Algal bloom prediction by support vector machine and relevance vector machine with genetic algorithm optimization in freshwater reservoirs. Issue 2 (18th April 2017)

• Record Type: Journal Article
• Authors: Wang, Yanjie; Xie, Zhengchao; Lou, InChio; Ung, Wai Kin; Mok, Kai Meng
• Is Part Of: Engineering computations. Volume 34:Issue 2(2017)
• Page Start: 664
• Page End: 679
• Publication Date: 2017-04-18
• Subjects: Algal bloom -- GA-RVM -- GA-SVM -- Phytoplankton abundance -- Prediction and forecast models; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-11-2015-0356
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: The purpose of this paper is to examine the applicability and capability of models based on a genetic algorithm and support vector machine (GA-SVM) and a genetic algorithm and relevance vector machine (GA-RVM) for the prediction of phytoplankton abundances associated with algal blooms in a Macau freshwater reservoir, and compare their performances with an artificial neural network (ANN) model. Design/methodology/approach: The hybrid models GA-SVM and GA-RVM were developed for the optimal control of parameters for predicting (based on the current month's variables) and forecasting (based on the previous three months' variables) phytoplankton dynamics in a Macau freshwater reservoir, MSR, which has experienced cyanobacterial blooms in recent years. There were 15 environmental parameters, including pH, SiO2, alkalinity, bicarbonate (HCO3 −), dissolved oxygen (DO), total nitrogen (TN), UV254, turbidity, conductivity, nitrate (NO3 −), orthophosphate (PO4 3− ), total phosphorus (TP), suspended solids (SS) and total organic carbon (TOC) selected from the correlation analysis, with eight years (2001-2008) of data for training, and the most recent three years (2009-2011) for testing. Findings: For both accuracy performance and generalized performance, the ANN, GA-SVM and GA-RVM had similar predictive powers of R 2 of 0.73-0.75. However, whereas ANN and GA-RVM models showed very similar forecast performances, GA-SVM models had better forecast performances of R 2 (0.862), RMSE (0.266) and MAE (0.0710) with the respective parameters of 0.987, 0.161 and 0.032 optimized using GA. Originality/value: This is the first application of GA-SVM and GA-RVM models for predicting and forecasting algal bloom in freshwater reservoirs. GA-SVM was shown to be an effective new way for monitoring algal bloom problem in water resources.