aún puede producir estimaciones precisas . 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