Italy-Japan joint workshop on landslide monitoring systems and related topics
Outline: existing real-time landslide monitoring systems developed in Italy, by CNR IRPI (the Institute for Geo-Hydrological Research of the National Research Council), and in Japan, by GSI Japan (the Geospatial Information Authority of Japan) will be described and discussed. The systems, intended to monitor either rainfall induced or earthquake induced landslides, are operational and capable of producing daily or sub-daily bulletins containing information about the likelihood of landslide occurrence throughout the country. Contributions to the workshop will describe the systems, their current state of operations, their input and output and their stakeholders. Additional contributions will discuss planned and possible developments of the systems, and additional topics related to landslide susceptibility assessment and monitoring. Eventually, a few contributions will discuss closely related topics of interest of both parties.
Information: contact the organizers
– M. Alvioli – massimiliano.alvioli [AT] irpi.cnr.it
– J. Iwahashi – iwahashi-j96pz [AT] mlit.go.jp
Programme – November, 4th
|9:15-9:45||17:15-17:45||Mauro ROSSI||–||CNR IRPI||I1|
|10:05-10:25||18:05-18:25||Yuki MATSUSHI||–||Kyoto Univ.||J7|
|10:30-10:50||18:30-18:50||Daniele GIORDAN||CNR IRPI||I5|
|10:50-11:10||18:50-19:10||Luca SCHENATO||–||CNR IRPI||I6|
|11:10-11:30||19:10-19:30||Neelima SATYAM||IIT Indore||J5|
Programme – November, 5th
|8:30-9:00||16:30-17:00||Junko IWAHASHI||GSI Japan||J1|
|9:00-9:20||17:00-17:20||Takayuki NAKANO||–||GSI Japan||J2|
|9:20-9:40||17:20-17:40||Ryo ENDO||–||GSI Japan||J3|
|9:40-10:00||17:40-18:00||Mio KASAI||Hokkaido Univ.||J4|
|10:05-10:25||18:05-18:25||Paola REICHENBACH||–||CNR IRPI||I2|
|10:25-10:45||18:25-18:45||Gaetano FALCONE||CNR IGAG||I4|
|10:45-11:05||18:45-19:05||Massimiliano ALVIOLI||–||CNR IRPI||I3|
Contributions (Speaker, affiliation, title, short abstract) – Italian side:
(I1) Mauro ROSSI (CNR IRPI, Perugia): Landslide early warning: lessons learned after 10-year experience in Italy.
Description of our monitoring system for rainfall-induced landslides which we have been running for more than ten years, its functionalities and the different customizations of the system we prepared for different users/stakeholders (Department of civil protection, two administrative sub-regions of Italy, and the national railway company).
(I2) Paola REICHENBACH (CNR IRPI, Perugia): Description of the on-going project FRA.SI (Seismically induced landslides)
Project funded by the Italian Ministry of Environment, ending December 2021, which involves two additional Institutes (CNR IGAG and CNR IREA), in addition to CNR IRPI. The project aims at local, regional and national analysis of seismically induced landslides. We are considering setting up a real-time monitoring system for seismically induced rockfalls, within the scope of the project.
(I3) Massimiliano ALVIOLI (CNR IRPI, Perugia): Physically based simulations of seismically induced rockfalls.
We discuss the possibility of applying STONE, a three-dimensional model for the simulation of rockfalls, on a regional scale, for seismically-induced rockfall events. We show a method to select locations of potential rockfall sources in two different modes, namely a static mode and a dynamic mode. Static sources are selected with a data-driven method, on the basis of expert mapping of a representative set of sources; dynamic sources are obtained with a “damping” procedure of static sources, based on intensity of seismic shaking. The method, in principle, allows application in near-real time, when seismic shaking maps are available after an earthquake event.
(I4) Gaetano FALCONE (CNR IGAG): Ground motion prediction maps including site effects for Near-Real-Time Assessment of Seismically Induced Landslide
Ground motion maps, produced a few hours after a seismic event, represent the key input for the rapid assessment of earthquake triggered landslides scenario maps in near real time. The IGAG approach improves the prediction of these maps using machine learning approach and seismic microzonation database.
(I5) Daniele GIORDAN (CNR IRPI, Torino): Innovative characterization and monitoring solutions for rockfall prone areas
The use of digital outcrop models acquired by drones can be very useful for identifying and mapping main discontinuities. Then, the recent development of codes aimed to identify the critical combination of fractures can be adopted to localize the most critical sectors of the studied area, for what concerns rockfalls. The project LASMON (landslide smart monitoring network) has developed innovative solutions for the use of small network nodes aimed to monitor the potential impact of rock blocks over rock barriers.
(I6) Luca SCHENATO (CNR IRPI, Padova): Fiber optic sensors for landslides and rockfall monitoring
Optical fiber sensors have shown to be a promising technology platform to monitor landslides and rockfalls. CNR-IRPI has been active on the topic for many years by developing, among the others, a distributed strain sensing system for rainfall-triggered shallow landslides, an acoustic and vibration system for rockfall detection, and a distributed acoustic sensing system for debris flows monitoring.
Contributions (Speaker, affiliation, title, short abstract) – Japanese side:
(J1) Junko IWAHASHI (GSI Japan): Seismic Ground Disaster Assessment System (SGDAS) of GSI Japan
GSI Japan is operating a system called “SGDAS”. The system estimates the approximate location and possibility of landslides and liquefaction in the event of a strong earthquake, generates PDF reports of the estimated results, and sends it to the relevant organizations by e-mail. The entire process operates automatically, and has been able to deliver the report within 15 minutes of the earthquake occurrence. The system was developed about 10 years ago and is scheduled to be upgraded over the next 5 years. The presenter will give an overview of the system and its prospects.
(J2) Takayuki NAKANO (GSI Japan)：Research for the improvement of SGDAS – liquefaction
“SGDAS” can estimate the approximate area/possibility of liquefaction from the relationship between the seismic intensity and geomorphologic classification data. However, the estimation algorithm needs to be improved because the estimation accuracy is not enough high. We will try to improve the estimation algorithm by reviewing the relationship between past liquefaction sites, geomorphologic classification, and seismic intensity.
(J3) Ryo ENDO (GSI Japan)：Research for the improvement of SGDAS – prior rainfall
“SGDAS” estimates the approximate area/possibility of landslides using seismic, geomorphological and geological data. However, SGDAS does not take into account soil moisture. We focus on prior rainfall before earthquake that impact on soil moisture and review the influence of the prior rainfall on landslides. Then, we examine the relationship between the prior rainfall and the SGDAS estimation accuracy.
(J4) Mio KASAI (Hokkaido University): A new index representative of seismic cracks to assess post-seismic landslide susceptibility.
A strong earthquake induces many open cracks, some of which show signs of subsequent landslides. We developed a simple index representing the spatial density of seismic cracks (DCI) to express the degree of local ground disturbance caused by strong tremors during a main shock. The index was proven an important factor in assessing the susceptibility of post-seismic landslides; however, the value is sensitive to data errors and requires care in its application.
(J5) Neelima SATYAM (IIT Indore): Integration of rainfall thresholds and field monitoring data: case studies from Japan and India
The applications of a set of equipment for monitoring unstable slopes, which is equipped with a Micro Electro Mechanical Systems (MEMS) tilt sensor and a volumetric water content sensor is discussed. The sensors were first tested in Japan, then in China and have been installed in Darjeeling Himalayas in India. Two case studies demonstrating the field applications of the sensors are discussed in detail, one from Japan and one from India. Both the studies point towards the possible applications of using the MEMS based tilt sensors for developing landslide early warning systems.
(J6) Yusuke SAKAI (National Institute for Land and Infrastructure Management): Study on seismic motion characteristics that induce numerous sediment disasters by spectral analysis of observed seismic waves
In order to analyze the relationship between the spectral characteristics of seismic motion and sediment disaster occurrence, we compared the acceleration response spectra of earthquakes with numerous sediment disasters and earthquakes with few sediment disasters. As a result, earthquakes with numerous sediment disasters may have stronger long-period component in acceleration response spectra, compared short-period component.
(J7) Yuki MATSUSHI (Disaster Prevention Research Institute, Kyoto University): Prediction of shallow landslides by rainstorm based on hydro-geomorphological modeling of hillslopes processes: dynamic hazard mapping toward disaster mitigation
This study examined a methodology for predicting location, magnitude, and timing of rain-induced shallow landslides. Simulation of soil production and transport on a digital terrain model provides the thickness of sliding material on hillslopes. Shear strength of the bulk soil, incorporating reinforcement effects by tree roots was evaluated through geotechnical tests and in-situ survey at soil pits. Hydrological monitoring was carried out at a representative hillslope for modeling the fluctuation in subsurface pore-water pressure by rainwater infiltration. By coupling all of those data and modeling, hillslope stability was analyzed on a geographic information system, and then compared the output with a landslide inventory map in an actual disaster case to confirm the accuracy and precision of the prediction. (Keywords: shallow landslide, soil, shear strength, tree roots, rainwater infiltration, hillslope stability).
Participants (in alphabetical order)
|Alessandro Pasuto||CNR IRPI||Padova||Italy|
|Ali P. Yunus||National Institute for Environmental Studies||Tsukuba||Japan|
|Badal Pokharel||University of New South Wales||Sidney||Australia|
|Carolina Fortunato||CRN IGAG||Rome||Italy|
|Chiara Varone||CNR IGAG||Rome||Italy|
|Daniele Giordan||CNR IRPI||Torino||Italy|
|Danilo Godone||CNR IRPI||Torino||Italy|
|Federica Fiorucci||CNR IRPI||Perugia||Italy|
|Federico Mori||CNR IGAG||Rome||Italy|
|Gaetano Falcone||CNR IGAG||Rome||Italy|
|Gioachino Roberti||Minerva Intelligence||Vancouver||Canada|
|Ivan Marchesini||CNR IRPI||Perugia||Italy|
|Junko Iwahashi||Geospatial Information Authority||Tsukuba||Japan|
|Kien Nguyen||Thuyloi University||Hanoi||Vietnam|
|Kshitij Dahal||Chinese Academy of Sciences||???||China|
|Kunal Gupta||Indian Institute of Technology||Indore||India|
|Luca Schenato||CNR IRPI||Padova||Italy|
|Luca SchilirÃ²||CNR IGAG||Rome||Italy|
|Marco Loche||Charles University||Prague||Cekia|
|Maria Teresa Brunetti||CNR IRPI||Perugia||Italy|
|Massimiliano Alvioli||CNR IRPI||Perugia||Italy|
|Massimiliano Moscatelli||CNR IGAG||Rome||Italy|
|Massimo Melillo||CNR IRPI||Perugia||Italy|
|Mauro Rossi||CNR IRPI||Perugia||Italy|
|Michele Santangelo||CNR IRPI||Perugia||Italy|
|Minu Treesa Abraham||Indian Institute of Technology||Indore||India|
|Mio Kasai||Hokkaido University||Sapporo||Japan|
|Monia Coltella||CNR IGAG||Rome||Italy|
|Neelima Satyam||Indian Institute of Technology||Indore||India|
|Paola Imprescia||CNR IGAG||Rome||Italy|
|Paola Reichenbach||CNR IRPI||Perugia||Italy|
|Paolo Tommasi||CNR IGAG||Rome||Italy|
|Raniero Beber||CNR IRPI||Padova||Italy|
|Ryo Endo||Geospatial Information Authority||Tsukuba||Japan|
|Stefano Gariano||CNR IRPI||Perugia||Italy|
|Takao Yamakoshi||National Institute for Land and Infrastructure Management||Tsukuba||Japan|
|Takayuki Nakano||Geospatial Information Authority||Tsukuba||Japan|
|Thuy Le||Thuyloi University||Hanoi||Vietnam|
|Tu Pham||Thuyloi University||Hanoi||Vietnam|
|Tuan Nguyen||Thuyloi University||Hanoi||Vietnam|
|Velio Coviello||CNR IRPI||Padova||Italy|
|Viet Tran||Thuyloi University||Hanoi||Vietnam|
|Yuichi S. Hayakawa||Hokkaido University||Sapporo||Japan|
|Yuki Matsushi||Kyoto University||Japan|
|Yusuke Sakai||National Institute for Land and Infrastructure Management||Tsukuba||Japan|
Local organizing committee
– M. Alvioli – massimiliano.alvioli [AT] irpi.cnr.it
– J. Iwahashi – iwahashi-j96pz [AT] mlit.go.jp