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 backpropagation (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. Keywords-harmful algal blooms; machine learnning; artificial neural network; back-propagation neural network; generalized regression neural network; support vector machineI.

New Algorithm for Evaluating the Green Supply Chain Performance in an Uncertain Environment

“…These data can be represented as follows: As noted in the previous discussion, in GA, the population size (55), the covariations' coefficient (0.068), the evolution (500), and the cross coefficient value (0.75) were set. The node numbers of the input (17) and hidden layers (20) were obtained. The output layer node number was one.…”

“…These data can be represented as follows: As noted in the previous discussion, in GA, the population size (55), the covariations' coefficient (0.068), the evolution (500), and the cross coefficient value (0.75) were set. The node numbers of the input (17) and hidden layers (20) To further examine the performance of the network after training, further simulation analysis was conducted based on the training results. Based on the output and target values, a linear regression analysis was conducted, and the analysis results are shown in Figure 4.…”

“…Therefore, a GA-LMBP neural network algorithm has been proposed. In fact, it has been used in many areas such as predicting water-assisted injection [15], analog circuit fault diagnosis [16], and harmful algal blooms prediction [17]. Moreover, the GA-LMBP neural network algorithm has also been used to evaluate passenger comfort with respect to vibration in railway vehicles [18].…”

A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

“…SVM models have some advantages over artificial neural networks (ANNs; method described below) (Table 1), including good generalization ability, only requiring a small amount of training data and assurance of a global optimal solution [34]. Ribeiro and Torgo (2008) [32] compared the performance of three prediction models, SVM, ANN, and random forest (RF; method described below), on the task of predicting algal blooms.…”

“…The bloom/no bloom models, that were trained using only the presence samples, showed a high predictive performance, which was superior to an ANN used in a previous study [60], and thus could offer useful information to the local shellfish industry. Li et al (2014) [34] used monthly/biweekly water quality data to predict HABs in Tolo Harbour, Hong Kong, based on several machine learning methods, namely, SVM, FFNN, and a generalized regression neural network [61]. For both one-and two-weeks ahead predictions of chla, SVM showed the best performance among all the tested models, outperforming all the ANNs.…”