Kim Jaeyoung, Seo Dongil
Department of Environmental Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon 34134, Korea.
Department of Environmental Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon 34134, Korea.
Water Res. 2024 Mar 1;251:121125. doi: 10.1016/j.watres.2024.121125. Epub 2024 Jan 9.
This research introduces a comprehensive methodology to enhance hyperspectral image data (HSD) utility, specifically focusing on the three-dimensional (3-D) augmentation of Chlorophyll-a (Chl-a). This study comprises three significant steps: (1) the augmentation of limited field water quality data in terms of time interval and number of variables using neural network models, (2) the generation of 3-D data using numerical models, and (3) the extension of the hyperspectral image data into 3-D data using machine learning models. In the first phase, Multilayer Perceptron (MLP) models were developed to train water quality interactions and successfully generated high-frequency water quality data by adjusting biased measurements and predicting detailed water quality variables. In the second phase, high-frequency data generated by MLP models were applied to develop two numerical models. These numerical models successfully generated 3-D data, thereby demonstrating the effectiveness of integrating numerical modeling with neural networks. In the final phase, ten machine learning models were trained to generate 3-D Chl-a data from HSD. Notably, the Gaussian Process Regression model exhibited superior performance, effectively estimating 3-D Chl-a data with robust accuracy, as evidenced by an R-square value of 0.99. The findings align with theories of algal bloom dynamics, further validating the effectiveness of the approach. This study demonstrated the successful integrated development for HSD extension using machine learning models, numerical models, and original HSD, highlighting the potential of such integrated methodologies in advancing water quality monitoring and estimation. Notably, the approach leverages readily accessible data, allowing for the swift generation of results and bypassing time-consuming data collection processes. This research marks a significant step towards more robust, comprehensive water quality monitoring and prediction, thereby facilitating better management of aquatic ecosystems.
本研究引入了一种全面的方法来提高高光谱图像数据（HSD）的效用，特别关注叶绿素-a（Chl-a）的三维（3-D）增强。本研究包括三个重要步骤：（1）使用神经网络模型在时间间隔和变量数量方面增强有限的现场水质数据；（2）使用数值模型生成三维数据；（3）使用机器学习模型将高光谱图像数据扩展为三维数据。在第一阶段，开发了多层感知器（MLP）模型来训练水质相互作用，并通过调整有偏差的测量值和预测详细的水质变量成功生成了高频水质数据。在第二阶段，将MLP模型生成的高频数据应用于开发两个数值模型。这些数值模型成功生成了三维数据，从而证明了将数值建模与神经网络相结合的有效性。在最后阶段，训练了十个机器学习模型，以从HSD生成三维Chl-a数据。值得注意的是，高斯过程回归模型表现出卓越的性能，以稳健的精度有效估计三维Chl-a数据，决定系数R平方值为0.99证明了这一点。研究结果与藻华动态理论一致，进一步验证了该方法的有效性。本研究展示了使用机器学习模型、数值模型和原始HSD成功进行HSD扩展的综合开发，突出了这种综合方法在推进水质监测和估计方面的潜力。值得注意的是，该方法利用了易于获取的数据，能够快速生成结果并绕过耗时的数据收集过程。这项研究朝着更强大、更全面的水质监测和预测迈出了重要一步，从而有助于更好地管理水生生态系统。
