# Methane Detection Based on Improved Chicken Algorithm Optimization Support Vector Machine
## 1 Introduction  
Air pollution is a serious environmental issue that has attracted more and more attention globally in recent years [1; 2; 3]. Methane is the main greenhouse pollutant, and also the main component of mine gas, biogas, and various liquid fuels [4; 5]. It is stipulated that the lowest limit of explosion in air is 5.0%, the highest limit is 15.0%, and the explosive capacity is the strongest when the volume fraction is 9.5% [6]. Methane in the atmosphere can also cause a greenhouse effect and accelerate global warming [7; 8]. It is for these reasons that methane detection is an indispensable field of research. Traditional detection generally adopts chemical methods, which require chemical reagents. These reagents have many disadvantages, such as danger, need to replace, and short life, and so these methods are not conducive to online real-time detection. In addition, absorption spectroscopy is a detection method that offers a rapid, direct, and selective technique to measure the concentration ofmethane [9], and it has become the dominant detecting method [10; 11]. Methane concentrations are obtained through infrared spectroscopy with an appropriate forecasting model. Currently, it is urgent that an accurate and effective methane prediction model be developed, such as least squares fit, multi-element linear regression, back propagation neural network, or support vector machine (SVM). SVM, based on the principle of structural risk minimization, has higher efficiency when the number of training samples is small [12; 13]. The performance of SVM is highly related to its kernel parameters and penalty factor, so choosing appropriate parameters is the key to improving the prediction accuracy. At present, there are a lot of parameter optimization algorithms. For example, [PERSON] and [PERSON] used the genetic algorithm (GA) to select features and optimize the SVM parameters to improve the prediction accuracy of a hospitalization expense model [14]. [PERSON] and [PERSON] used the particle swarm optimization algorithm optimized support vector machine (PSO-SVM) classifier to classify the maximum tensile shear strength of spot-welded joints; the results showed that the PSO-SVM classifier had a good accuracy [15]. The PSO-SVM based on adaptive mutation was used to classify the increased volume and complexity of flow cytometry (FCM) data by [PERSON] [16]. [PERSON] proposed a short-term wind speed forecasting method, which consists of ensemble empirical mode decomposition (EEMD) for data preprocessing, and an SVM optimized by the cuckoo search algorithm (CS). The experimental results indicated that the proposed model can not only improve the forecasting accuracy, but also can be an effective tool in assisting the management of wind power plants [17]. A new cuckoo search algorithm based on a chaotic catfish effect optimization of the SVM was proposed by [PERSON] and [PERSON], who applied it to oil layer recognition [18]. [PERSON] and [PERSON] proposed a SVM optimization based on differential evolution and the grey wolf optimization (DE-GWO-SVM) algorithm to predict power grid investment, which proved that the DE-GWO-SVM model had strong generalization capacity and had a good prediction effect on power grid investment forecasting in China [19]. The CSO algorithm, known as a novel nature-inspired algorithm, was proposed by [PERSON] in 2014 [20]. CSO is a stochastic optimization method based on the search behavior of the chicken, which simulates the hierarchy order and the behaviors of a chicken swarm. The chicken swarm is divided into subgroups, and each subgroup consists of chicks, some hens, and a rooster. There is competition between subgroups, that is to say, there is a global optimization result. However, in the paper [18], it was indicated that CSO easily falls into a local optimum, and its progress and speed are greatly influenced by initial values. To this end, an ICSO is proposed.