English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Managing the quality of water for present and future generations of coastalregions should be a central concern of both citizens and public officials.Remote sensing can contribute to the management and monitoring of coastal waterand pollutants. Algal blooms are a coastal pollutant that is a cause ofconcern. Many satellite data, such as MODIS, have been used to generatewater-quality products to detect the blooms such as chlorophyll a (Chl-a), aphotosynthesis index called fluorescence line height (FLH), and sea surfacetemperature (SST). It is important to characterize the spatial and temporalvariations of these water quality products by using the mathematical models ofthese products. However, for monitoring, pollution control boards will neednowcasts and forecasts of any pollution. Therefore, we aim to predict thefuture values of the MODIS Chl-a, FLH, and SST of the water. This will not belimited to one type of water but, rather, will cover different types of watervarying in depth and turbidity. This is very significant because the temporaltrend of Chl-a, FLH, and SST is dependent on the geospatial and waterproperties. For this purpose, we will decompose the time series of each pixelinto several components: trend, intra-annual variations, seasonal cycle, andstochastic stationary. We explore three such time series machine learningmodels that can characterize the non-stationary time series data and predictfuture values, including the Seasonal ARIMA (Auto Regressive Integrated MovingAverage) (SARIMA), regression, and neural network. The results indicate thatall these methods are effective at modelling Chl-a, FLH, and SST time seriesand predicting the values reasonably well. However, regression and neuralnetwork 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 predictionof FLH and SST.

Time series and machine learning to forecast the water quality from satellite data