Abstract
An accountable disaster prediction and the appropriate forewarned time are the key issues to reduce the possible damages. Around the globe, landslides and mudslides are serious geological hazards affecting people, and cause significant damages every year. The stability of a slope changed from a stable to an unstable condition that spawns a landslide or mudslide. In most of mudslide-damaged residences, the electricity equipments, especially electricity poles, are usually tilted or moved. Since the location and status of each electricity pole are usually recorded in AMI (Advanced Metering Infrastructure) MDMS (Meter Data Management System), AMI communication network is a substantial candidate for constructing the mudslide detection network. To identify the possible mudslide areas from the numerous gathered data, this paper proposes a data analysis method that indicates the severity and a mechanism for detecting the movement. According the detecting result and the gathered data, this study calculates the remaining forewarned time when an anomaly happens.
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Tang, CJ., Dai, M.R. (2010). Identifying Mudslide Area and Obtaining Forewarned Time Using AMI Associated Sensor Network. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2010. ICCSA 2010. Lecture Notes in Computer Science, vol 6018. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12179-1_31
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DOI: https://doi.org/10.1007/978-3-642-12179-1_31
Publisher Name: Springer, Berlin, Heidelberg
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