An integrated information system for snowmelt flood early-warning based on internet of things
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Floods and water resource management are major challenges for human in present and the near future, and snowmelt floods which usually break out in arid or semi-arid regions often cause tremendous social and economic losses, and integrated information system (IIS) is valuable to scientific and public decision-making. This paper presents an integrated approach to snowmelt floods early-warning based on geoinformatics (i.e. remote sensing (RS), geographical information systems (GIS) and global positioning systems (GPS)), Internet of Things (IoT) and cloud services. It consists of main components such as infrastructure and devices in IoT, cloud information warehouse, management tools, applications and services, the results from a case study shows that the effectiveness of flood prediction and decision-making can be improved by using the IIS. The prototype system implemented in this paper is valuable to the acquisition, management and sharing of multi-source information in snowmelt flood early-warning even in other tasks of water resource management. The contribution of this work includes developing a prototype IIS for snowmelt flood early-warning in water resource management with the combination of IoT, Geoinformatics and Cloud Service, with the IIS, everyone could be a sensor of IoT and a contributor of the information warehouse, professional users and public are both servers and clients for information management and services. Furthermore, the IIS provides a preliminary framework of e-Science in resources management and environment science. This study highlights the crucial significance of a systematic approach toward IISs for effective resource and environment management.
KeywordsSnowmelt flood early-warning Integrated information system Internet of things Geoinformatics e-Science Cloud services
The authors thank all research team members for their wholehearted participation during this work, and also grateful to anonymous referees and associate editors for their insightful comments and thoughtful suggestions on the manuscript, which has led to improvements in this paper.
The work was joint funded by the “Research Plan of LREIS, CAS (grant no. O88RA900PA)”, the “State Key Laboratory Program of SKLCS, CAS (grant no. SKLCS 2011–08)”, the “Key Project for the Strategic Science Plan in IGSNRR, CAS (grant no. 2012ZD010)”, the “Open Funding of the Key Laboratory of Oasis Ecology (Xinjiang University) Ministry of Education (grant no. XJDX0206-2011-01)”, the “National Natural Science Foundation of China (grant no. 41201097 & no. 41101031)” and the “Youth Science Funds of LREIS, CAS”.
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