Freshwater Algal Bloom Prediction by Support Vector Machine in Macau Storage Reservoirs

Abstract

Understanding and predicting dynamic change of algae population in freshwater reservoirs is particularly important, as algae-releasing cyanotoxins are carcinogens that would affect the health of public. However, the high complex nonlinearity of water variables and their interactions makes it difficult to model the growth of algae species. Recently, support vector machine (SVM) was reported to have advantages of only requiring a small amount of samples, high degree of prediction accuracy, and long prediction period to solve the nonlinear problems. In this study, the SVM-based prediction and forecast models for phytoplankton abundance in Macau Storage Reservoir (MSR) are proposed, in which the water parameters of pH, SiO2, alkalinity, bicarbonate(HCO3 -), dissolved oxygen (DO), total nitrogen (TN), UV254, turbidity, conductivity, nitrate, total nitrogen (TN), orthophosphate(PO4 3−), total phosphorus (TP), suspended solid (SS) and total organic carbon (TOC) selected from the correlation analysis of the 23 monthly water variables were included, with 8-year (2001–2008) data for training and the most recent 3 years (2009–2011) for testing. The modeling results showed that the prediction and forecast powers were estimated as approximately 0.76 and 0.86, respectively, showing that the SVM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir.

Harmful algal blooms prediction with machine learning models in Tolo Harbour

“…Thus, some researchers have introduced SVM into algal blooms prediction problems and validated the superiority of SVM over ANN. However, their studies are mostly applied to freshwater environments (i.e., reservoirs [9], limnological or riverine systems [10]) while almost no related research based on SVM are applied to marine eutrophic areas. Whether SVM can show competitive performance over ANN for coastal algal blooms prediction still needs more study.…”

Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater

“…These results further confirmed the historical effects on the model accuracy and generalization performance, and also implied that take the latest 3 months data as memorizing learning can improve the prediction power in the forecast model. Besides, further compared with our previous study [30] for the prediction and forecast of phytoplankton abundance using SVRonly with R 2 of 0.863, the present study integrating SVR and PSO as the optimization algorithm have better prediction power with R 2 , RMSE and MAE.…”

“…And then, the data process for both the prediction and forecast model follows the same steps as illustrated in [30]. After the data were processed, the performance of prediction and forecast models were shown in Table 2.…”

Data-Driven Soft Sensor Modeling for Algal Blooms Monitoring

“…Xie et al [21] develop a model to simulate the dynamic change of phytoplankton abundance based on 15 kinds of lab-analysis water parameter data, which can help to develop guidelines for monitoring algal blooms.…”