Support Vector Machine Modeling Using Particle Swarm Optimization Approach for the Retrieval of Atmospheric Ammonia Concentrations

This study was performed in order to improve the estimation accuracy of atmospheric ammonia (NH₃) concentration levels in the Greater Houston area during extended sampling periods. The approach is based on selecting the appropriate penalty coefficient C and kernel parameter σ ². These parameters directly influence the regression accuracy of the support vector machine (SVM) model. In this paper, two artificial intelligence techniques, particle swarm optimization (PSO) and a genetic algorithm (GA), were used to optimize the SVM model parameters. Data regarding meteorological variables (e.g., ambient temperature and wind direction) and the NH₃ concentration levels were employed to develop our two models. The simulation results indicate that both PSO-SVM and GA-SVM methods are effective tools to model the NH₃ concentration levels and can yield good prediction performance based on statistical evaluation criteria. PSO-SVM provides higher retrieval accuracy and faster running speed than GA-SVM. In addition, we used the PSO-SVM technique to estimate 17 drop-off NH₃ concentration values. We obtained forecasting results with good fitting characteristics to a measured curve. This proved that PSO-SVM is an effective method for estimating unavailable NH₃ concentration data at 3, 4, 5, and 6 parts per billion (ppb), respectively. A 4-ppb NH₃ concentration had the optimum prediction performance of the simulation results. These results showed that the selection of the set-point values is a significant factor in compensating for the atmospheric NH₃ dropout data with the PSO-SVM method. This modeling approach will be useful in the continuous assessment of NH₃ sensor discrete data sources.