Palmos Co. — Department of Agriculture SBIR Phase I: 8.6

Description

In the United States, landslides cause over $1 billion in damage and 50 deaths annually. Landslidesoccur in every state in the United States and the majority of these landslides occur in rural areas ofthe country. These events primarily affect the rural areas of the country and are becoming more frequent with the increase in extreme weather events due to climate change and variability. The proposed research will lead to a wireless sensor network that will monitor the environment around rural developments alerting local officials to at-risk areas for landslides. This will give sufficient time to either stabilize the at-risk locations or develop effective emergency response measures against a flow landslide event. In addition, this can ensuring future development proceeds in a risk-averse manner. The proposed system will allow for a more complete understanding of the soil drainage patterns in elevated areas surrounding rural developments.There have been concerted efforts by government, academia and industry to more fullyunderstand the conditions under which landslides occur but widespread monitoring and predictionof landslide initiation has been largely unsuccessful because the sensors are cost-prohibitive andrequire extensive calibration and maintenance. The research plan aims to verify andexpand the use of a novel wireless sensor network developed by Palmos that uses ground vibrationand pore groundwater pressure data to monitor and forecast flow landslides. The proposed systemwill use low-cost, easily calibratedsensors for significantly increased spatial and temporalresolution.To determine the success of the proposed system, it is necessary to run a large-scale multi-monthtest in real meteorogical conditions.Both thewireless sensor sytem and the corresponding software used to analyze the environmentalmonitoring data could be commercialized because it could provide substantial benefits to privatecommunities andgovernments that live in landslide prone areas.