A stream computing approach for live environmental models using a spatial data infrastructure with a waterlogging model case study
Section snippets
Software availability
Program name: EMGeoStreaming
Developer: Boyi Shangguan
Contact address: [email protected]
Year first required: 2018
Software required: Apache Spark 2.1.0 or later, Apache Kafka 2.11
Programming language: Java 1.8, Scala 2.11
Program size: 16.6 MB (compressed source code)
Availability: https://github.com/whu-ypfamily/EMGeoStreaming
Cost: Free of charge
The motivating example of waterlogging disaster
Environmental models play key roles in geographical process simulation, whose goal is to dynamically show processes of geographical phenomena with GIS technologies to provide decision-support information. The waterlogging disaster management is a typical case of geographical process simulation, which is taken as the motivating example in this paper.
As one of the most destructive natural hazards, the waterlogging disaster has been studied intensively and there is a number of simulation models
Background
This section introduces two basic technologies that are related to our approach: Sensor Web and Stream computing.
An observation stream computing model
The observation stream from Sensor Web can be fed into the stream computing framework by using a set of extended RDD types, which constitute a distributed in-memory model for observation stream computing. This section describes how observation stream can be modelled (Section 4.1), and how the model can provide in-house support for stream computing (Section 4.2). A theoretical analysis of the performance is provided in Section 4.3 to help understand how to achieve low latency in observation
Walk-through for waterlogging information derivation
The use case of waterlogging disaster management described in Section 2 is used to illustrate the approach. The walk-through example includes the transformation of environmental models as a set of operators in the event model (Section 5.1) and enactment of O-Stream computing (Section 5.2).
Implementation
In this section, we introduce the implementation of a prototype system (Section 6.1) for waterlogging disaster management based on the model and framework proposed in Section 4. Experimental analysis and discussion are given in Section 6.2.
Conclusions and future work
This paper presents an approach for feeding real-time observation data of the Sensor Web into environmental models with stream computing technologies to generate “live” models. The approach makes it possible to derive timely decision-support information in time-critical environmental events. A case on waterlogging disaster management is used to illustrate the approach.
In the paper, an interoperable, extensible, and scalable framework is proposed to couple Sensor Web with environment model in a
Acknowledgements
We appreciate the reviewers and editors for their constructive comments that helped improve the quality of the paper. The work was supported by Major State Research Development Program of China (No. 2017YFB0503704), National Natural Science Foundation of China (No. 41722109, 61825103, 91738302), Hubei Provincial Natural Science Foundation of China (No. 2018CFA053), Nature Science Foundation Innovation Group Project of Hubei Province, China (No. 2016CFA003), and Wuhan Yellow Crane Talents
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