A Deep Learning Framework for Air Pollution Forecasting and Interpolation

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

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

Procedural characteristics.

<p>Procedural characteristics.</p

ROC curve analysis.

<p>An increase in 6h post-PCI NGAL > 96 ng/ml significantly predicts an absolute SCr increase > 0.24 mg/dl after contrast exposure with sensitivity of 53% and specificity of 74% (AUC 0.819, 95% CI: 0.656 to 0.983, p = 0.005) and with and OR of 3.15 (p = 0.023).</p

Clinical, demographic and laboratory baseline characteristics of the study population.

<p>Clinical, demographic and laboratory baseline characteristics of the study population.</p