Article
Engineering, Environmental
Yan Liu, Hemanta Hazarika, Haruichi Kanaya, Osamu Takiguchi, Divyesh Rohit
Summary: Under heavy rainfall, a low-cost and sustainable landslide early warning system (LEWS) that integrates the Internet of Things (IoT) and off-the-grid solar energy-powered platform has been developed. The system provides timely warnings by utilizing data on soil moisture content, pore water pressure, deflection angle, and real-time factor of safety. Slope model tests have confirmed its feasibility and its capability of identifying risk levels, sending warning signals, and predicting potential movement for risk management. The LEWS's low cost and standalone energy harvesting feature make it applicable worldwide.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2023)
Article
Engineering, Geological
Gaetano Pecoraro, Michele Calvello
Summary: The study proposed a methodology that integrates meteorological monitoring and pore water pressure measurements, which was tested in 30 hydrological basins in Norway. By analyzing data on weather-induced landslides, meteorological monitoring, and pore water pressure measurements, an effective territorial warning model was developed, providing accurate identification and warning of landslide events.
Article
Geosciences, Multidisciplinary
Yan Du, Lize Ning, Santos D. Chicas, Mowen Xie
Summary: Traditional early warning criteria have limitations and can lead to false alarms. A new landslide warning criterion and method are proposed to accurately determine landslide risk changes and reduce false alarms.
Article
Environmental Studies
Moritz Gamperl, John Singer, Carolina Garcia-Londono, Lisa Seiler, Julian Castaneda, David Ceron-Hernandez, Kurosch Thuro
Summary: Fatalities from landslides are increasing globally, especially in mountainous regions where cities expand into steep slopes. Integrated landslide early warning systems (LEWS) can be a viable solution for residents, particularly those in poor neighborhoods and informal settlements, if they are affordable and replicable. We developed a LEWS prototype in Medellin, Colombia, with the involvement and support of local stakeholders, including authorities, agencies, NGOs, and the community. The system, which integrates into the social structure of the neighborhood, provides precise deformation and trigger measurements, and initial data from the measurement system are now available for defining thresholds and future automatic early warnings.
Article
Geosciences, Multidisciplinary
Prakash Singh Thapa, Basanta Raj Adhikari, Rajib Shaw, Diwakar Bhattarai, Seiji Yanai
Summary: The Nepal Himalayas is prone to landslides, which are caused by seismic activity, monsoon rainfall, and improper land-use practices. Mitigating and preventing landslides is challenging for countries like Nepal, but low-cost techniques like bioengineering combined with affordable early warning systems have been effective. A case study in Lalitpur district analyzed landslide geomorphology, triggering factors, and evaluated the effectiveness of a landslide early warning system (LEWS). The LEWS measures rainfall, soil moisture, and displacement activity to generate alarms for nearby residents, reducing landslide risk at the community level.
Article
Environmental Sciences
Tsai-Tsung Tsai, Yuan-Jung Tsai, Chjeng-Lun Shieh, John Hsiao-Chung Wang
Summary: Typhoon Morakot had a serious impact on Taiwan, especially in terms of large-scale landslides (LSL). This study aimed to establish a specific relationship between LSL and triggering rainfall for future early warning predictions. By collecting various data, including satellite imagery, field investigation data, major event reports, and seismic data, the study analyzed rainfall/landslide depth and friction angle/slope through linear and non-linear regression analysis. The results showed that the non-linear regression analysis had a better correlation trend and could provide more conservative indicators for early warning management. Incorporating real-time rainfall forecasts, the study suggested that these indicators could be used to guide evacuation operations and improve response time.
Article
Multidisciplinary Sciences
Xiangpeng Wang, Kunpeng Wang, Fanqiang Lin, Kai Guo
Summary: Based on the analysis of the formation mechanism, stability state, and development trend, this paper focuses on monitoring key indicators such as surface deformation and rainfall to provide early warning and guidance for landslide disaster prevention in the alpine mountainous area of Nangqian, Qinghai Province.
SCIENTIFIC REPORTS
(2022)
Article
Chemistry, Analytical
Marco Esposito, Lorenzo Palma, Alberto Belli, Luisiana Sabbatini, Paola Pierleoni
Summary: This paper provides a literature review on Internet of Things solutions in the field of Early Warning for different natural disasters. It describes the adopted IoT architectures, defines the constraints and requirements of an Early Warning system, and determines the most used solutions in the four examined use cases. The review highlights the main gaps in literature and suggests integrating a Fog/Edge layer in the developed IoT architectures for improved performance.
Article
Chemistry, Multidisciplinary
Dongxin Bai, Guangyin Lu, Ziqiang Zhu, Xudong Zhu, Chuanyi Tao, Ji Fang
Summary: The data collection in automated landslide monitoring is characterized by large amounts of data, periodic fluctuations, outliers, and different collection intervals. This paper proposes a hybrid early warning method for landslide acceleration based on automated monitoring data, which combines traditional warning methods and critical sliding warning methods based on normalized tangent angle. Experiments show that the proposed method accurately identifies accelerating deformation of landslides with minimal false warnings.
APPLIED SCIENCES-BASEL
(2022)
Article
Geosciences, Multidisciplinary
Taorui Zeng, Thomas Glade, Yangyi Xie, Kunlong Yin, Dario Peduto
Summary: This study applies deep learning algorithm and landslide evolution model in long-term warning systems, using the Sifangbei landslide in the Three Gorges reservoir area of China as a test site. The results show that the combination of deep learning and evolution methods can predict landslide displacement and evolution stages, which can contribute to the establishment of long-term warning systems.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2023)
Article
Forestry
Yanhui Liu, Junbao Huang, Ruihua Xiao, Shiwei Ma, Pinggen Zhou
Summary: This paper introduces a method for constructing a regional landslide early-warning model based on machine learning and validates it using Fujian Province in China as an example. The research results show that the Random Forest algorithm is the most effective model with high hit rate and accurate warnings.
Article
Environmental Sciences
Yufei Song, Wen Fan, Ningyu Yu, Yanbo Cao, Chengcheng Jiang, Xiaoqing Chai, Yalin Nan
Summary: This study proposes a new method for calculating the spatiotemporal probability of rainfall-induced landslides based on a Bayesian approach and develops a probabilistic-based early warning model at the regional scale. The results show that the proposed model has higher warning accuracy and economic benefits compared to the conventional model.
Article
Engineering, Geological
Won Young Lee, Seon Ki Park, Hyo Hyun Sung
Summary: This study established criteria for a landslide early warning system (LEWS) using a Bayesian model and optimal thresholds for cumulative event rainfall-duration (ED), improving landslide monitoring and warning efficiency.
Article
Computer Science, Interdisciplinary Applications
Zhice Fang, Hakan Tanyas, Tolga Gorum, Ashok Dahal, Yi Wang, Luigi Lombardo
Summary: Traditional landslide early warning systems approximate precipitation-induced landslides based on single precipitation values, while this study uses a modeling architecture inspired by speech-recognition tasks to improve prediction power by considering the full rainfall signal, potentially paving the way for a new generation of speech-recognition-based landslide early warning systems.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Computer Science, Information Systems
Liang Liu, Jiqiu Deng, Yu Tang
Summary: This paper proposes a framework for dynamic management and integration of models in LEWS by using Web APIs and Docker to standardize model interfaces and facilitate model deployment. It also utilizes Kubernetes and Istio to enable microservice architecture, dynamic scaling, and high availability of models. Additionally, a model repository management system is used to manage and orchestrate model-related information and application processes. The results demonstrate that this approach supports efficient deployment, management, and integration of models within the system, providing a rapid and feasible implementation method for upgrading, expanding, and maintaining LEWS in response to changes in business requirements.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2023)
Article
Geography, Physical
Giuseppe Esposito, Stefano Luigi Gariano, Rocco Masi, Stefano Alfano, Gaetano Giannatiempo
Summary: Wildfires in the Campania region of Southern Italy increase the risk of postfire debris flows (PFDFs) due to surface runoff and erosional processes. A catalog of 113 PFDFs that occurred between 2001 and 2021 was analyzed, with an average of 5 events per year and a peak in 2017. The rainfall conditions associated with PFDF initiation were reconstructed and analyzed, showing that these events are not rare or extreme.
Article
Environmental Sciences
Francesca Ardizzone, Stefano Luigi Gariano, Evelina Volpe, Loredana Antronico, Roberto Coscarelli, Michele Manunta, Alessandro Cesare Mondini
Summary: Earth observation data are used to analyze the impact of rainfall on slow-moving landslides. In this study, a quantitative procedure is applied to identify displacement clusters and evaluate their relationship with rainfall series at the regional scale. The study area is the Basento catchment in southern Italy. Rainfall series are collected from rain gauges and analyzed for temporal trends. Ground displacements are obtained using Sentinel-1 images and analyzed in relation to rainfall series using statistical and non-parametric tests. Results show significant correlations for certain rainfall conditions and slope responses. Challenges in the procedure are discussed and potential solutions are proposed. The proposed quantitative procedure can be applied to other study areas.
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
Loredana Antronico, Roberto Coscarelli, Stefano Luigi Gariano, Paola Salvati
Summary: Natural hazards have significant impacts on people, and these impacts are expected to increase with climate change. Sustainable approaches to mitigating these hazards and reducing vulnerability need to be based on an analysis of risk perception. This study investigates the awareness, perception, and preparation of Italian young people regarding natural risks, particularly landslides, floods, and climate change. The results reveal that the surveyed students are aware of climate change and its effects on nature and the environment.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2023)
Article
Geosciences, Multidisciplinary
Faye Wyatt, Joanne Robbins, Rebecca Beckett
Summary: Impact-based forecasts and warnings (IbFWs) are used by meteorological agencies to predict the likelihood and severity of impacts from hydrometeorological hazards. Evaluating the performance of IbFWs is challenging due to the lack of impact observations and biases in publicly available impact data. Different data sources have varying types and magnitudes of bias, affecting the coverage, severity, timing, geographic scale, and accuracy of impact observations. Despite these challenges, using a range of data sources can provide a more comprehensive understanding of hydrometeorological events.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2023)
Article
Engineering, Geological
Marco Donnini, Michele Santangelo, Stefano Luigi Gariano, Francesco Bucci, Silvia Peruccacci, Massimiliano Alvioli, Omar Althuwaynee, Francesca Ardizzone, Cinzia Bianchi, Txomin Bornaetxea, Maria Teresa Brunetti, Mauro Cardinali, Giuseppe Esposito, Susanna Grita, Ivan Marchesini, Massimo Melillo, Paola Salvati, Mina Yazdani, Federica Fiorucci
Summary: Timely and systematic collection of landslide information is crucial in understanding landslide trends in response to climate change. A severe rainfall event in central Italy triggered 1687 landslides, causing widespread flash floods and affecting the road network. The collected data can be used for comparison with past landslides, validation of landslide susceptibility models, and understanding the interaction between landslides and structures/infrastructures.
Article
Meteorology & Atmospheric Sciences
Seshagiri Rao Kolusu, Marion Mittermaier, Joanne Robbins, Raghavendra Ashrit, Ashis K. Mitra
Summary: This study evaluates the skill of the fully coupled lagged ensemble forecasts from GloSea5-GC2 for the sub-seasonal to seasonal (S2S) timescale up to 4 weeks. The results show that model biases increase with increasing lead time accumulation windows and ensemble member age. The S2S model exhibits wet and dry biases across different parts of the Indian domain. The study concludes that the older lagged members do not necessarily add value to the forecasts.
METEOROLOGICAL APPLICATIONS
(2023)
Article
Geosciences, Multidisciplinary
Evelina Volpe, Stefano Luigi Gariano, Luca Ciabatta, Yaser Peiro, Elisabetta Cattoni
Summary: Cultural heritage plays an important role in Italy, with archaeological sites contributing significantly to national and international cultural heritage. However, these sites face challenges such as natural deterioration, human impact, and climate-related hazards. This study focuses on assessing landslide hazards at the archaeological site of Pietrabbondante in Italy. Using a physically based model, the impact of expected rainfall on slope stability was evaluated, considering the random uncertainty of soil parameters. The results provide reference for the safety assessment and conservation of archaeological areas with high cultural value.
Article
Meteorology & Atmospheric Sciences
Seshagiri Rao Kolusu, Marion Mittermaier, Joanne Robbins, Raghavendra Ashrit, Ashis K. Mitra
Summary: This study assesses the skill of the fully coupled lagged ensemble forecasts from GloSea5-GC2 for the sub-seasonal to seasonal timescale. The results show that model biases increase with lead time accumulation windows and ensemble member age. The S2S model exhibits wet and dry biases across different parts of the Indian domain, and the model error grows as the lead time increases. The actual skill and potential skill of the ensemble forecasts reveal that the potential skill is not always greater than the actual skill.
METEOROLOGICAL APPLICATIONS
(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)
Article
Geosciences, Multidisciplinary
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, Massimiliano Pittore
Summary: The increasing availability of long-term observational data has allowed for the development of innovative modelling approaches to determine landslide triggering conditions at a regional scale, leading to new avenues for landslide prediction and early warning.
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
(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
Meteorology & Atmospheric Sciences
Robert Neal, Galina Guentchev, T. Arulalan, Joanne Robbins, Ric Crocker, Ashis Mitra, A. Jayakumar
Summary: A method for probabilistic medium-range weather pattern forecasts in India is presented, using a set of 30 daily weather patterns. The forecasts show skill up to 10-15 days and can be used to highlight weather pattern transitions and likelihood of weather impacts. The study also presents an application for highlighting periods of extreme rainfall based on weather pattern probabilities. The research demonstrates the importance and benefits of a weather pattern forecasting approach over basic daily climatology.
METEOROLOGICAL APPLICATIONS
(2022)
Article
Environmental Studies
Evelina Volpe, Stefano Luigi Gariano, Francesca Ardizzone, Federica Fiorucci, Diana Salciarini
Summary: Land use is a significant factor in rainfall-induced shallow landslides. Improper agricultural practices can negatively impact slope stability. However, there is a lack of research on the effects of soil tillage on slope stability.
