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Monitoring systems in landslide-prone areas are crucial for effective Early Warning structures. Data acquisition and retrieval enable the detection of movement processes, essential for timely warnings. Reliable data is fundamental, as outliers can cause false alarms and diminish trust in these systems. Key aspects of monitoring mass movements include identifying whether movement is occurring, its speed, and the reliability of the information. This thesis explores the development of a prototype monitoring system for various landslide types within the joint project "A Sensorbased Landslide Early Warning System (SLEWS)." The system utilizes a modern Wireless Sensor Network (WSN) for data transmission, featuring a self-organizing structure with real-time bi-directional communication and multi-hop data transfer. For detecting surface deformations in landslide areas, low-cost micro sensors, known as Micro-Electro-Mechanical-Systems (MEMS), along with position sensors from the automotive industry and other technologies, were selected. Laboratory tests assessed the accuracy and resolution of the sensors within the WSN environment, complemented by field tests to verify system stability in real conditions. To enhance data quality and reduce errors, the concept of Multi-Sensor Data Fusion was applied, adapting a model from the Joint Directors of Laboratories (JDL) specifically for landslide monitoring.
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Monitoring of surface movements in landslide areas with a self-organizing wireless sensor network (WSN), Christian Arnhardt
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- 2012
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