A5026730159- Landslides and related hazards
- Cryospheric studies and observations
- Flood Risk Assessment and Management
- Geotechnical Engineering and Analysis
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Tree Root and Stability Studies
- Soil and Unsaturated Flow
- Dam Engineering and Safety
- Soil erosion and sediment transport
- Fire effects on ecosystems
- Hydrology and Watershed Management Studies
- Seismology and Earthquake Studies
- Anomaly Detection Techniques and Applications
- Hydrology and Sediment Transport Processes
- Remote Sensing and Land Use
- Soil Moisture and Remote Sensing
- Rock Mechanics and Modeling
- Remote Sensing in Agriculture
- Climate change and permafrost
- Remote Sensing and LiDAR Applications
- 3D Surveying and Cultural Heritage
- Land Use and Ecosystem Services
- Geochemistry and Geologic Mapping
- Remote-Sensing Image Classification
- Aeolian processes and effects
University of Padua
2020-2024
Indian Institute of Technology Roorkee
2023
University of Kerala
2023
China University of Geosciences (Beijing)
2023
Hokkaido University
2023
Chengdu University of Technology
2023
Indian Institute of Science Education and Research Mohali
2023
University of Florence
2012-2022
Centre Tecnologic de Telecomunicacions de Catalunya
2021
Massachusetts Institute of Technology
2001-2021
This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional national), as well verification validation results. The described focus on evaluation probabilities occurrence types with certain characteristics. Methods used to determine distribution intensity, characterisation elements risk, assessment potential degree damage quantification those perform are also described. is...
Abstract. Despite the large number of recent advances and developments in landslide susceptibility mapping (LSM) there is still a lack studies focusing on specific aspects LSM model sensitivity. For example, influence factors such as survey scale conditioning variables (LCVs), resolution unit (MUR) optimal ranking LCVs have never been investigated analytically, especially data sets. In this paper we attempt experimentation concentrating impact tuning choice final result, rather than...
This paper concerns a regional scale warning system for landslides that relies on decisional algorithm based the comparison between rainfall recordings and statistically defined thresholds. The latter were total amount of rainfall, which was cumulated considering different time intervals: 1-, 2- 3-day cumulates took into account critical influencing shallow movements, whilst variable interval cumulate (up to 240 days) used consider triggering deep-seated in low permeability terrains. A...
: The measurement of landslide superficial displacement often represents the most effective method for defining its behavior, allowing one to observe relationship with triggering factors and assess effectiveness mitigation measures. Persistent Scatterer Interferometry (PSI) a powerful tool measure displacement, as it offers synoptic view that can be repeated at different time intervals various scales. In many cases, PSI data are integrated in situ monitoring instrumentation, since joint use...
In this paper, the updating of landslide inventory Tuscany region is presented. To achieve goal, satellite SAR data processed with persistent scatter interferometry (PSI) technique have been used. The leads to a consistent reduction unclassified landslides and an increasing active landslides. After updating, we explored characteristics new inventory, analysing distribution geomorphological features. Several maps elaborated, as sliding index or density map; also propose density-area map...
Abstract Event-based landslide inventories are essential sources to broaden our understanding of the causal relationship between triggering events and occurring landslides. Moreover, detailed crucial for succeeding phases risk studies like susceptibility hazard assessment. The openly available differ in quality completeness levels. created based on manual interpretation, there can be significant differences mapping preferences among interpreters. To address this issue, we used two different...
To perform landslide susceptibility prediction (LSP), it is important to select appropriate mapping unit and landslide-related conditioning factors. The efficient automatic multi-scale segmentation (MSS) method proposed by the authors promotes application of slope units. However, LSP modeling based on these units has not been performed. Moreover, heterogeneity factors in neglected, leading incomplete input variables modeling. In this study, extracted MSS are used construct modeling,...
Multiple landslide events are common around the globe. They can cause severe damage to both human lives and infrastructures. Although a huge quantity of research has been shaped address rapid mapping landslides by optical Earth Observation (EO) data, various gaps uncertainties still present when dealing with cloud obscuration 24/7 operativity. To issue, we explore usage SAR data over eastern Iburi sub-prefecture Hokkaido, Japan. In area, about 8000 co-seismic were triggered an Mw 6.6...
Abstract Accurate early warning systems for landslides are a reliable risk-reduction strategy that may significantly reduce fatalities and economic losses. Several machine learning methods have been examined this purpose, underlying deep (DL) models’ remarkable prediction capabilities. The long short-term memory (LSTM) gated recurrent unit (GRU) algorithms the sole DL model studied in extant comparisons. However, several other suitable time series forecasting tasks. In paper, we assess,...
Most literature related to landslide susceptibility prediction only considers a single type of landslide, such as colluvial rock fall or debris flow, rather than different types, which greatly affects performance. To construct efficient considering Huichang County in China is taken example. Firstly, 105 falls, 350 landslides and 11 environmental factors are identified. Then four machine learning models, namely logistic regression, multi-layer perception, support vector C5.0 decision tree...
Abstract The numerical simulation and slope stability prediction are the focus of disaster research. Recently, machine learning models commonly used in prediction. However, these have some problems, such as poor nonlinear performance, local optimum incomplete factors feature extraction. These issues can affect accuracy Therefore, a deep algorithm called Long short-term memory (LSTM) has been innovatively proposed to predict stability. Taking Ganzhou City China study area, landslide inventory...
Due to the similarity of conditioning factors, aggregation feature landslides and multi-temporal landslide inventory, spatial temporal effects need be considered in susceptibility prediction (LSP). The ignorance this issue will result some biases time-invariance susceptibility. Hence, a novel framework has been proposed update by simultaneously considering at regional scale. In framework, inventory Chongyi County divided into pre- fresh-landslide inventories. According LSP results predicted...
Abstract Mapping of landslides over space has seen an increasing attention and good results in the last decade. While current methods are chiefly applied to generate event-inventories, whereas multi-temporal (MT) inventories rare, even using manual landslide mapping. Here, we present innovative deep learning strategy which employs transfer that allows for Attention Deep Supervision Multi-Scale U-Net model be adapted detection tasks new areas. The method also provides flexibility re-training...
The accuracy of landslide susceptibility prediction (LSP) mainly depends on the precision spatial position. However, position error survey is inevitable, resulting in considerable uncertainties LSP modeling. To overcome this drawback, study explores influence positional errors uncertainties, and then innovatively proposes a semi-supervised machine learning model to reduce error. This paper collected 16 environmental factors 337 landslides with accurate positions taking Shangyou County China...