Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA E-mail:
J Water Health. 2021 Apr;19(2):254-266. doi: 10.2166/wh.2021.251.
This paper presents a hybrid model for predicting oyster norovirus outbreaks by combining the Artificial Neural Networks (ANNs) and Principal Component Analysis (PCA) methods and using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote-sensing data. Specifically, 10 years (2007-2016) of cloud-free MODIS Aqua data for water leaving reflectance and environmental data were extracted from the center of each oyster harvest area. Then, the PCA was utilized to compress the size of the MODIS Aqua data. An ANN model was trained using the first 4 years of the data from 2007 to 2010 and validated using the additional 6 years of independent datasets collected from 2011 to 2016. Results indicated that the hybrid PCA-ANN model was capable of reproducing the 10 years of historical oyster norovirus outbreaks along the Northern Gulf of Mexico coast with a sensitivity of 72.7% and specificity of 99.9%, respectively, demonstrating the efficacy of the hybrid model.
本文提出了一种通过结合人工神经网络（ANNs）和主成分分析（PCA）方法，并使用中分辨率成像光谱仪（MODIS）卫星遥感数据来预测牡蛎诺如病毒爆发的混合模型。具体来说，从每个牡蛎收获区的中心提取了 10 年（2007-2016 年）无云 MODIS Aqua 数据的水反射率和环境数据。然后，利用 PCA 压缩 MODIS Aqua 数据的大小。使用 2007 年至 2010 年的数据对 ANN 模型进行训练，并使用 2011 年至 2016 年收集的另外 6 年独立数据集进行验证。结果表明，混合 PCA-ANN 模型能够再现墨西哥湾北部沿海地区 10 年来牡蛎诺如病毒爆发的情况，其敏感性分别为 72.7%和特异性为 99.9%，证明了该混合模型的有效性。
