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Harnessing Neural Networks to Predict Water Quality Dynamics: State-of-the-Art Developments and Emerging Directions24
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La degradación de los ecosistemas de agua dulce ha intensificado la necesidad de contar con herramientas precisas y oportunas para la predicción de la calidad del agua. Los modelos estadísticos y mecanísticos tradicionales suelen tener dificultades para capturar los procesos no lineales y altamente dinámicos que caracterizan a los entornos acuáticos. Las redes neuronales han surgido como alternativas prometedoras, capaces de integrar fuentes de datos heterogéneas —incluyendo sensores in situ, redes IoT y percepción remota por satélite o drones— para modelar patrones biogeoquímicos complejos. Esta revisión sintetiza más de 450 estudios para evaluar el panorama actual de aplicaciones de aprendizaje profundo en la predicción de oxígeno disuelto, clorofila-a, turbidez, nutrientes y floraciones algales nocivas. Evaluamos el desempeño de arquitecturas como Long Short-Term Memory, Gated Recurrent Units, Convolutional Neural Networks, ConvLSTM, Transformers, Graph Neural Networks y modelos híbridos basados en descomposición, con énfasis en el preprocesamiento de datos, la fusión multimodal, los retos de resolución temporal y técnicas de Inteligencia Artificial Explicable como Shapley Additive Explanations, mecanismos de atención y Gradient-weighted Class Activation Mapping. En la literatura revisada, las redes neuronales superan con frecuencia a los modelos convencionales, especialmente cuando se aprovechan entradas multisource y tuberías híbridas de modelado. Los desafíos pendientes incluyen la interpretabilidad, la reproducibilidad, la escasez de datos y la limitada generalización entre regiones. Las líneas futuras de investigación destacan el aprendizaje basado en principios físicos, el aprendizaje por transferencia y federado, el modelado multi-profundidad y la aplicación de estos marcos para predecir el destino y transporte de entidades emergentes (p. ej., farmacéuticos, microplásticos) en un clima cambiante.
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