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What is Machine Learning for Algal Bloom Prediction about?

This study applied various Machine Learning (ML) algorithms to analyze eutrophication and predict algal blooms in the Han River, South Korea, using a comprehensive dataset of 20 water quality parameters collected over 10 years. The adaptive neuro-fuzzy inference system (ANFIS) was found to be the most effective model for predicting chlorophyll-derived trophic index, demonstrating high accuracy in both regression and classification tasks. A user-friendly web application was developed to assist users in predi

Author

Minh Tiến Lê

Language

EN