An Ontology-Driven Approach for Integrating Intelligence to Manage Human and Ecological Health Risks in the Geospatial Sensor Web.

Xiaoliang Meng,Shiyuan Hu,Guoqiang Song,Yiming Yang,Shifa Ma,Feng Wang,Yichun Xie,Junming Bai

doi:10.3390/s18113619

Xiaoliang Meng, Shiyuan Hu + Show 6 more

Open Access

https://doi.org/10.3390/s18113619

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Abstract

Due to the rapid installation of a massive number of fixed and mobile sensors, monitoring machines are intentionally or unintentionally involved in the production of a large amount of geospatial data. Environmental sensors and related software applications are rapidly altering human lifestyles and even impacting ecological and human health. However, there are rarely specific geospatial sensor web (GSW) applications for certain ecological public health questions. In this paper, we propose an ontology-driven approach for integrating intelligence to manage human and ecological health risks in the GSW. We design a Human and Ecological health Risks Ontology (HERO) based on a semantic sensor network ontology template. We also illustrate a web-based prototype, the Human and Ecological Health Risk Management System (HaEHMS), which helps health experts and decision makers to estimate human and ecological health risks. We demonstrate this intelligent system through a case study of automatic prediction of air quality and related health risk.

Highlights

Ecological public health promotes the concept that health depends on successful co-existence of the natural world and social relationships [1]
This study proposed an ontology-driven approach for integrating intelligence to manage human and ecological health risks in the geospatial sensor web (GSW)
In order to sustain the hybrid standards of Keyhole Markup Language (KML), we developed the agent components, as shown in Figure 6, to realize the solution of admixing XLink for the XML detail, RDFa for the HTML, and the XHTML detail

Summary

Introduction

Ecological public health promotes the concept that health depends on successful co-existence of the natural world and social relationships [1]. This co-existence focuses on increasing people’s awareness of environmental change and the interaction between the biological world and material consumption. Human health depends on the health of ecosystems. Many environmental indicators are included in health and ecological models to analyze or simulate interactions between the nature environment and its impacts on human society. Environmental data sources cover a wide range of fields, such as biological, physical, chemical, etc. It is critical to integrate computational intelligence, such as intelligent data analysis and data-driven decision-making, to solve the problems of human

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