Yoon Sunghyun, Ahn Kuk-Hyun
Department of Artificial Intelligence, Kongju National University, Cheon-an, South Korea.
Department of Civil and Environmental Engineering, Kongju National University, Cheon-an, South Korea.
Sci Total Environ. 2025 Jun 1;979:179481. doi: 10.1016/j.scitotenv.2025.179481. Epub 2025 Apr 24.
Accurate estimation of harmful algal blooms is essential for protecting surface water. Chlorophyll-a (Chl-a), commonly used as a proxy for estimating algal concentration, is influenced by a broad range of weather and physicochemical factors that operate across various spatial and temporal scales. This study aims to propose a deep learning (DL)-based framework for long-term Chl-a simulation, consisting of two separate blocks for processing multi-modal sources together: one for incorporating irregularly measured water quality observations and the other for integrating climate data measured at constant time steps. Besides a fully connected network for encoding irregular water quality observations, we benchmark several state-of-the-art graph neural network (GNN) architectures, including ChebNet and Graph Convolutional Network (GCN), for encoding continuous climate data. Specifically, we represent water quality stations as nodes in a graph, model the spatiotemporal dependencies between these nodes, and utilize the learned relationships to predict Chl-a simulations simultaneously across all nodes in the graph. Additionally, we introduce a gating mechanism to integrate the outputs from the two blocks. The performance of advanced GNN models is evaluated using a daily dataset from the upper Han River basins in South Korea. The results indicate that our proposed models are promising, outperforming several baseline models developed for similar objectives with improvements up to 47 % in the R. In particular, the combination of the GCN algorithm with Long Short-Term Memory (LSTM) in our DL framework achieves superior performance. We then conduct further analyses to assess the effectiveness of the gating mechanism, revealing that it enhances prediction performance by achieving a 12 % improvement in the R compared to the model without the gating mechanism. We conclude that the proposed GNN-variant framework shows promise as a robust machine learning-based approach for aggregating spatiotemporal information to achieve reliable Chl-a predictions.
准确估计有害藻华对于保护地表水至关重要。叶绿素a（Chl-a）通常用作估计藻类浓度的替代指标，它受到广泛的天气和物理化学因素影响，这些因素在不同的空间和时间尺度上起作用。本研究旨在提出一个基于深度学习（DL）的长期Chl-a模拟框架，该框架由两个独立的模块组成，用于共同处理多模态源数据：一个用于纳入不规则测量的水质观测数据，另一个用于整合按固定时间步长测量的气候数据。除了用于编码不规则水质观测数据的全连接网络外，我们还对几种先进的图神经网络（GNN）架构进行了基准测试，包括ChebNet和图卷积网络（GCN），用于编码连续的气候数据。具体而言，我们将水质监测站表示为图中的节点，对这些节点之间的时空依赖性进行建模，并利用学到的关系同时预测图中所有节点的Chl-a模拟值。此外，我们引入了一种门控机制来整合两个模块的输出。使用韩国汉江上游流域的每日数据集评估了先进GNN模型的性能。结果表明，我们提出的模型很有前景，优于为类似目标开发的几个基线模型，相关系数（R）提高了47%。特别是，我们的深度学习框架中将GCN算法与长短期记忆网络（LSTM）相结合取得了卓越的性能。然后，我们进行了进一步分析以评估门控机制的有效性，结果表明，与没有门控机制的模型相比，它将相关系数（R）提高了12%，从而提高了预测性能。我们得出结论，所提出的GNN变体框架作为一种强大的基于机器学习的方法，有望聚合时空信息以实现可靠的Chl-a预测。
