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Machine learning approaches to coastal water quality monitoring using GOCI satellite data

机译：使用GOCI卫星数据进行沿海水质监测的机器学习方法

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Since coastal waters are one of the most vulnerable marine systems to environmental pollution, it is very important to operationally monitor coastal water quality. This study attempts to estimate two major water quality indicators, chlorophyll-a (chl-a) and suspended particulate matter (SPM) concentrations, in coastal environments on the west coast of South Korea using Geostationary Ocean Color Imager (GOCI) satellite data. Three machine learning approaches including random forest, Cubist, and support vector regression (SVR) were evaluated for coastal water quality estimation. In situ measurements (63 samples) collected during four days in 2011 and 2012 were used as reference data. Due to the limited number of samples, leave-one-out cross validation (CV) was used to assess the performance of the water quality estimation models. Results show that SVR outperformed the other two machine learning approaches, yielding calibration R~2 of 0.91 and CV root-mean-squared-error (RMSE) of 1.74 mg/ m~3 (40.7%) for chl-a, and calibration R~2 of 0.98 and CV RMSE of 11.42 g/m~3 (63.1%) for SPM when using GOCI-derived radiance data. Relative importance of the predictor variables was examined. When GOCI-derived radiance data were used, the ratio of band 2 to band 4 and bands 6 and 5 were the most influential input variables in predicting chl-a and SPM concentrations, respectively. Hourly available GOCI images were useful to discuss spatiotemporal distributions of the water quality parameters with tidal phases in the west coast of Korea.
机译：由于沿海水域是最容易受到环境污染的海洋系统之一，因此在运营中监测沿海水质非常重要。这项研究试图使用地球静止海洋彩色成像仪（GOCI）卫星数据估算韩国西海岸沿海环境中的两个主要水质指标叶绿素a（chl-a）和悬浮颗粒物（SPM）浓度。评估了三种机器学习方法，包括随机森林，立体主义者和支持向量回归（SVR），以评估沿海水质。在2011年和2012年的四天内收集的现场测量结果（63个样本）用作参考数据。由于样品数量有限，因此采用留一法交叉验证（CV）来评估水质估算模型的性能。结果表明，SVR优于其他两种机器学习方法，对chl-a的校准R〜2为0.91，CV均方根误差（RMSE）为1.74 mg / m〜3（40.7％），并且校准R使用GOCI得出的辐射数据时，SPM的〜2为0.98，CV RMSE为11.42 g / m〜3（63.1％）。检查了预测变量的相对重要性。当使用GOCI衍生的辐射数据时，在预测chl-a和SPM浓度时，频带2与频带4的比率以及频带6和5分别是影响最大的输入变量。每小时可获得的GOCI图像有助于讨论韩国西海岸潮汐阶段水质参数的时空分布。

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