Accurate pollutant modeling and mapping: Applying machine learning to participatory sensing and urban topology data

As sensor networks and mobile and participatory sensing mature, large environmental datasets become available. Environmental scientist are not prepared to use these vast and noisy datasets for environmental modeling. Today, environmental pollutants (e.g., noise) are simulated and the resulting model is verified by a small number of stationary measurements. These models are updated infrequently and provide only limited time resolution. Recently, people have started to apply regression to train environmental models. This has shown great promise, but the complexity of regression models might not always be needed. Classification, however, has not been investigated as a mean to provide high-resolution environmental models from noisy data. The main contribution of this paper is a thorough investigation on the application of classification to environmental modeling (using noise as example pollutant). We present an end-to-end classification pipeline that predicts six classes of noise pollution with a precision of 80.89% and a recall of 80.90% using 10-fold cross-validation. Furthermore, we show the advantages of our approach regarding robustness to underline the applicability of classification for real-world scenarios.