Early Air Pollution Forecasting as a Service: An Ensemble Learning Approach

Abstract

Air quality has become a major global concern for human beings involving all social stratums, for both developing and developed countries. Web service of precise and early air pollution forecasting is of great importance as it allows people to pro-actively take preventative and protective measurements. As an endeavor on the course of machine learning based air quality forecasting, this paper presents an initiative and its technological details in solving this challenging problem. Specifically, this work involves three major highlights regarding with both algorithmic innovation and deployment with its impact: 1) We propose a multi-channel ensemble learning framework, 2) We propose a new supervised feature learning and extraction method, i.e. sufficient statistics feature mapping based on Deep Boltzman Machine, which serves as a building block for our learning system, 3) We target our air pollution prediction method to the city of Beijing, China as it is at the forefront for battling against air pollution, which is embodied as a web service for prediction. Extensive experiments of real time air pollution forecasting on the real-world data demonstrates the effectiveness of the proposed method and value of the deployed web service system.