scholarly journals Landslide forecasting and factors influencing predictability

2016 ◽  
Vol 16 (12) ◽  
pp. 2501-2510 ◽  
Author(s):  
Emanuele Intrieri ◽  
Giovanni Gigli
Keyword(s):  
Slope Failure ◽  
High Reliability ◽  
Data Representation ◽  
Landslide Risk ◽  
Natural Event ◽  
Predictability Index ◽  
Factors Influencing ◽  
External Agents ◽  
Landslide Forecasting ◽  
Traditional Approaches

Abstract. Forecasting a catastrophic collapse is a key element in landslide risk reduction, but it is also a very difficult task owing to the scientific difficulties in predicting a complex natural event and also to the severe social repercussions caused by a false or missed alarm. A prediction is always affected by a certain error; however, when this error can imply evacuations or other severe consequences a high reliability in the forecast is, at least, desirable. In order to increase the confidence of predictions, a new methodology is presented here. In contrast to traditional approaches, this methodology iteratively applies several forecasting methods based on displacement data and, thanks to an innovative data representation, gives a valuation of the reliability of the prediction. This approach has been employed to back-analyse 15 landslide collapses. By introducing a predictability index, this study also contributes to the understanding of how geology and other factors influence the possibility of forecasting a slope failure. The results showed how kinematics, and all the factors influencing it, such as geomechanics, rainfall and other external agents, are key concerning landslide predictability.

scholarly journals Operative and reliable landslide forecasting and influence of geology to predictability

2016 ◽  
Author(s):  
Emanuele Intrieri ◽  
Giovanni Gigli
Keyword(s):  
Slope Failure ◽  
High Reliability ◽  
Data Representation ◽  
Landslide Risk ◽  
Natural Event ◽  
Predictability Index ◽  
Forecasting Methods ◽  
Factors Influencing ◽  
Landslide Forecasting ◽  
Traditional Approaches

Abstract. Forecasting a catastrophic collapse is a key element in landslide risk reduction, but also a very difficult task, owing to the scientific difficulties in predicting a complex natural event and also to the severe social repercussions caused by a false or a missed alarm. A prediction is always affected by a certain error, however when this error can imply evacuations or other severe consequences a high reliability in the forecast is, at least, desirable. In order to increase the confidence of predictions, a new methodology is here presented. Differently from traditional approaches, it iteratively applies several forecasting methods based on displacement data and, also thanks to an innovative data representation, gives a valuation of how the prediction is reliable. This approach has been employed to back-analyse 15 landslide collapses. By introducing a predictability index, this study also contributes to the understanding of how geology and other factors influence the possibility to forecast a slope failure. The results showed that, contrarily to what is generally believed, geomechanics plays an indirect role in landslide predictability; instead kinematics, and all the factors influencing it, is the key feature.

scholarly journals The Norwegian forecasting and warning service for rainfall- and snowmelt-induced landslides

2018 ◽  
Vol 18 (5) ◽  
pp. 1427-1450 ◽  
Author(s):  
Ingeborg K. Krøgli ◽  
Graziella Devoli ◽  
Hervé Colleuille ◽  
Søren Boje ◽  
Monica Sund ◽  
...  
Keyword(s):  
Flood Forecasting ◽  
Landslide Risk ◽  
Public Agencies ◽  
Positive Feedbacks ◽  
Public Road ◽  
Landslide Forecasting ◽  
Close Cooperation ◽  
Forecasting And Warning ◽  
Main Components ◽  
Recent Evaluation

Abstract. The Norwegian Water Resources and Energy Directorate (NVE) have run a national flood forecasting and warning service since 1989. In 2009, the directorate was given the responsibility of also initiating a national forecasting service for rainfall-induced landslides. Both services are part of a political effort to improve flood and landslide risk prevention. The Landslide Forecasting and Warning Service was officially launched in 2013 and is developed as a joint initiative across public agencies between NVE, the Norwegian Meteorological Institute (MET), the Norwegian Public Road Administration (NPRA) and the Norwegian Rail Administration (Bane NOR). The main goal of the service is to reduce economic and human losses caused by landslides. The service performs daily a national landslide hazard assessment describing the expected awareness level at a regional level (i.e. for a county and/or group of municipalities). The service is operative 7 days a week throughout the year. Assessments and updates are published at the warning portal http://www.varsom.no/ at least twice a day, for the three coming days. The service delivers continuous updates on the current situation and future development to national and regional stakeholders and to the general public. The service is run in close cooperation with the flood forecasting service. Both services are based on the five pillars: automatic hydrological and meteorological stations, landslide and flood historical database, hydro-meteorological forecasting models, thresholds or return periods, and a trained group of forecasters. The main components of the service are herein described. A recent evaluation, conducted on the 4 years of operation, shows a rate of over 95 % correct daily assessments. In addition positive feedbacks have been received from users through a questionnaire. The capability of the service to forecast landslides by following the hydro-meteorological conditions is illustrated by an example from autumn 2017. The case shows how the landslide service has developed into a well-functioning system providing useful information, effectively and on time.

scholarly journals Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

2017 ◽  
Vol 17 (2) ◽  
pp. 225-241 ◽  
Author(s):  
Susana Almeida ◽  
Elizabeth Ann Holcombe ◽  
Francesca Pianosi ◽  
Thorsten Wagener
Keyword(s):  
Slope Stability ◽  
Slope Failure ◽  
Numerical Models ◽  
Landslide Risk ◽  
Residual Soils ◽  
Loss Of Life ◽  
Economic Changes ◽  
Critical Thresholds ◽  
Stability Model ◽  
Steep Slopes

Abstract. Landslides have large negative economic and societal impacts, including loss of life and damage to infrastructure. Slope stability assessment is a vital tool for landslide risk management, but high levels of uncertainty often challenge its usefulness. Uncertainties are associated with the numerical model used to assess slope stability and its parameters, with the data characterizing the geometric, geotechnic and hydrologic properties of the slope, and with hazard triggers (e.g. rainfall). Uncertainties associated with many of these factors are also likely to be exacerbated further by future climatic and socio-economic changes, such as increased urbanization and resultant land use change. In this study, we illustrate how numerical models can be used to explore the uncertain factors that influence potential future landslide hazard using a bottom-up strategy. Specifically, we link the Combined Hydrology And Stability Model (CHASM) with sensitivity analysis and Classification And Regression Trees (CART) to identify critical thresholds in slope properties and climatic (rainfall) drivers that lead to slope failure. We apply our approach to a slope in the Caribbean, an area that is naturally susceptible to landslides due to a combination of high rainfall rates, steep slopes, and highly weathered residual soils. For this particular slope, we find that uncertainties regarding some slope properties (namely thickness and effective cohesion of topsoil) are as important as the uncertainties related to future rainfall conditions. Furthermore, we show that 89 % of the expected behaviour of the studied slope can be characterized based on only two variables – the ratio of topsoil thickness to cohesion and the ratio of rainfall intensity to duration.

scholarly journals Climate variability, disasters and their impacts assessment in Manahari, rural municipality of Makwanpur, Nepal

2019 ◽  
Vol 4 (6) ◽  
pp. 201-210
Author(s):  
Ram Asheshwar Mandal ◽  
Bindu Subedi ◽  
Dhruba Lochan Adhikari ◽  
Ajay Bhakta Mathema
Keyword(s):  
Slope Failure ◽  
Secondary Data ◽  
Flood Frequency ◽  
Primary Data ◽  
Landslide Risk ◽  
Mean Annual Temperature ◽  
Precipitation Trend ◽  
Rural Municipality ◽  
Temperature And Precipitation ◽  
The Mean

Nepal is climatically very sensitive country because of long drought, heavy floods, landslides and soil erosion caused by changing pattern of rainfall and temperature. However, there are very limited studies related to these issues, thus this research was objectively carried out to analyze temperature and precipitation trend of study area, examine the climate pattern and assess the impacts of climate change hazards on different sectors. Ward number 7 and 8 Manahari Rural Municipality of Makwanpur district was selected as the study site. Total 40 households survey, 15 Key informants interview and two focus group discussions were conducted involving the affected local to collect the primary data. Moreover, secondary data specifically monthly maximum and minimum temperature and rainfall for thirty one years between 1985–2015 were gathered from nearest meteorological station i.e. NFI Hetauda Station (Station No. 906) and Manahari Station (Station No. 920). The drought trend was calculated using the ratio of Precipitation<2Temperatures. The theoretical distribution i.e. Gumbel, Log-Pearson and Log Normal models were applied to predict the flood peaks and maximum rainfalls. The mean annual temperature was increasing at the rate of 0.0226°C per year. The highest mean annual temperature was 24.1°C in 2015. It was found that, the number of days exceeding the maximum average temperature in the period of 31 years. However, the trend of total annual precipitation in Hetauda was decreasing at the rate of 5.6607 mm per year. The highest rainfall was recorded about 3323.1 mm in year 2002 and it was the least only 1626.2 mm in 2012. The January, February, March, November and December were the driest months. Flood frequency using Log Pearson showed the highest flood in 1000 years return period. The mean rank was the highest of drought having value 5 while it was the lowest only 1.4 of flood. The slope failure at the edges of the rural roads also causes landslides which also fills the agriculture land. The locals responded that the drainage systems were poor and there were no protection structure and/or biological component to reduce landslide risk during construction periods. Major five disasters were recorded in Manahari during from March to June whereas, wildlife attack throughout the year and so on.

Cohesive slope failure analysis using methods combining smoothed particle hydrodynamics and response surface function

2019 ◽  
Vol 37 (3) ◽  
pp. 1093-1108
Author(s):  
Liang Li ◽  
Xuesong Chu ◽  
Guangming Yu
Keyword(s):  
Failure Analysis ◽  
Smoothed Particle Hydrodynamics ◽  
Traditional Method ◽  
Slope Failure ◽  
Slip Surface ◽  
Landslide Risk ◽  
Content Type ◽  
Particle Hydrodynamics ◽  
Smoothed Particle ◽  
Response Surface Function

Purpose The paper aims to construct a method to simulate the relationship between the parameters of soil properties and the area of sliding mass of the true slip surface of a landslide. Design/methodology/approach The smoothed particle hydrodynamics (SPH) algorithm is used to calibrate a response surface function which is adopted to quantify the area of sliding mass of the true slip surface for each failure sample in Monte Carlo simulation. The proposed method is illustrated through a homogeneous and a heterogeneous cohesive soil slope. Findings The comparison of the results between the proposed method and the traditional method using the slip surface with minimum factor of safety (FSmin) to quantify the failure consequence has shown that the landslide risk tends to be attributed to a variety of risk sources, and that the use of a slip surface with FSmin to quantify the consequence of a landslide underestimates the landslide risk value. The difference of the risk value between the proposed method and the traditional method increases dramatically as the uncertainty of soil properties becomes significant. Practical implications A geotechnical engineer could use the proposed method to perform slope failure analysis. Originality/value The failure consequence of a landslide can be rationally predicted using the proposed method.

scholarly journals Landslide Forecast by Time Series Modeling and Analysis of High-Dimensional and Non-Stationary Ground Motion Data

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 850-867
Author(s):  
Guoqi Qian ◽  
Antoinette Tordesillas ◽  
Hangfei Zheng
Keyword(s):  
Time Series ◽  
Ground Motion ◽  
Slope Failure ◽  
Time Series Data ◽  
Warning System ◽  
Threshold Level ◽  
Series Data ◽  
High Dimensional ◽  
Landslide Risk ◽  
Landslide Data

High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advanced econometric methods, i.e., error-correction cointegration (ECC) and vector autoregression (VAR), and a newly developed dimension reduction technique named empirical dynamic quantiles (EDQ). Our ECC–VAR–EDQ method was born by analyzing a big landslide dataset, comprising interferometric synthetic-aperture radar (InSAR) measurements of ground displacement that were observed at 5090 time states and 1803 locations on a slope. The aim was to develop an early warning system for reliably forecasting any impending slope failure whenever a precursory slope deformation is on the horizon. Specifically, we first reduced the spatial dimension of the observed landslide data by representing them as a small set of EDQ series with negligible loss of information. We then used the ECC–VAR model to optimally fit these EDQ series, from which forecasts of future ground motion can be efficiently computed. Moreover, our method is able to assess the future landslide risk by computing the relevant probability of ground motion to exceed a red-alert threshold level at each future time state and location. Applying the ECC–VAR–EDQ method to the motivating landslide data gives a prediction of the incoming slope failure more than 8 days in advance.

scholarly journals DECIMER - Towards Deep Learning for Chemical Image Recognition

2020 ◽  
Author(s):  
Kohulan Rajan ◽  
Achim Zielesny ◽  
Christoph Steinbeck
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Open Data ◽  
Data Representation ◽  
Training Data ◽  
Training Time ◽  
Training Structures ◽  
Training Success ◽  
Traditional Approaches ◽  
Chemical Image

The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-access databases. Here we report preliminary findings in our development of DECIMER (Deep lEarning for Chemical ImagE Recognition), a deep learning method based on existing show-and-tell deep neural networks which makes very few assumptions about the structure of the underlying problem. The training state reported here does not yet rival the performance of existing traditional approaches, but we present evidence that our method will reach a comparable detection power with sufficient training time. Training success of DECIMER depends on the input data representation: DeepSMILES are clearly superior over SMILES and we have preliminary indication that the recently reported SELFIES outperform DeepSMILES. An extrapolation of our results towards larger training data sizes suggest that we might be able to achieve >90% accuracy with about 60 to 100 million training structures, so that training can be completed within several months on a single GPU. This work is completely based on open-source software and open data and is available to the general public for any purpose.

scholarly journals The Norwegian forecasting and warning service for rainfall- and snowmelt-induced landslides

2017 ◽  
Author(s):  
Ingeborg K. Krøgli ◽  
Graziella Devoli ◽  
Hervé Colleuille ◽  
Monica Sund ◽  
Søren Boje ◽  
...  
Keyword(s):  
Flood Forecasting ◽  
Landslide Risk ◽  
Public Agencies ◽  
Positive Feedbacks ◽  
Public Road ◽  
Landslide Forecasting ◽  
Close Cooperation ◽  
Forecasting And Warning ◽  
Main Components ◽  
Recent Evaluation

Abstract. The Norwegian Water Resources and Energy Directorate (NVE) has run a national flood forecasting and warning service since 1989. Back in 2009, the directorate was given the responsibility of initiating also a national forecasting service for rainfall-induced landslides. Both services are part of a political effort to improve flood and landslide risk prevention. The Landslide Forecasting and Warning Service was officially launched in 2013 and is developed as a joint initiative across public agencies between NVE, the Norwegian Meteorological Institute (MET), the Norwegian Public Road Administration (NPRA) and the Norwegian Rail Administration (Bane NOR). The main goal of the service is to reduce economic and human losses caused by landslides. The service performs a national landslide hazard assessment every day describing the expected awareness level at a regional level (i.e. for a county and/or group of municipalities). The service is operative seven days a week throughout the year. Assessments and updates are published at the warning portal http://www.varsom.no at least twice a day, for the three coming days. The service delivers continuous updates on the current situation and future development to national and regional stakeholders and to the general public. The service is running in close cooperation with the flood forecasting service. Both services are based on the five pillars: automatic hydrological and meteorological stations, landslide and flood historical database, hydro-meteorological forecasting models, thresholds or return periods, and a trained group of forecasters. The main components of the service are herein described. A recent evaluation, conducted on the four years of operation, shows a rate of over 95 % correct daily assessments. In addition positive feedbacks have been received from users through a questionnaire. The capability of the service to forecast landslides by following the hydro-meteorological conditions is illustrated by an example from autumn 2017. The case shows how the landslide service has developed into a well-functioning system providing useful information, effectively, on-time.

scholarly journals DECIMER: towards deep learning for chemical image recognition

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Kohulan Rajan ◽  
Achim Zielesny ◽  
Christoph Steinbeck
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Open Data ◽  
Data Representation ◽  
Training Data ◽  
Training Time ◽  
Training Structures ◽  
Training Success ◽  
Traditional Approaches ◽  
Chemical Image

Abstract The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-access databases. Here we report preliminary findings in our development of Deep lEarning for Chemical ImagE Recognition (DECIMER), a deep learning method based on existing show-and-tell deep neural networks, which makes very few assumptions about the structure of the underlying problem. It translates a bitmap image of a molecule, as found in publications, into a SMILES. The training state reported here does not yet rival the performance of existing traditional approaches, but we present evidence that our method will reach a comparable detection power with sufficient training time. Training success of DECIMER depends on the input data representation: DeepSMILES are superior over SMILES and we have a preliminary indication that the recently reported SELFIES outperform DeepSMILES. An extrapolation of our results towards larger training data sizes suggests that we might be able to achieve near-accurate prediction with 50 to 100 million training structures. This work is entirely based on open-source software and open data and is available to the general public for any purpose.

Statistical Analysis of Screening Studies in Toxicology with Special Emphasis on Neurotoxicology

1989 ◽  
Vol 8 (1) ◽  
pp. 171-183 ◽  
Author(s):  
Shayne C. Gad
Keyword(s):  
Statistical Analysis ◽  
Control Charts ◽  
Additional Data ◽  
Central Tendency ◽  
Operating Characteristics ◽  
Traditional Methods ◽  
Factors Influencing ◽  
Alternative Approaches ◽  
Principles Of Design ◽  
Traditional Approaches

Screens have a common set of operating characteristics that are not widely appreciated and that make traditional approaches to statistical analysis both insensitive and inefficient in comparison to other available methods. Traditional methods also do not incorporate additional data as it is generated. Such incorporation would serve to strengthen both the design and analysis processes and is essential in the case of screens. Traditional methods of analysis (contingency tables, rank sum, and ANOVA methods) are overviewed briefly, and their weaknesses are discussed. The concept of power and the factors influencing it are discussed. Alternative approaches to analysis of univariate (control charts and central tendency plots) and multivariate (analog contrast plots and multidimensional cluster plots) data from screens are presented, and their performance is evaluated. The resulting general principles of design and analysis of screens for neurotoxicology are presented. The alternative approaches are shown to be superior to traditional approaches in performance toward meeting the objectives of screens.

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