# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
###### Abstract
Regarding factors acting from inside apartments, one could mention studies on the role of plants [15] and the impact of daily activities of occupants [16], as well as their actions oriented toward attaining high IAQ [17]. So far, little research has been conducted on multipoint IAQ monitoring in apartments [18; 19] and in examining interdependencies between their different zones. This is particularly true for studies involving measurements of multiple air parameters in several locations (multipoint) with high temporal resolution for a longer period. The two main reasons for this are privacy concerns and the difficulty of running multipoint measurements. A particular location in apartments is modern living rooms. They fulfill many different functions by being a space for spending time together, eating, relaxing, and, sometimes, working. Family life is concentrated there. Typically, the living room is located close to the kitchen, or it is fitted with a kitchen zone directly. The kitchen stands out as a primary source of indoor air pollutants within a dwelling. It is the consequence of emissions associated with food preparation, cooking, dishwashing, laundry, etc. Due to its proximity, the living room may experience a more pronounced influence from kitchen-related pollutants compared to other rooms. In this paper, we want to show that sensors located in the kitchen may provide information about air quality in other rooms of the same apartment, particularly in the living room. This study seeks to explore the capabilities of MLTs in predicting air parameters in the living room based on IAQ monitoring in the kitchen. The MLTs have already shown a considerable contribution to the IAQ examination. Diverse regression models have been implemented to explain the impacts of occupancy on the indoor environment, focusing on specific locations such as the kitchen [20], bedroom [21], and living room [22]. Multiple linear regression (MLR), combined with preprocessing methods [23], has verified its capability in predicting indoor particulate matter (PM2.5-10) relying on outdoor PM2.5-10 and carbon dioxide (CO\({}_{2}\)) [24; 25; 26], indoor temperature (T) [25; 26], ventilation rate [24; 25], and occupancy [24]. For addressing nonlinear correlations, robust models such as decision trees (DTs) and random forest regressor (RFR) have been implemented. DT was employed to study the association of IAQ with sick building syndrome in the office [27], while RFR indicated notable performance in predictingindoor PM2.5 in dwelling environments [28; 29]. Non-parametric models such as k-nearest neighbors (KNN) are versatile in addressing linear and nonlinear problems. Although some research indicated lower accuracy in comparison to alternative techniques [30; 31], the adoption of the KNN model persists due to its incremental nature [30; 31; 32]. An additional alternative for regression involves the implementation of artificial neural network (ANN) models such as multilayer perceptron (MLP). MLP demonstrated successful prediction of CO\({}_{2}\) levels in a room when furnished with indoor temperature and relative humidity (RH) as inputs [33]. However, it requires a meticulous tuning process to ensure effective learning [32]. Various linear and nonlinear models such as MLR, DT, RFR, KNN, and MLP (representing regression ANN models) were employed for IAQ prediction in this work. The methods were systematically compared based on selected performance metrics.