A Day at the Beach: Enabling Coastal Water Quality Prediction with High-Frequency Sampling and Data-Driven Models,Environmental Science & Technology

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A Day at the Beach: Enabling Coastal Water Quality Prediction with High-Frequency Sampling and Data-Driven Models
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2021-01-20 , DOI: 10.1021/acs.est.0c06742
Ryan T. Searcy ₁ , Alexandria B. Boehm ₁

Affiliation

To reduce the incidence of recreational waterborne illness, fecal indicator bacteria (FIB) are measured to assess water quality and inform beach management. Recently, predictive FIB models have been used to aid managers in making beach posting and closure decisions. However, those predictive models must be trained using rich historical data sets consisting of FIB and environmental data that span years, and many beaches lack such data sets. Here, we investigate whether water quality data collected during discrete short duration, high-frequency beach sampling events (e.g., samples collected at sub-hourly intervals for 24–48 h) are sufficient to train predictive models that can be used for beach management. We use data collected during six high-frequency sampling events at three California marine beaches and train a total of 126 models using common data-driven techniques. Tide, solar irradiation, water temperature, significant wave height, and offshore wind speed were found to be the most important environmental variables in the models. We validate the predictive performance of models using withheld data. Random forests are consistently the top performing model type. Overall, we find that data-driven models trained using high-frequency FIB and environmental data perform well at predicting water quality and can be used to inform public health decisions at beaches.

中文翻译：

在海滩上度过一天：通过高频采样和数据驱动模型来实现沿海水质预测

为了减少娱乐性水传播疾病的发生，对粪便指示细菌（FIB）进行了测量，以评估水质并通知海滩管理人员。最近，预测性FIB模型已用于帮助经理制定海滩发布和关闭决策。但是，必须使用丰富的历史数据集（包括FIB和跨年的环境数据）来训练那些预测模型，而且许多海滩都缺乏此类数据集。在这里，我们调查在离散的短时间，高频海滩采样事件中收集的水质数据（例如，以亚小时为间隔24-48小时的采样）是否足以训练可用于海滩管理的预测模型。我们使用在三个加利福尼亚海洋海滩的六次高频采样事件中收集的数据，并使用常见的数据驱动技术训练了总共126个模型。潮汐，太阳辐射，水温，明显的波高和海上风速被认为是模型中最重要的环境变量。我们使用保留的数据验证模型的预测性能。随机森林始终是表现最佳的模型类型。总体而言，我们发现使用高频FIB和环境数据训练的数据驱动模型在预测水质方面表现良好，可用于为海滩的公共卫生决策提供依据。我们使用保留的数据验证模型的预测性能。随机森林始终是表现最佳的模型类型。总体而言，我们发现使用高频FIB和环境数据训练的数据驱动模型在预测水质方面表现良好，可用于为海滩的公共卫生决策提供依据。我们使用保留的数据验证模型的预测性能。随机森林始终是表现最佳的模型类型。总体而言，我们发现使用高频FIB和环境数据训练的数据驱动模型在预测水质方面表现良好，可用于为海滩的公共卫生决策提供依据。

更新日期：2021-02-02

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