An end-to-end process to achieve a complete framework methodology for Harmful Algal Bloom (HAB) growth prediction is crucial for water management, especially in implementing robust predictive modelling of HAB to prevent water pollution. Previous works have separately focused on the prediction part or the implementation of the water monitoring system that involves the integration of sensors through the Internet of Things (IoT). These studies lack in terms of discussion of both IoT with the algae

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... ecological domain and prediction method. Therefore, this paper takes the initiative to provide a wider coverage on the end-to-end process including the assembly and integration of sensors, data acquisition and predictive modelling using data-driven approaches, for example, machine learning, deep learning and deep time series forecasting algorithm for future algal bloom outbreak mitigation. This paper believes that discussion in a complete framework perspective based on the execution of each phase is important besides providing a true understanding of the algae growth factors and prediction problems to achieve a robust prediction algorithm for algal growth. In the end, this paper presents proof that selecting the right features and utilising time series with deep learning are much better for tackling the issues of highly non-linear and dynamic algae ecological data that are briefly introduced in this paper. Among all the algorithms selected, Long Short-term Memory (LSTM) is the best fit for the prediction method and has outperformed other basic machine learning methods in accurately predicting algal growth through the prediction of chlorophyll-a (Chl-a) as a strong indicator of algal presence for coastal studies. INDEX TERMS Deep learning, harmful algal bloom (HAB), IoT, long short-term memory (LSTM), machine learning, time series forecasting.