Temporal Prediction of Coastal Water Quality Based on Environmental Factors with Machine Learning
Temporal Prediction of Coastal Water Quality Based on Environmental Factors with Machine Learning
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DOI：
10.3390/jmse11081608
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发表时间：
2023-08
影响因子：
2.9
通讯作者：
Junan Lin;Qianqian Liu;Yang Song;Jiting Liu;Yixue Yin;N. Hall
中科院分区：
文献类型：
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作者：
Junan Lin;Qianqian Liu;Yang Song;Jiting Liu;Yixue Yin;N. Hall
准确的水华预测可以为水资源管理提供有用的信息。然而，环境变量和水华之间的复杂关系使预测具有挑战性。在这项研究中，我们建立了一个管道，结合四个常用的机器学习模型，支持向量回归（SVR），随机森林回归（RFR），小波分析（WA）-反向传播神经网络（BPNN）和WA-长短期记忆（LSTM），预测叶绿素a在沿海沃茨。两个地区具有独特的环境特征，纽斯河口，NC，美国-机器学习模型首次应用于短期藻类水华预测在单站和斯克里普斯码头，CA，美国，被选中。应用管道，我们可以很容易地从NRE预测切换到斯克里普斯码头预测与最小的模型调整。该管道成功地预测了这两个地区藻类水华的发生，使用WA-LSTM和WA-BPNN比SVR和RFR更具鲁棒性。管道允许我们通过尝试不同数量的神经元隐藏层来找到最佳结果。这条管道很容易适应其他沿海地区。两个研究区域的经验表明，丰富的数据集，包括占主导地位的物理过程是必要的，以提高叶绿素预测时，将其应用到其他水生系统。
The accurate forecast of algal blooms can provide helpful information for water resource management. However, the complex relationship between environmental variables and blooms makes the forecast challenging. In this study, we build a pipeline incorporating four commonly used machine learning models, Support Vector Regression (SVR), Random Forest Regression (RFR), Wavelet Analysis (WA)-Back Propagation Neural Network (BPNN) and WA-Long Short-Term Memory (LSTM), to predict chlorophyll-a in coastal waters. Two areas with distinct environmental features, the Neuse River Estuary, NC, USA—where machine learning models are applied for short-term algal bloom forecast at single stations for the first time—and the Scripps Pier, CA, USA, are selected. Applying the pipeline, we can easily switch from the NRE forecast to the Scripps Pier forecast with minimum model tuning. The pipeline successfully predicts the occurrence of algal blooms in both regions, with more robustness using WA-LSTM and WA-BPNN than SVR and RFR. The pipeline allows us to find the best results by trying different numbers of neuron hidden layers. The pipeline is easily adaptable to other coastal areas. Experience with the two study regions demonstrated that enrichment of the dataset by including dominant physical processes is necessary to improve chlorophyll prediction when applying it to other aquatic systems.