AccuMM
AccuMM is a low-cost solar or battery-powered GPS sensor that is attempting to make monitoring and modelling landslide activity far easier and more accurate. Engineering and Computer Science master’s student Jonathan Olds of Victoria University explored landslide prediction for his master’s thesis in 2016, where he created the AccuMM sensors that can continuously monitor a hillside for incremental movement by a fixed base station receiving GPS data from the sensors fixed on the hillside. An algorithm then calculates changes in the position of each sensor— data that can be used to calculate the risk of a landslide for said area. The project was piloted in landslide-prone areas of Taiwan, including the transport corridors in Kaikoura, Kāpiti Coast, and Wellington. The degree to which these sensors ease the monitoring of landslides is hard to overstate since most of the activity is monitored by surveyors periodically visiting a site in person and using line-of-site technology to measure movement. Such manual processes are time-consuming, relatively infrequent, and expensive. AccuMM sensors are self-powered, quickly transmit data on a frequent and fixed timescale, and can sit on a hillside undisturbed for up to five years following installation, requiring no human intervention except at its removal. AccuMM sensors are still in the testing phase, and commercial cost and availability is not yet a consideration; rather, the focus is on testing efficacy. However, making sure the technology is accessible both in use and cost should be kept in mind.
Victoria University. “Technological breakthrough for monitoring and predicting landslides.” Phys.org, February 13, 2018. https://phys.org/news/2018-02-technological-breakthrough-landslides.html
Stringer, J., and M. Brook. “Monitoring of landslides along the Southern Kaikoura Transport Corridor, New Zealand.” In EAGE-GSM 2nd Asia Pacific Meeting on Near Surface Geoscience and Engineering, vol. 2019, no. 1, pp. 1-5. European Association of Geoscientists & Engineers, 2019.
Categories
Data, Ecological Modelling, Ecological Monitoring, Monitoring, Regulation
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