Rainfall Thresholding and Susceptibility assessment of rainfall induced landslides: application to landslide management in St Thomas, Jamaica

The final publication is available at Springer via http://dx.doi.org/10.1007/s10064-009-0232-zThe parish of St Thomas has one of the highest densities of landslides in Jamaica, which impacts the residents, local economy and the built and natural environment. These landslides result from a combination of steep slopes, faulting, heavy rainfall and the presence of highly weathered volcanics, sandstones, limestones and sandstone/shale series and are particularly prevalent during the hurricane season (June–November). The paper reports a study of the rainfall thresholds and landslide susceptibility assessment to assist the prediction, mitigation and management of slope instability in landslide-prone areas of the parish

Seismically induced landslide hazard and exposure modelling in Southern California based on the 1994 Northridge, California earthquake event

Quantitative modelling of landslide hazard, as opposed to landslide susceptibility, as a function of the earthquake trigger is vital in understanding and assessing future potential exposure to landsliding. Logistic regression analysis is a method commonly used to assess susceptibility to landsliding; however, estimating probability of landslide hazard as a result of an earthquake trigger is rarely undertaken. This paper utilises a very detailed landslide inventory map and a comprehensive dataset on peak ground acceleration for the 1994 Mw6.7 Northridge earthquake event to fit a landslide hazard logistic regression model. The model demonstrates a high success rate for estimating probability of landslides as a result of earthquake shaking. Seven earthquake magnitude scenarios were simulated using the Open Source Seismic Hazard Analysis (OpenSHA) application to simulate peak ground acceleration, a covariate of landsliding, for each event. The exposure of assets such as population, housing and roads to high levels of shaking and high probabilities of landsliding was estimated for each scenario. There has been urban development in the Northridge region since 1994, leading to an increase in prospective exposure of assets to the earthquake and landslide hazards in the event of a potential future earthquake. As the earthquake scenario magnitude increases, the impact from earthquake shaking initially increases then quickly levels out, but potential losses from landslides increase at a rapid rate. The modelling approach, as well as the specific model, developed in this paper can be used to estimate landslide probabilities as a result of an earthquake event for any scenario where the peak ground acceleration variable is available

Geostatistical Models for the Prediction of Water Supply Network Failures in Bogotá, Integrating Machine Learning Algorithms

[EN] Currently new strategies of spatial referencing, data analysis, and machine learning methods are integrated with Geographical Information Systems (GISs) to understand specific characteristics and water supply dynamics. This work explores the variables that can cause spacial failures and potential risk areas with application to a zone in the Bogotá water supply network. Machine learning algorithms are proposed to generate prediction models and potential failure maps. A sensitivity analysis was held to identify the model with the best fit for the estimation. This study will allow water supply decisions makers to focalize their efforts in the field.[ES] Actualmente se buscan nuevas estrategias y/o metodologías basadas en la integración de los Sistemas de Información Geográfica (SIGs) como forma de georeferenciacion espacial y visualización de las variables analizadas, junto con métodos de aprendizaje automático (Machine Learning) que permitan entender características puntuales, variables influyentes y dinámicas de los sistemas de abastecimiento de agua potable.En este trabajo se hace la identificación espacial de los fallos y zonas potenciales de riesgo que se presentan en una zona de la red de abastecimiento de Bogotá, explorando las variables que puedan tener mayor incidencia en los mismos. Se propone el uso de algoritmos de aprendizaje automático para la generación de modelos de predicción y la elaboración de mapas de fallos potenciales, identificando, a través de un análisis de sensibilidad, cuál de estos modelos presenta un mejor ajuste en la estimación. Este estudio permite a los gestores del abastecimiento una localización precisa y eficiente de los fallos en la red, apoyando el proceso de toma de decisiones.Navarrete-López, CF.; Calderón-Rivera, D.; Díaz Arévalo, JL.; Herrera Fernández, AM.; Izquierdo Sebastián, J. (2018). Modelos geoestadísticos para la predicción de fallos de una zona de la red de abastecimiento de agua de Bogotá, integrando algoritmos de Machine Learning. Social Science Research Network. 1-8. https://doi.org/10.2139/ssrn.3113048S1

Landslide susceptibility mapping using support vector machine and GIS at the Golestan province, Iran

The main goal of this study is to produce landslide susceptibility map using GIS-based support vector machine (SVM) at Kalaleh Township area of the Golestan province, Iran. In this paper, six different types of kernel classifiers such as linear, polynomial degree of 2, polynomial degree of 3, polynomial degree of 4, radial basis function (RBF) and sigmoid were used for landslide susceptibility mapping. At the first stage of the study, landslide locations were identified by aerial photographs and field surveys, and a total of 82 landslide locations were extracted from various sources. Of this, 75% of the landslides (61 landslide locations) are used as training dataset and the rest was used as (21 landslide locations) the validation dataset. Fourteen input data layers were employed as landslide conditioning factors in the landslide susceptibility modelling. These factors are slope degree, slope aspect, altitude, plan curvature, profile curvature, tangential curvature, surface area ratio (SAR), lithology, land use, distance from faults, distance from rivers, distance from roads, topographic wetness index (TWI) and stream power index (SPI). Using these conditioning factors, landslide susceptibility indices were calculated using support vector machine by employing six types of kernel function classifiers. Subsequently, the results were plotted in ArcGIS and six landslide susceptibility maps were produced. Then, using the success rate and the prediction rate methods, the validation process was performed by comparing the existing landslide data with the six landslide susceptibility maps. The validation results showed that success rates for six types of kernel models varied from 79% to 87%. Similarly, results of prediction rates showed that RBF (85%) and polynomial degree of 3 (83%) models performed slightly better than other types of kernel (polynomial degree of 2 = 78%, sigmoid = 78%, polynomial degree of 4 = 78%, and linear = 77%) models. Based on our results, the differences in the rates (success and prediction) of the six models are not really significant. So, the produced susceptibility maps will be useful for general land-use planning

Application of Neural Networks for Landslide Susceptibility Mapping in Turkey

 Landslides are a major natural hazard in many areas of the world, and globally  they cause hundreds of billions of dollars of damage, and hundreds of  thousands of deaths and injuries each year. Landslides are the second most  common natural hazard in Turkey, and the Black Sea region of that country is  particularly affected. Therefore, landslide susceptibility mapping is one of the  important issues for urban and rural planning in Turkey. The reliability of  these maps depends mostly on the amount and quality of available data used,  as well as the selection of a robust methodology. Although statistical methods  generally have been implemented and used for evaluating landslide  susceptibility and risk in medium scale studies, they are distribution-based and  cannot handle multi-source data that are commonly collected from nature.  These drawbacks are responsible for the on-going investigations into slope  instability. To overcome these weaknesses, the desired technique must be able  to handle multi-type data and its superiority should increase as the  dimensionality and/or non-linearity of the problem increases - which is when  traditional regression often fails to produce accurate approximations. Although  neural networks have some problems with the creation of architectures,  processing time, and the negative “black boxi syndrome, they still have an  advantage over traditional methods in that they can deal with the problem  comprehensively and are insensitive to uncertain data and measurement errors.  Therefore, it is expected that the application of neural networks will bring new  perspectives to the assessment of landslide susceptibility in Turkey. In this  paper, the application of neural networks for landslide susceptibility mapping  will be examined and their performance as a component of spatial decision  support systems will be discussed

Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)

Landslide susceptibility mapping is one of the most critical issues in Turkey. At present, geotechnical models appear to be useful only in areas of limited extent, because it is difficult to collect geotechnical data with appropriate resolution over larger regions. In addition, many of the physical variables that are necessary for running these models are not usually available, and their acquisition is often very costly. Conversely, statistical approaches are currently pursued to assess landslide hazard over large regions. However, these approaches cannot effectively model complicated landslide hazard problems, since there is a non-linear relationship between nature-based problems and their triggering factors. Most of the statistical methods are distribution-based and cannot handle multisource data that are commonly collected from nature. In this respect, logistic regression and neural networks provide the potential to overcome drawbacks and to satisfy more rigorous landslide susceptibility mapping requirements. In the Hendek region of Turkey, a segment of natural gas pipeline was damaged due to landslide. Re-routing of the pipeline is planned but it requires preparation of landslide susceptibility map. For this purpose, logistic regression analysis and neural networks are applied to prepare landslide susceptibility map of the problematic segment of the pipeline. At the end, comparative analysis is conducted on the strengths and weaknesses of both techniques. Based on the higher percentages of landslide bodies predicted in very high and high landslide susceptibility zones, and compatibility between field observations and the important factors obtained in the analyses, the result found by neural network is more realistic

Comments on “Landslide susceptibility zonation study using remote sensing and GIS technology in the Ken–Betwa River Link area, India” by R. Avtar, C. K. Singh, G. Singh, R. L. Verma, S. Mukherjee, H. Sawada in Bulletin of Engineering Geology and the Environment (doi:10.1007/s10064-011-0368-5)

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