ABF: A data-driven approach for algal bloom forecasting using machine intelligence and remotely sensed data series

This paper presents a fully automated framework for algal bloom forecasting in inland water by combining remote sensing data series and unsupervised machine learning concepts. In contrast to other methods the specialized literature that usually employ pre-labeled data, proposed approach was designed be autonomous concerning pre-requisites, assuming as input only time of remotely sensed products forecast proliferation. more technical terms, machine-intelligent methodology comprises steps pre-processing, feature extraction modeling, it learns from past events predict future scenarios blooms, outputting insurgence maps.