Theme 3. Monitoring and Early Warning

Theme 3: Monitoring and Early Warning
Contact: Veronica Tofani <veronica.tofani@unifi.it>

 

Session 3.1 Landslide monitoring and geophysical surveys

 

1 Paola Revellino Italy Defining kinematic and evolutive features of earth flows using integrated monitoring and low-cost sensors
2 Jan Blahůt Czech Republic Monitoring of thermoelastic wave within a rock mass coupling information from IR camera and crack meters: a 24-hour experiment on “Branická skála” Rock in Prague, Czechia
3 David Huntley Canada Field testing innovative differential geospatial and photogrammetric monitoring of a slow-moving landslide, south-central British Columbia, Canada
4 Paolo Allasia Italy The role of measure of deep-seated displacements in the monitoring networks on large-scale landslide
5 Filip Hartvich Czech Republic Multiinstrumental monitoring network Slopenet – new advances
6 Lal Dinpuia India Slope Instabilities Analysis and Monitoring of Aizawl Landslide, Mizoram, Northeast India
7 Jongmans Denis France Geophysical monitoring of landslides: state-of-the art and recent advances
8 Sebastian Uhlemann USA Geophysical monitoring of landslides – A step closer towards predictive understanding?
9 Jim Whiteley UK Recent advances in high spatial resolution geophysical monitoring of moisture-induced landslides
10 Hao Luo China Characteristic analysis of the Nayong rock avalanche based on the seismic signal
11 Liang Feng Italy Rockfall detection and early warning using micro-seismic monitoring
12 Yu Zhuang China Electrical resistivity tomography (ERT) based investigation of two landslides in Guizhou, China
13 Kiminori Araiba Japan Vibration of Piled Rocks – Which rock can be removed ?

 

Session 3.2 Remote sensing for landslide risk management

 

1 Mihai Niculita Romania LiDAR and UAV SfM for landslide monitoring
2 Paolo Mazzanti Italy Recent developments in photomonitoring
3 Ko-Fei Liu Chinese Taipei Debris flow detection with video camera
4 Giulia Tessari Switzerland Comparison between PS and SBAS InSAR techniques in monitoring shallow landslides
5 Ying Liu China Remote sensing monitoring of landslides along highways
6 Anna Barra Spain Sentinel-1 landslides detection: the Granada coast
7 Chaoying Zhao China Landslide Dynamic Deformation Monitoring with Sequential Least Squares Based SAR/InSAR techniques
8 D Jean Hutchinson Canada Towards managing debris channel risks to infrastructure: Understanding debris processes using remotely sensed data.

 

Session 3.3 Landslide early warning systems

 

1 Gaetano Pecoraro Italy Definition and first application of a probabilistic warning model for rainfall-induced landslides
2 Katerina Kavoura Greece Establishment of an integrated landslide early warning and monitoring system in populated areas
3 Nguyen Duc Ha Vietnam An Integrated WebGIS System for Shallow Landslide Hazard Early Warning
4 Adrian Wicki Switzerland The value of soil wetness measurements for regional landslide Early Warning Systems
5 John Singer Germany Technical concepts for an early warning system for rainfall induced landslides in informal settlements
6 Agus Setyo Muntohar Indonesia Development of Landslide Early Warning System based on the Satellite-Derived Rainfall Threshold in Indonesia
7 Qiang Xu China Presenting Some Successful Cases of Regional Landslides Early Warning Systems in China
8 Klaus-Peter Keilig Germany Towards an early warning system for instable slopes in Gorgia The large Tskneti Akhaldaba landslide
9 Lin Wang Japan An EWS of landslide and slope failure by MEMS tilting sensor array
10 Piciullo Luca Norway Standards for the performance assessment of territorial landslide early warning systems
11 Zongji Yang China Application and verification of a multivariate real-time early warning method for rainfall-induced landslides: implication for evolution of landslide-generated debris flows Landslides
12 Michele Calvello Italy LandAware: a new international network on Landslide Early Warning Systems
13 Chih-Chung Chung Chinese Taipei The Development of TDR-integrated landslide Early Warning System
14 Thom Bogaard Netherlands What hydrological information should we include in landslide hazard assessment and Early Warning Systems?
15 Teuku Faisal Fathani Indonesia Global standard for multi-hazards early warning system
16 Masashi Sekiguchi Japan Need for Information Disclosure of Landslide Early Warning Systems
17 Imaya Ariyarathna Japan The time prediction Method of an onset of rainfall induced landslides for early warning

 

 

Session 3.4 Forecasting models and time predictions of landslides

 

1 Maria Teresa Brunetti Italy Regional approaches in forecasting rainfall-induced landslides
2 Graziella Devoli Norway Seven years of landslide forecasting in Norway – strengths and limitations
3 Hyuck-Jin Park Republic of Korea Probabilistic modelling of uncertainties in physically based landslide susceptibility assessment
4 Veronica Tofani Italy Characterization of hillslope deposits for physically-based landslide forecasting models
5 Judith Uwihirwe Netherlands Landslide precipitation thresholds in Rwanda
6 Nikhil Nedumpallile Vasu UK Methodology for developing a preliminary hydrological threshold for rainfall-induced landslides in Kuala Lumpur city, Malaysia
7 Brenda Rosser New Zealand Development of a Rainfall-induced Landslide Forecast Tool for New Zealand
8 Naoki Iwata Japan Influence of intervals measuring surface displacement on time prediction of slope failure using Fukuzono Method
9 Katsuo Sasahara Japan Velocity and acceleration of surface displacement in sandy model slope with various slope conditions
10 Praveen Kumar India Comparison of Moving-average, Lazy, and Information Gain Methods for Predicting Weekly Slope-movements: A Case-study in Chamoli, India
11 Antoinette Tordesillas Australia New insights into the spatiotemporal precursory failure dynamics of the 2017 Xinmo landslide and its surrounds
12 Martin Krkač Croatia A comparative study of random forests and multiple linear regression in the prediction of landslide velocity
13 Adriaan van Natijne Netherlands Machine Learning: Potential for Deep-Seated Landslide Nowcasting