Machine learning-based design and monitoring of algae blooms: Recent trends and future perspectives - A short review

Machine learning (ML) models are widely used methods for analyzing data from sensors and satellites to monitor climate change, predict natural disasters, and protect wildlife. However, the application of these technologies for monitoring and managing algal blooms in freshwater environments is relatively new and novel. The commonly used models in algal blooms (ABS) so far are artificial neural networks (ANN), random forests (RF), support vector machine (SVM), data-driven modeling, and long short-term memory (LSTM). In the past, researchers have mostly worked on predicting the effluent parameters, nutrients, microculture, area and weather conditions, meteorological factors, ground waters, energy optimization, and metallic substances in algal blooms using ML models. Most of the studies have employed performance metrics like root mean squared error, mean squared error, peak signal, precision, and determination coefficient as their primary model performance measures for accuracy analysis, and the usage of transfer, and activation function. While there have been some studies on this topic, several research gaps are still to be addressed. The most significant gaps are related to the limited application of ML in different algae bloom scenarios, the interpretability of ML models, and the lack of integration with existing monitoring systems. Keeping these in mind, this review article has been methodically arranged to present an overview of the past studies, their limitations, and the way forward toward the application of ML in the prediction of ABS, thus benefitting future researchers in this area. This review aims to summarize the data that are available, including some benchmarking values.

Automated summary

Machine learning models are widely used for various applications, including climate change monitoring, natural disaster prediction, and wildlife protection, by analyzing data from sensors and satellites. However, the use of these technologies for monitoring and managing algal blooms in freshwater environments is relatively new. The commonly used models in algal blooms are artificial neural networks, random forests, support vector machine, data-driven modeling, and long short-term memory. This review article aims to provide an overview of past studies, their limitations, and the way forward in applying machine learning to predict algal blooms, benefiting future researchers in this field.