Pollution Prediction Model Using Data Collected by a Mobile Sensor Network

In this paper we investigate how to build a model to predict pollution levels using geographical information. By focusing on this kind of attributes we hope to contribute to an effective city management as we will find the urban configurations that conduct to the lowest pollution levels. We used decision trees to build a regression model. We performed a parameter grid search using cross validation. Ablation analysis where some attributes were removed from training showed that geographical based attributes impact the prediction error of decision trees.