This is the code repository as part of the 2022-2023 Q2 edition of the Research Project of TU Delft.
An algal bloom is defined as a rapid increase in common algae (phytoplankton) abundance in water bodies and it can occur when a group of certain environmental factors is combined. If the algae populations grow out of control, such algal blooms become problematic and cause damage to the ecosystem, such phenomena are called harmful algal blooms. For this reason, it is important to detect and forecast these phenomena to be able to take action beforehand. Remote sensing is measuring and monitoring the characteristics of an area at a distance and it is typically done by satellites. Remote sensed data containing various environmental measurements can be used as the input for a machine learning system to estimate chlorophyll-a concentrations which is the main indicator used for detecting algal blooms. The main question this research aims to answer is: Which input modality is the most predictive for estimating chlorophyll-a concentrations for water bodies in Uruguay? This research presents the step-by-step construction of a system to pre-process the environmental data collected through remote sensing and use this data to train and test a machine learning system to assess and compare 11 different environmental factors or so-called data modalities individually against each other to find out the most predictive one. Carrying out the machine learning experiments brings the results into the open that radiation mean and turbidity of water are the two most predictive data modalities for algal bloom forecasting with accuracy scores of approximately 34%, while radiation mean is performing slightly better.
The Research paper can be found at: http://resolver.tudelft.nl/uuid:22dfe743-8c33-437b-9106-6a5b533a79c4