论文标题
时间序列和机器学习以预测卫星数据的水质
Time series and machine learning to forecast the water quality from satellite data
论文作者
论文摘要
在沿海地区管理水质量应该是公民和公职人员的核心问题。遥感可以有助于沿海水和污染物的管理和监测。藻华是一种沿海污染物，是引起关注的原因。许多卫星数据（例如MODIS）已用于生成水质产品，以检测诸如叶绿素A（CHL-A）的花朵，称为荧光线高（FLH）和海面温度（SST）的光合作用指数。重要的是要使用这些产品的数学模型来表征这些水质产品的空间和时间变化。但是，为了监视，污染控制委员会将需要对任何污染的现象和预测。因此，我们旨在预测水的MODIS CHL-A，FLH和SST的未来值。这不仅限于一种水，而是涵盖不同类型的深度和浊度变化的水。这是非常重要的，因为CHL-A，FLH和SST的时间趋势取决于地理空间和水性质。为此，我们将每个像素的时间序列分解为几个组成部分：趋势，年内变化，季节性周期和随机静止。我们探索了三个这样的时间序列机器学习模型，这些模型可以表征非平稳时间序列数据并预测未来值，包括季节性Arima（自动回归整合移动平均线）（SARIMA）（SARIMA），回归和神经网络。结果表明，所有这些方法在对CHL-A，FLH和SST时间序列进行建模方面均有效，并可以很好地预测值。但是，发现回归和神经网络在所有类型的水（浑浊和浅层）中预测CHL-A方面是最好的。同时，Sarima模型提供了FLH和SST的最佳预测。
Managing the quality of water for present and future generations of coastal regions should be a central concern of both citizens and public officials. Remote sensing can contribute to the management and monitoring of coastal water and pollutants. Algal blooms are a coastal pollutant that is a cause of concern. Many satellite data, such as MODIS, have been used to generate water-quality products to detect the blooms such as chlorophyll a (Chl-a), a photosynthesis index called fluorescence line height (FLH), and sea surface temperature (SST). It is important to characterize the spatial and temporal variations of these water quality products by using the mathematical models of these products. However, for monitoring, pollution control boards will need nowcasts and forecasts of any pollution. Therefore, we aim to predict the future values of the MODIS Chl-a, FLH, and SST of the water. This will not be limited to one type of water but, rather, will cover different types of water varying in depth and turbidity. This is very significant because the temporal trend of Chl-a, FLH, and SST is dependent on the geospatial and water properties. For this purpose, we will decompose the time series of each pixel into several components: trend, intra-annual variations, seasonal cycle, and stochastic stationary. We explore three such time series machine learning models that can characterize the non-stationary time series data and predict future values, including the Seasonal ARIMA (Auto Regressive Integrated Moving Average) (SARIMA), regression, and neural network. The results indicate that all these methods are effective at modelling Chl-a, FLH, and SST time series and predicting the values reasonably well. However, regression and neural network are found to be the best at predicting Chl-a in all types of water (turbid and shallow). Meanwhile, the SARIMA model provides the best prediction of FLH and SST.
