![\"Longjing\"](\"https:\/\/blogs.agu.org\/landslideblog\/files\/2019\/02\/after-sliding-overview-e1550818391338.jpg\")
The Longjing village landslide in Guizhou village, China. Image via SKLGP<\/a>.<\/p><\/div>\n .<\/p>\n This landslide was initially triggered in 2013 by cutting of the toe of the slope during road construction . The image below shows the state of the slope before the February 2019 landslide:-<\/p>\n The Longjing village landslide prior to the February 2019 failure. Image via SKLGP<\/a>.<\/p><\/div>\n .<\/p>\n The image shows a 270 metre long sliding surface with a steep scarp at the crown.\u00a0 It is unsurprising that there was potential further instability in this area, and the sliding surface clearly meant that the debris could impact the road.\u00a0 Investigations by SKLGP<\/a> suggested that movement was facilitated on a 2 to 5 cm thick layer of clay between the dolomite bands, a situation that is often hazardous.<\/p>\n On June 2018 a 0.9 m wide tension crack was observed above the existing landslide crown, suggesting that further instability was developing.\u00a0 Over the next few months further deformation was observed.\u00a0 The local government worked with Professor Nengpan Ju and Professor Qiang Xu from SKLGP<\/a> to install a real-time monitoring system on the landslide.\u00a0 This system is based on the analysis of patterns of deformation of the landslide (see Xu et al.<\/em> 2017)<\/a>, and in particular it uses artificial intelligence to identify accelerating trends in movement data, sending an alert message when key thresholds were met.\u00a0 In this case, the system identified such a trend over the day preceding the landslide, issuing a “caution” (yellow) warning on 13th February and a “vigilance” (orange) warning on 15th February.\u00a0 E<\/span>arly in the morning on 17th February 2019 a red alert was issued, allowing 400 people from the area around the slope to be evacuated.\u00a0 The slope failed later that day.\u00a0 <\/span><\/p>\n The image below shows the movement record of the landslide:-<\/span><\/p>\n The movement record of the Longjin landslide as it accelerated to failure. Data provided by SKLGP<\/a>, used with permission.<\/p><\/div>\n .<\/p>\n In the main graph the green triangles show the displacement of a sensor on the landslide with time, showing the characteristic hyperbolic acceleration to failure<\/a>.\u00a0 The blue stars represent the analytical approach used to trigger the alerts, which are shown in the bar chart at the foot of the image.<\/p>\n This is not the first time that this system has been successfully deployed. In 2017 SKLGP<\/a> report that it was used to predict the failure of two loess landslides in Gansu province, at Heifangtai in Yongjing County<\/a>.<\/p>\n The development of warning systems for landslides continues to be a challenging but exciting area, and there is little doubt that in certain circumstances they can be a useful tool.\u00a0 In this case the system has played a major role in increasing public safety, which is very welcome.<\/p>\n Reference<\/p>\n Xing Zhu, Qiang Xu, Xing Qi, Hanxiang Liu. 2017. A Self-adaptive Data Acquisition Technique and Its Application in Landslide Monitoring<\/a>. In WLF 2017: Advancing Culture of Living with Landslides<\/em> pp 71-78. https:\/\/link.springer.com\/chapter\/10.1007%2F978-3-319-53487-9_7<\/a><\/p>\n![\"Longjing](\"https:\/\/blogs.agu.org\/landslideblog\/files\/2019\/02\/before-sliding-photo-e1550818611351.jpg\")
![\"Longjing](\"https:\/\/blogs.agu.org\/landslideblog\/files\/2019\/02\/Fig-2-e1550819425836.jpg\")
Acknowledgement<\/h4>\n