Harmful algal blooms prediction with machine learning models in Tolo Harbour

Abstract

Machine learning (ML) techniques such as artificial neural network (ANN) and support vector machine (SVM) have been increasingly used to predict harmful algal blooms (HABs). In this paper, we use the biweekly data in Tolo Harbour, Hong Kong, and choose several machine learning methods to develop prediction models of algal blooms. Three different kinds of models are designed based on back-propagation (BP) neural network, generalized regression neural network (GRNN) and support vector machine (SVM) respectively. The experimental results show that the improved BP algorithm and SVM work better than GRNN methods, and the models based on SVM present the best performance in terms of goodness-of-fit measures, but need to be further improved in the running time. We develop these prediction models with different lead time (7-day and 14-day) to study further. The results indicate that the use of biweekly data can simulate the general trend of algal biomass reasonably, but it is not ideally suited for exact predictions. The use of higher frequency data may improve the accuracy of the predictions.