# Investigating Effects of Landfill Soil Gases on Landfill Elevated Subsurface Temperature
## 5 Results
### Decision Tree Analysis  
We applied the conditional inference trees algorithm [53] to 18,469 observation datapoints of wellhead temperatures and gas parameters to identify the factors most closely associated with subsurface temperatures. The algorithm approximates a regression relationship in a conditional inference framework using the binary recursive partitioning method. First, it tests the null hypothesis of independence between the response (temperature) and input variables (gas variables), and stops if the test fails to reject the hypothesis that temperature is an independent variable. Otherwise, the input variable with the strongest association is selected based on a \(p\)-value (significance level, \(p<0.05\)) corresponding to the hypothesis test. Following this test, a binary partition of the selected variable is implemented. The algorithm repeats these two steps recursively at each split of a variable. The first parameter selected for the binary split represents a significant parameter generally linked to subsurface temperature.  
We classified all the parameters as to whether they were in a safe or unsafe range before applying the algorithm, classifying the temperatures as \"under 55 \({}^{\circ}\)C\", \"55-80 \({}^{\circ}\)C\", \"80-93 \({}^{\circ}\)C\" or \"93-149 \({}^{\circ}\)C\". Figure 5 demonstrates all the possible splits of a decision tree at a significance level of \(<\)0.05 and the parameters with the best splits in circles with their corresponding \(p\)-values. The decision tree branches indicate the levels of parameters and the bar plots at the bottom show the proportions of the four temperature ranges in each end node containing all observations with a combination of features. Among the four parameters, the CH\({}_{4}\)-to-CO\({}_{2}\) ratio is the covariate showing the strongest association with the temperature ranges with a significance level less than 0.001. Using a univariate logistical regression to study the synergistic effect of fire indicators, [PERSON] et al. [56] found the CH\({}_{4}\)-to-CO\({}_{2}\) ratio concentration to be a statistically significant fire indicator. The first tree branch starting with a ratio of \(<\)1, shows a strong association with O\({}_{2}\) (significance level, \(p<0.001\)), whereas CH\({}_{4}\) was strongly associated (significance level, \(p<0.001\)) with the branch of a ratio of \(>\)1. The tree shows that the branch with CH\({}_{4}\)-to-CO\({}_{2}\) ratio \(<1\), O\({}_{2}<5\%\), and CH\({}_{4}<45\%\) and \(>\)60% yields 8888 events from the total 18,469 data points. This branch results in the highest number of events in the temperature ranges of \"80-93 \({}^{\circ}\)C\" and \"93-149 \({}^{\circ}\)C\" than any other branches. Hence, the branch can be interpreted  
Figure 4: Temperature in different gas conditions.  
as the most unsafe one. The second highest datapoints in the temperature range 80-93 \({}^{\circ}\)C is noticed in the branch with a CH\({}_{4}\)-to-CO\({}_{2}\) ratio of \(<\)1 and O\({}_{2}\)\(>\) 5%. The third highest incidents (approximately 720 observations) in the temperature range of 55-80 \({}^{\circ}\)C were observed within the branch of CH\({}_{4}\)-to-CO\({}_{2}\) ratio \(>\) 1 and CH\({}_{4}\) with 45-60%. Therefore, the unsafe ranges of the gas parameters are not always associated with high temperature ranges, but rather different combinations of parameters.