Powering an Egyptian Air Quality Information System with the Bayesian Maximum Entropy Space/Time Analysis Toolbox: Results From the Cairo Baseline Year Study

2001 ◽  
pp. 91-100 ◽  
Author(s):  
M. Serre ◽  
G. Christakos ◽  
J. Howes ◽  
A. G. Abdel-Rehiem
Keyword(s):  
Information System ◽  
Air Quality ◽  
Maximum Entropy ◽  
Space Time ◽  
Bayesian Maximum Entropy ◽  
Quality Information ◽  
Time Analysis

scholarly journals Air Quality Context Information Model for Ubiquitous Public Access to Geographic Information

2018 ◽  
Vol 7 (8) ◽  
pp. 316 ◽  
Author(s):  
Sungchul Hong
Keyword(s):  
Information System ◽  
Air Quality ◽  
Information Model ◽  
Heterogeneous Data ◽  
Geographic Information ◽  
Information Services ◽  
Public Access ◽  
Quality Information ◽  
Context Information ◽  
New Paradigm

The advance in Information Communication Technology (ICT) has contributed to global challenges of improving urban air quality. Ubiquitous computing technology enables citizens to easily access air quality information services without spatial or temporal limitations. Citizens are also encouraged to participate in air quality assessment and environmental governance. These societal and technical changes require a new paradigm to develop an air quality information system and its services. An air quality information system needs to integrate varied types of air quality information from heterogeneous data sources as well as allow citizens to express their concerns about air quality. Thus, a standardized manner is necessary to develop an air quality information system. In this regard, an air quality context information model was designed according to the Ubiquitous Public Access (UPA) context information model defined in the International Organization for Standard (ISO) 19154. For validation and verification purposes, the air quality context information model was implemented in a geographic information system (GIS)-based air quality information system. Implementation results showed that spatially relevant air quality information services were generated from the system, depending on the location and air quality situations near a specific user. Also, citizens can contribute air quality information at their current regions.

scholarly journals Hierarchical Bayesian space-time interpolation versus spatio-temporal BME approach

2010 ◽  
Vol 25 ◽  
pp. 97-102 ◽  
Author(s):  
I. Hussain ◽  
J. Pilz ◽  
G. Spoeck
Keyword(s):  
Maximum Entropy ◽  
Time Domain ◽  
Prediction Error ◽  
Bayesian Model ◽  
Space Time ◽  
Bayesian Maximum Entropy ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Monsoon Period ◽  
Spatio Temporal

Abstract. The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors. Moreover, the arbitrary combinations of purely spatial and purely temporal models may not yield valid models for the space-time domain. For such processes the variation can be characterized by sophisticated spatio-temporal modeling. In the present study the composite spatio-temporal Bayesian maximum entropy (BME) method and transformed hierarchical Bayesian space-time interpolation are used in order to predict precipitation in Pakistan during the monsoon period. Monthly average precipitation data whose time domain is the monsoon period for the years 1974–2000 and whose spatial domain are various regions in Pakistan are considered. The prediction of space-time precipitation is applicable in many sectors of industry and economy in Pakistan especially; the agricultural sector. Mean field maps and prediction error maps for both methods are estimated and compared. In this paper it is shown that the transformed hierarchical Bayesian model is providing more accuracy and lower prediction error compared to the spatio-temporal Bayesian maximum entropy method; additionally, the transformed hierarchical Bayesian model also provides predictive distributions.

Design and prototype of an interoperable online air quality information system

2016 ◽  
Vol 79 ◽  
pp. 354-366 ◽  
Author(s):  
Stefan Wiemann ◽  
Johannes Brauner ◽  
Pierre Karrasch ◽  
Daniel Henzen ◽  
Lars Bernard
Keyword(s):  
Information System ◽  
Air Quality ◽  
Quality Information
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