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Development of Sanitary Landfill's Carbon Dioxide Concentration Models Using Machine Learning Algorithms

Abstract:

Carbon dioxide is one of the major component of landfill gas being emitted by sanitary landfills. High concentration of this gas may cause several health condition. It is also one of the greenhouse gas that consistently contributes to climate change. Monitoring and assessing the carbon dioxide concentration in landfills is vital to ensure better living conditions. This study presents the development of carbon dioxide concentration model based on machine learning algorithms. A prototype was developed using Arduino Uno, Wi-Fi module, DHT11 temperature and humidity sensor, MQ4 and MQ135 gas sensors. This prototype was used to gather CO2 and CH4 concentrations, humidity and air temperature of the sanitary landfill. Five machine learning model based on linear regression, support vector machine, regression trees, boosted regression trees and neural network was trained and evaluated. Matlab software was used in this study for the development of each model. The R-square and MSE of each model was calculated and compared which results to an almost identical r-square value of 0.75 and 0.76. An MSE of 6.90857e-05 for the neural network model followed by SVM, Boosted Regression Trees, Regression Trees and Linear Regression with an MSE of 8.8168e-05, 9.0085e-05, 9.4227e-05 and 9.4652e-05 respectively was also obtained. Based on these results, it was concluded that the machine learning model based on neural network is the best algorithm for the carbon dioxide concentration modelling in sanitary landfills since it obtained the lowest MSE among the five models.