Yanjie Wang, Zhengchao Xie, InChio Lou, Wai Kin Ung and Kai Meng Mok
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…
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 (PO43−), 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 R2 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 R2 (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.
Details
Keywords
Mohamed A. Tawhid and Kevin B. Dsouza
In this paper, we present a new hybrid binary version of bat and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed…
Abstract
In this paper, we present a new hybrid binary version of bat and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid Binary Bat Enhanced Particle Swarm Optimization Algorithm (HBBEPSO). In the proposed HBBEPSO algorithm, we combine the bat algorithm with its capacity for echolocation helping explore the feature space and enhanced version of the particle swarm optimization with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBBEPSO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for feature selection in the past. A set of assessment indicators are used to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBBEPSO algorithm to search the feature space for optimal feature combinations.