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A Deep Learning Framework for Air Pollution Forecasting and Interpolation

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posted on 06.08.2019 by Marco Miglionico
Air pollution has been identified as the world's largest single environmental health risks by the World Health Organization. Real time air-quality information is necessary, to pretect humans against from the damage casused by air pollution. In this Thesis we will address this problem by creating a new framework capable of predicting and interpolating the PM2.5 concentration. We will use a Biderectional LSTM for the prediction part and an Artificial Neural Network with Self Training for the interpolation part. We will create 1km x 1km maps of the city of Chicago and we will compare our results with different baselines and existing frameworks.

History

Advisor

Wolfson, Ouri

Chair

Wolfson, Ouri

Department

Computer Science

Degree Grantor

University of Illinois at Chicago

Degree Level

Masters

Committee Member

Lin, Jane Mattucci, Matteo

Submitted date

May 2019

Issue date

22/04/2019

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