Paper Info
Title
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.
Year
DOI
Venue
2017
10.1109/ICWS.2017.76
2017 IEEE International Conference on Web Services (ICWS)
Keywords
Field
DocType
Web service,Ensemble learning,Air quality
Data mining,Boltzmann machine,Software deployment,Computer science,Air quality index,Air pollution,Web service,Ensemble learning,Feature learning,Beijing
Conference
ISBN
Citations 
PageRank 
978-1-5386-0753-4
0
0.34
References 
Authors
16
8
Name
Order
Citations
PageRank
Chao Zhang1334.39
Junchi Yan289183.36
Yunting Li300.34
Feng Sun400.34
Jinghai Yan500.68
Dawei Zhang600.34
Xiaoguang Rui7877.59
Rongfang Bie854768.23