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An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application

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Abstract

Air quality control requires real-time monitoring of pollutant concentration distributions in large urban areas. Estimation models are used for the soft-calibration of low-cost multisensor data to improve precision of pollutant concentration measurements. This study introduces an integrated genetic programming dynamic neural network model for more accurate estimation of carbon monoxide and nitrogen dioxide pollutant concentrations from the multisensor measurement data. This model combines a genetic programming-based estimation model with a neural estimator model and improves estimation performances. In this structure, a genetic programming-based polynomial model works as a former estimator and it feeds the neural estimator model via a short-term former estimation memory. Then, the neural model utilizes this former estimation memory in order to enhance pollutant concentration estimations. This integration approach benefits from the correlation enrichment strategy that is performed by the former model. The proposed two-stage training procedure facilitates the training of the integrated models. In experimental study, the standalone genetic programming model, artificial neural network model, and the proposed integrated model are implemented to estimate carbon monoxide and nitrogen dioxide pollutant concentrations from the experimental multisensor air quality data. Results demonstrate that the proposed integrated model can decrease mean relative error about 10% compared to the standalone artificial neural network and about 28% compared to the standalone genetic programming estimation models. Authors suggested that the integrated estimation model can be used for more accurate soft-calibration of multisensor electronic noses in a wide-area air-quality monitoring application.

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  1. Department of Computer Engineering, Bitlis Eren University, Bitlis, Turkey

    Davut Ari

  2. Department of Computer Engineering, Inonu University, Malatya, Turkey

    Baris Baykant Alagoz

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Appendix

Appendix

Detailed performance analyses of the IGPD-NN, GP and ANN models for randomly sampled test datasets after 100 repeated training sessions are presented below. (Average performance indices in tables were validated within the statistical significance level of 5%.) See Tables 6, 7, 8, 9, 10, 11, 12.

Table 6 MSE performance analyses of CO estimation models
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Table 7 MSE performance analyses of NO2 estimation models
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Table 8 MRE performance analyses of CO estimation models
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Table 9 MRE performance analyses of NO2 estimation models
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Table 10 R2 score performance analyses of CO estimation models
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Table 11 R2 score performance analyses of NO2 estimation models
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Table 12 Effects of hidden layer neuron numbers on average MSE performances of CO and NO2 estimation models
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Ari, D., Alagoz, B.B. An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application. Neural Comput & Applic 34, 12633–12652 (2022). https://doi.org/10.1007/s00521-022-07129-0

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