Development of a radiative transfer model for the determination of toxic gases by Fourier transform-infrared spectroscopy with a support vector machine algorithm

摘要

This report describes a radiative transfer model for Fourier transform-infrared (FT-IR) spectroscopy to create close-to-reality toxic gas spectra by reflecting the unique spectral responses of detectors and using the atmospheric radiative transfer code, MODTRAN. This system can be highly useful in overcoming the limitations for measuring toxic gases in open environments. The emulated gas spectra can be used to train support vector machine (SVM) for chemical gas detection. Its detection performance is evaluated with nerve agents (tabun, sarin, soman, and cyclosarin) and a simulant gas (sulfur hexafluoride) for indoor and outdoor experiments by using two off-the-shelf FT-IR gas detectors. The experimental results show that the proposed SVM algorithm successfully detected and classified targeted gases while reducing false negative and false positive detection rates.