Spatio-temporal modeling for real-time ozone forecasting

Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data

We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scaled framework is a seamless coupling of two major components: a self-exciting point process that models the macroscale statistical behaviors of the ST data and a graph structured recurrent neural network (GSRNN) to dis...

Spatial-Temporal Trend Modeling for Ozone Concentration in Tehran City

 Fitting a suitable covariance function for the correlation structure of spatial-temporal data requires de-trending the data. In this article, some potential models for spatial-temporal trend are presented. Eventually the best model will be announced for de-trending tropospheric ozone concentration data for the city of Tehran (Capital city of Iran). By using the selected trend model, some ...

Modeling of spatio-temporal of albedo over Iran

The aim of this study is modeling spatiotemporal variations of albedo. This study was conducted using simultaneous effects of several components, such as wetness of surface layer of soil, cloudiness, topography and vegetation density (NDVI), using MEERA2 model with a resolution of 50 in 50 km during 2000-2010 in Iran. The results of spatial analysis of albedo values in Iran showed that the high...

Local Real-time Forecasting of Ozone Exposure using Temperature Data

Local Real-time Forecasting of Ozone Exposure using Temperature Data

Rigorous and prompt assessment of ambient ozone exposure is important for informing the public about ozone levels that may lead to adverse health effects. In this paper, we make use of hierarchical modeling to forecast 8-hour average ozone exposure. Our contribution is to show how incorporating temperature data in addition to observed ozone can significantly improve forecast accuracy, as measur...