Article
Environmental Sciences
Yifan Sheng, Guangli Xu, Bijing Jin, Chao Zhou, Yuanyao Li, Weitao Chen
Summary: Landslide susceptibility mapping is important for assessing landslide risks. This study integrates spatio-temporal probability analysis and MT-InSAR method to dynamically map landslide hazards. The results identify key factors for landslide development and show that machine learning methods, particularly CNN, outperform statistical methods in accuracy. Adopting the CNN approach can enhance LSM accuracy.
Article
Engineering, Geological
Ugur Ozturk, Massimiliano Pittore, Robert Behling, Sigrid Roessner, Louis Andreani, Oliver Korup
Summary: Contemporary landslide research often focuses on predicting and mapping susceptibility to slope failure using generalized linear models, however the impact of sample size, location, or time on model performance remains largely untested. Findings suggest that considering only lower parts of landslides can significantly improve model performance, particularly for medium-sized landslides. Additionally, model performance shows marginal variation when progressively updating and adding more landslide data through time.
Article
Engineering, Geological
Rui Yuan, Jing Chen
Summary: A novel deep learning-based method for national-scale landslide hazard assessment was proposed in this study. It includes three stages: landslide susceptibility analysis, landslide temporal probability prediction, and quantitative landslide hazard calculation. The validation in the conterminous United States showed excellent performance of this method. Therefore, it has practical significance for national-scale landslide hazard assessment.
Article
Environmental Sciences
Yiming Mao, Deborah Simon Mwakapesa, Kaibin Xu, Chen Lei, Youcun Liu, Maosheng Zhang
Summary: This study compared the performance of Wave-cluster and DBSCAN clustering algorithms in landslide susceptibility assessment in Baota District, China, and found that the Wave-cluster model outperformed the DBSCAN model.
ENVIRONMENTAL EARTH SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Chiara Martinello, Claudio Mercurio, Chiara Cappadonia, Viviana Bellomo, Andrea Conte, Giampiero Mineo, Giulia Di Frisco, Grazia Azzara, Margherita Bufalini, Marco Materazzi, Edoardo Rotigliano
Summary: The quality of landslide inventories used for calibration greatly affects the quality of the model and its prediction image in statistical landslide susceptibility evaluation. This research proposes a two-step susceptibility modeling procedure to verify and solve the limitations caused by the incompleteness and mapping inaccuracy of regional-scale inventories. By applying this procedure to the Torto River basin in Italy, it was found that the limitations of the initial models were largely solved by the recalibrated models, suggesting the proposed procedure as a suitable modeling strategy for regional susceptibility studies.
APPLIED SCIENCES-BASEL
(2023)
Article
Geography, Physical
Storm Roberts, Joshua N. Jones, Sarah J. Boulton
Summary: Recent research in Italy showed that landslide susceptibility is controlled by path dependency, leading this study to quantify path dependency in the Nepal Himalaya. The findings confirm the presence of landslide path dependency in Nepal, with characteristics varying based on inventory resolution.
Article
Chemistry, Multidisciplinary
Gaetano Pecoraro, Gianfranco Nicodemo, Rosa Menichini, Davide Luongo, Dario Peduto, Michele Calvello
Summary: This paper presents a procedure to assess the risk level of stretches of roads exposed to slow-moving landslides at the municipal scale. It proposes an analysis method that combines landslide susceptibility maps, a road-damage database developed using Google Street View images, and ground-displacement measurements from satellite SAR images. The results demonstrate the importance of integrating these different approaches and data to understand the behavior of slow-moving landslides affecting road networks.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Geological
William Frodella, Ascanio Rosi, Daniele Spizzichino, Massimiliano Nocentini, Luca Lombardi, Andrea Ciampalini, Pietro Vannocci, Niandry Ramboason, Claudio Margottini, Veronica Tofani, Nicola Casagli
Summary: A study conducted on the High City of Antananarivo, an important cultural heritage site in Madagascar, assessed the landslide hazard in the area. The study found that slope, lithology, creek erosion, and human activities are the main factors affecting landslides, with heavy rainfall being the primary trigger. The study's findings provide fundamental land use management tools for the protection and conservation of the High City.
Article
Green & Sustainable Science & Technology
Jaydip Dey, Saurabh Sakhre, Ritesh Vijay, Hemant Bherwani, Rakesh Kumar
Summary: Landslides pose a significant challenge in mountainous regions like Nainital district in India, where geological structures make the area particularly susceptible. In order to mitigate the risk of landslides, susceptibility mapping using remote sensing and geographic information systems is crucial for identifying vulnerable areas and implementing appropriate measures.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2021)
Article
Geosciences, Multidisciplinary
Zhiyong Fu, Changdong Li, Wenmin Yao
Summary: This study proposes a novel method that improves the performance of landslide susceptibility (LS) models using the TrAdaBoost transfer learning algorithm. The method transfers useful knowledge from one landslide inventory to another, reducing the effort required to collect landslide data. The results demonstrate that when using the TrAdaBoost model with the landslide inventory from the source area, the performance of LS models is significantly improved.
Article
Engineering, Geological
Tianhe Ren, Liang Gao, Wenping Gong
Summary: Rain-induced landslides pose significant threats to the safety and property of people living in mountainous areas worldwide. Analyzing landslide susceptibility during rain events is an effective strategy for risk management and reduction. This study proposes a maximum rolling rainfall index (MRRI) and develops a spatiotemporal landslide susceptibility modeling approach based on this index. The approach is demonstrated to be effective and versatile in the central area of Hong Kong, producing accurate landslide susceptibility maps that align with actual landslide occurrences.
Article
Remote Sensing
Ruilong Wei, Chengming Ye, Yonggang Ge, Yao Li, Jonathan Li
Summary: Accurate landslide segmentation is crucial for disaster mitigation and relief efforts. This study proposes a deep learning network called DGA-Net for accurate point cloud landslide segmentation. The results show the superiority of the proposed method in landslide segmentation.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Environmental Sciences
Svetlana Gantimurova, Alexander Parshin, Vladimir Erofeev
Summary: This article introduces the methodology and techniques of using UAV data for landslide research, and presents the results of landslide susceptibility assessment conducted in practical applications using an indirect heuristic approach in a GIS environment.
Article
Environmental Sciences
Bohao Li, Kai Liu, Ming Wang, Qian He, Ziyu Jiang, Weihua Zhu, Ningning Qiao
Summary: Precipitation is the main factor that triggers landslides. Developing a global dynamic rainfall-induced landslide susceptibility model is crucial for revealing landslide mechanisms, providing accurate landslide susceptibility maps for risk assessment and hazard prediction.
Article
Environmental Sciences
Gonzalo Maragano-Carmona, Ivo J. Fustos Toribio, Pierre-Yves Descote, Luis F. Robledo, Diego Villalobos, Gustavo Gatica
Summary: This study employs machine learning methods to assess the changes in landslide susceptibility in the Central Andes and proposes an implementation for a rainfall-induced landslides early warning system. The results demonstrate that the method accurately predicts landslide changes based on geomorphological features and precipitation conditions. The study also suggests strengthening monitoring of unstable slopes and incorporating landslide early warning into risk management strategies to mitigate the impact of landslides on densely populated areas.
Article
Environmental Sciences
E. Leonarduzzi, R. M. Maxwell, B. B. Mirus, P. Molnar
Summary: This study investigated the impact of subgrid variability in coarse resolution hydrological modeling, showing that the variability in subgrid topography and soil thickness has a significant influence on hydrological processes, potentially leading to underestimation.
WATER RESOURCES RESEARCH
(2021)
Article
Environmental Sciences
Francesco Fusco, Benjamin B. Mirus, Rex L. Baum, Domenico Calcaterra, Pantaleone De Vita
Summary: Incorporating the influence of soil layering and local variability into physics-based numerical models for distributed landslide susceptibility assessments remains a challenge, but a multi-scale approach has been proposed in this study to account for these factors. This approach allows for accurate simulation of slope failures without modifying the model structure, and has been successfully applied to assess landslide hazard in complex layered soil environments.
Article
Geosciences, Multidisciplinary
Alessandro Cesare Mondini, Fausto Guzzetti, Kang-Tsung Chang, Oriol Monserrat, Tapas Ranjan Martha, Andrea Manconi
Summary: Landslides are geomorphological processes with serious threats to people, property, and the environment on all continents. Investigators have shown increasing interest in using Synthetic Aperture Radar (SAR) imagery for landslide detection and mapping, but challenges remain to be faced for effective utilization.
EARTH-SCIENCE REVIEWS
(2021)
Article
Geosciences, Multidisciplinary
John R. Nimmo, Kim S. Perkins, Michelle R. Plampin, Michelle A. Walvoord, Brian A. Ebel, Benjamin B. Mirus
Summary: The unsaturated zone plays a crucial role in land and water resource management by controlling water flow and reducing vulnerability to contaminants. Rapid flow and transport in the unsaturated zone are becoming more common due to extreme hydroclimatic events, yet they are poorly understood. Scaling issues pose challenges in accurately representing these processes at larger spatial scales.
FRONTIERS IN EARTH SCIENCE
(2021)
Article
Engineering, Geological
Eric S. Hinds, Ning Lu, Benjamin B. Mirus, Jonathan W. Godt, Alexandra Wayllace
Summary: The study found that seasonal failures at the Straight Creek landslide were caused by rapid infiltration of snowmelt and differences in hydraulic conductivity between slope materials and the highway embankment. Remediation designs such as lightweight caissons and horizontal drains were implemented, but their effectiveness was limited by the low hydraulic conductivity of subsurface materials. Results suggest that an alternative drain design intercepting subsurface flow could improve stability during critical periods.
ENGINEERING GEOLOGY
(2021)
Article
Geography, Physical
Guoqiang Jia, Massimiliano Alvioli, Stefano Luigi Gariano, Ivan Marchesini, Fausto Guzzetti, Qiuhong Tang
Summary: This study proposes a non-susceptibility analysis method for selecting locations with negligible landslide occurrence likelihood, simplifying classification methods and applying it globally. The global map, which classifies 82.9% of landmasses with negligible landslide susceptibility, can be used for non-exposure analysis, land planning, and disaster response. Population and settlements are denser within non-susceptible areas, making the map potentially valuable for global-scale applications.
Article
Environmental Sciences
Ivan Marchesini, Paola Salvati, Mauro Rossi, Marco Donnini, Simone Sterlacchini, Fausto Guzzetti
Summary: The study introduces a data-driven and statistically-based procedure, Flood-SHE, for delineating potential areas of river floods. Results demonstrate that Flood-SHE accurately identifies potentially inundated areas, delineating larger areas compared to physically-based models, depending on the quality of flood information. This new data-driven approach shows promise for predicting flood risk and could be used where traditional hydrological models are not available.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Engineering, Geological
Massimiliano Alvioli, Michele Santangelo, Federica Fiorucci, Mauro Cardinali, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Fausto Guzzetti, Silvia Peruccacci
Summary: The study utilizes a data-driven approach to determine rockfall source points and simulate trajectories, extracting probabilistic maps of slope values based on expert mapping of potential source areas. Simulated trajectories are used to analyze rockfall susceptibility of railway segments, resulting in a network-ranked susceptibility map.
ENGINEERING GEOLOGY
(2021)
Article
Engineering, Geological
Luigi Lombardo, Hakan Tanyas, Raphael Huser, Fausto Guzzetti, Daniela Castro-Camilo
Summary: The article presents the development of landslide hazard assessment methods and introduces a statistically-based model capable of predicting aggregated landslide extent, which was tested on a global dataset. This new spatial predictive paradigm could be a breakthrough in landslide hazard studies and potentially become part of official landslide risk assessment protocols in the future.
ENGINEERING GEOLOGY
(2021)
Article
Multidisciplinary Sciences
Alessandro C. Mondini, Fausto Guzzetti, Massimo Melillo
Summary: Rainfall-triggered landslides pose threats to people and the environment in all mountain ranges. Due to projected climate changes, the risk of landslides is expected to increase, emphasizing the need to anticipate their occurrence. This study proposes a deep-learning based strategy to link rainfall to landslide occurrence, which effectively predicts their location and timing, opening up the possibility of operational landslide forecasting based on rainfall measurements and meteorological forecasts.
NATURE COMMUNICATIONS
(2023)
Article
Geosciences, Multidisciplinary
J. B. Woodard, B. B. Mirus, M. M. Crawford, D. Or, B. A. Leshchinsky, K. E. Allstadt, N. J. Wood
Summary: Landslide susceptibility maps show the likelihood of landslide occurrence in different areas. Developing models for large or diverse terrains is challenging due to limited landslide data and variability in triggering conditions. This study introduces a statistical framework to evaluate the effects of different sampling strategies on model accuracy, and highlights the importance of using uniformly distributed data for training over spatially isolated data.
JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE
(2023)
Article
Geosciences, Multidisciplinary
Silvia Peruccacci, Stefano Luigi Gariano, Massimo Melillo, Monica Solimano, Fausto Guzzetti, Maria Teresa Brunetti
Summary: Italy is frequently affected by landslides, which cause significant disruptions to the population, communication infrastructure, and economy. To mitigate landslide risks, accurate landslide catalogues are needed. ITALICA, the largest catalogue of rainfall-induced landslides in Italy, provides detailed and precise information on 6312 landslides that occurred between January 1996 and December 2021, making it crucial for decision-making and landslide risk management.
EARTH SYSTEM SCIENCE DATA
(2023)
Review
Engineering, Environmental
Jordi Corominas, Fausto Guzzetti, Hengxing Lan, Renato Macciotta, Cristian Marunteranu, Scott McDougall, Alexander Strom
Summary: Significant effort has been made to develop methodologies for landslide hazard and risk assessment, but there is still debate on the usage of terms and their implementation. Harmonization of methodologies and terminology is necessary to facilitate communication within the landslide community and with stakeholders from other disciplines. In 2016, the IAEG established a working group to prepare a multilingual glossary for landslide hazard and risk terms, aiming for international harmonization.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2023)
Article
Engineering, Electrical & Electronic
Savinay Nagendra, Daniel Kifer, Benjamin Mirus, Te Pei, Kathryn Lawson, Srikanth Banagere Manjunatha, Weixin Li, Hien Nguyen, Tong Qiu, Sarah Tran, Chaopeng Shen
Summary: This paper investigates pixel-wise labeling of landslide areas in remotely-sensed images using deep learning, focusing on large-scale heterogeneous landslide data collection. The study introduces a mechanism for incremental training of semantic segmentation models, known as task-specific model updates (TSMU), which can be utilized for creating new landslide inventories or developing hazard maps following landslide events.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Geosciences, Multidisciplinary
Fausto Guzzetti
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
(2021)