Research progress in water quality prediction based on deep learning technology: a review.
Environ Sci Pollut Res Int
; 31(18): 26415-26431, 2024 Apr.
Article
en En
| MEDLINE
| ID: mdl-38538994
ABSTRACT
Water, an invaluable and non-renewable resource, plays an indispensable role in human survival and societal development. Accurate forecasting of water quality involves early identification of future pollutant concentrations and water quality indices, enabling evidence-based decision-making and targeted environmental interventions. The emergence of advanced computational technologies, particularly deep learning, has garnered considerable interest among researchers for applications in water quality prediction because of its robust data analytics capabilities. This article comprehensively reviews the deployment of deep learning methodologies in water quality forecasting, encompassing single-model and mixed-model approaches. Additionally, we delineate optimization strategies, data fusion techniques, and other factors influencing the efficacy of deep learning-based water quality prediction models, because understanding and mastering these factors are crucial for accurate water quality prediction. Although challenges such as data scarcity, long-term prediction accuracy, and limited deployments of large-scale models persist, future research aims to address these limitations by refining prediction algorithms, leveraging high-dimensional datasets, evaluating model performance, and broadening large-scale model application. These efforts contribute to precise water resource management and environmental conservation.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Calidad del Agua
/
Aprendizaje Profundo
Idioma:
En
Revista:
Environ Sci Pollut Res Int
Asunto de la revista:
SAUDE AMBIENTAL
/
TOXICOLOGIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Alemania