# Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
###### Abstract
Other than the chemical methods of gas detection and the advancements in interdisciplinary technologies, various Artificial Intelligence (AI) based techniques are also reported in the literature. Different machine learning algorithms such as Logistic Regression, Random Forest, and Support Vector Machines (SVM) are proposed in the literature for gas detection [8]. However, these methods require multiple hyperparameters tuning and statistical calculation for accurate and robust gas classification. It increases the processing time, the power used, and computations [9]. [PERSON] [10] provided a methodology that selected top weighted features from complex datasets for improving the time complexity as well as accuracy of the machine learning models. [PERSON] [11] proposed an electronic nose system of classification and concentration estimation that uses least square regression. An array of eight different gas sensors is used to identify gases' concentration in [12]. In this work, Deep convolutional neural networks are employed for the application of gas classification. It was shown that the deep learning algorithms can learn features from the measurements from gas sensors in a better way and can achieve higher classification accuracy. [PERSON] et al. [13] presented a fusion of different AI models for Gas Source Localization to determine the point of leakage in a ground using six various gas sensors. [PERSON] et al. [14] presented a deep learning approach consisting of a hybrid framework comprised of the Convolutional Neural Network (CNN) and Long short-term memory (LSTM) to extract sequential information from transient response curves. Fast Gas Recognition algorithm based on hybrid CNN and Recurrent Neural Network (RNN) is presented in [9]. It was shown that the fusion model outperforms Support Vector Machine (SVM), Random Forest, \(k-\)nearest neighbors. [PERSON] et al. [15] described two network structures, Deep Belief Networks and Stacked Autoencoders, to extract abstract gas features from E-nose. Then the Softmax classifiers are constructed using these features. These reported approaches use sequential methods based on the gas sensor data directly. However, there are several issues with using only a gas sensors-based detection and identification approach. The primary reason is that the proportion of gas in air is very low in some cases, and the gases are not identifiable with standard gas sensors. This generates false negatives or false positives and hence hampers the detection accuracy of the system. Additionally, low-cost sensors are typically less sensitive and may not provide accurate measurements. Another method observed for gas detection is the use of thermal imaging. When a gas is leaked, the surrounding temperature increases compared with the normal conditions. The increase in temperature can be characterized and analyzed by thermal imaging cameras. This concept can be utilized to detect leakages [16; 17]. The system for Methane and Ethane gas leak detection using a thermal camera is proposed in [18]. [PERSON] and [PERSON] [19] presented a method for detecting gas leak using infrared image analysis. The system was designed by the technique of image processing, which are data acquisition, image preprocessing, image processing, feature extraction, and classification. Single modality sensing methods may not achieve the system's required accuracy and robustness as such systems are limited to sensor characteristics. Individual sensors are limited to temporal and spatial characteristics [20]. A thermal imaging system can identify the presence of gas but fails to identify its type. Hence, a concept of multimodal/multi-sensor data fusion came into existence. Data fusion combines information from multiple sources to obtain the better output compared to any individual modality taken alone [20]. Kalman filter proposed in [21] is one of the most widely used sensor fusion algorithms in robotics applications like position and orientation estimation, guided vehicles, etc.