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.
摘要：
水,一种宝贵且不可再生的资源，在人类生存和社会发展中起着不可或缺的作用。水质的准确预测涉及对未来污染物浓度和水质指数的早期识别,能够基于证据的决策和有针对性的环境干预。先进计算技术的出现，特别是深度学习，由于其强大的数据分析能力，在水质预测中的应用引起了研究人员的极大兴趣。本文全面回顾了深度学习方法在水质预测中的部署，包括单模型和混合模型方法。此外,我们描述了优化策略，数据融合技术，以及影响基于深度学习的水质预测模型效果的其他因素，因为理解和掌握这些因素对于准确的水质预测至关重要。尽管数据稀缺等挑战，长期预测精度，大规模模型的有限部署持续存在，未来的研究旨在通过改进预测算法来解决这些局限性，利用高维数据集，评估模型性能，扩大大规模模型应用。这些努力有助于精确的水资源管理和环境保护。
