Environmental Modeling & Assessment

, Volume 21, Issue 4, pp 531–546 | Cite as

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

  • Jiawei Zhang
  • Frank K. Tittel
  • Longwen Gong
  • Rafal Lewicki
  • Robert J. Griffin
  • Wenzhe Jiang
  • Bin Jiang
  • Mingbao Li


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.


Atmospheric ammonia concentration Predictions of concentration levels Particle swarm optimization Support vector machine 



This study was supported by the Mid-InfraRed Technologies for Health and the Environment (MIRTHE) Center and National Science Foundation (NSF) under grant no. EEC-0540832. It was also supported by the Natural Science Foundation of China (Grant No. 31470715 and Grant No. 31470714) and international advanced forestry science and technology plan (2013-4-58). The authors gratefully acknowledge Texas Commission on Environmental Quality for supplying the relevant data (http://www5.tceq.state.tx.us/tamis/index.cfm?fuseaction=home.welcome) used in this publication.

Compliance with ethical standards

Author Contributions

J. Zhang and F.K. Tittel designed the research plan and the preparation of this manuscript. R.J. Griffin and L. Gong provided environmental and atmospheric background knowledge. R. Lewicki developed the EC-QCL sensor architecture to measure atmospheric NH3 concentration levels. J. Zhang and W. Jiang compiled the model coding and carried out numerical simulations. M. Li and B. Jiang provided programming support.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jiawei Zhang
    • 1
    • 2
  • Frank K. Tittel
    • 2
  • Longwen Gong
    • 3
  • Rafal Lewicki
    • 4
  • Robert J. Griffin
    • 3
  • Wenzhe Jiang
    • 2
  • Bin Jiang
    • 5
  • Mingbao Li
    • 6
  1. 1.College of Mechanical and Electrical EngineeringNortheast Forestry UniversityHarbinChina
  2. 2.Department of Electrical and Computer EngineeringRice UniversityHoustonUSA
  3. 3.Department of Civil and Environmental EngineeringRice UniversityHoustonUSA
  4. 4.Sentinel Photonics CompanyPlainsboroUSA
  5. 5.Harbin Research Institute of Electrical InstrumentHarbinChina
  6. 6.College of Civil EngineeringNortheast Forestry UniversityHarbinChina

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