NEC tests landslide prediction tool in Thailand

Enterprise Innovation editors
Enterprise Innovation

NEC and Thailand's National Disaster Warning Center (NDWC) have completed a trial for a system that identifies areas where there is danger of a landslide occurring.

The effectiveness of the system was confirmed as the trial was conducted in Chiang Mai Province in Northern Thailand during the period from November 2016 to March 2017.

In April 2015, Japan's Ministry of Internal Affairs and Communications and Thailand's Ministry of Information and Communication Technology issued a joint statement announcing that the two countries would cooperate in a wide range of areas, including the development of more sophisticated disaster prevention ICT and the use and application of the technologies.

NEC conducted this trial in collaboration with the Embassy of Japan in Thailand as part of the "Research and study for the development of a landslide simulator in Thailand" project commissioned by Japan's Ministry of Internal Affairs and Communications.

NEC joined NDWC in conducting verification experiments with a flooding simulation system in Uttaradit Province in Northern Thailand between November 2015 and March 2016. The trial reported here is a follow-up to that work.

The landslide prediction system is one of the modules of NEC's "integrated risk management system." The integrated risk management system consists of a shared platform that has functions such as data integration, visualization, and early warning, and disaster modules specialized for particular disasters such as landslides, flooding, and earthquakes.

The disaster modules or functions can be selected individually as required, or several disaster modules can be combined in order to predict multiple disasters simultaneously.

The landslide prediction system performs a simulation based on meteorological data (observed rainfall and forecast rainfall), topographical data (elevation values, land use purposes), and soil data (soil depth, hydraulic conductivity, porosity, cohesion force, internal friction angle, etc.), making it possible to predict the degree of landslide danger.

First published in Enterprise Innovation

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