scholarly journals Analysis and Risk Study on Landslide Hazard Frequency at Road Corridor of Batu City – Kediri Regency Border

2018 ◽  
Vol 4 (3) ◽  
pp. 265
Author(s):  
Emil Wahyudianto
Keyword(s):  
Flash Flood ◽  
Mass Movement ◽  
Method Comparison ◽  
Daily Rainfall ◽  
Landslide Hazard ◽  
Landslide Risk ◽  
Landslide Occurrence ◽  
Cumulative Rainfall ◽  
Moving Vehicles ◽  
Road Corridors

Road corridor of Kota Batu – Kediri Regency Boundary is a provincial road that has a vital function for the economic and tourism movement from and to Batu City in East Java Province. This inter-regency road is historically vulnerable to disaster events such as landslide, Kali Konto flash flood, Kelud Mountain lahar, flood inundation, etc. This research was referred to Regulation of Ministry of Public Work No.22/PRT/M/2007 on Guidelines for Spatial Planning of Landslide Vulnerable Areas and helped with Geographic Information System (GIS). Method comparison was also conducted by Meiliana (2011) with the indicators from the same regulation, and by using Landslide Hazard Assessment (LHA) method that is based on historical data. The landslide risk mapping with LHA method that is combined with analysis result from the vulnerability of moving vehicles is suggested to be the reference in mapping the mass-movement disaster risk on Indonesian road corridors. Analysis on frequency of rainfall that triggered landslide concluded that the probability of landslide occurrence (PLO) on daily rainfall was 126.2 mm, or 3 days-cumulative rainfall of 192.26 mm.

scholarly journals Analysis of human exposure to landslides with a GIS multiscale approach

2022 ◽  
Author(s):  
S. Modugno ◽  
S. C. M. Johnson ◽  
P. Borrelli ◽  
E. Alam ◽  
N. Bezak ◽  
...  
Keyword(s):  
Human Exposure ◽  
Local Level ◽  
Explanatory Power ◽  
National Level ◽  
Landslide Hazard ◽  
Landslide Risk ◽  
Multiscale Approach ◽  
Landslide Occurrence ◽  
Multi Scale ◽  
Overview Analysis

AbstractDecision-making plays a key role in reducing landslide risk and preventing natural disasters. Land management, recovery of degraded lands, urban planning, and environmental protection in general are fundamental for mitigating landslide hazard and risk. Here, we present a GIS-based multi-scale approach to highlight where and when a country is affected by a high probability of landslide occurrence. In the first step, a landslide human exposure equation is developed considering the landslide susceptibility triggered by rain as hazard, and the population density as exposed factor. The output, from this overview analysis, is a global GIS layer expressing the number of potentially affected people by month, where the monthly rain is used to weight the landslide hazard. As following step, Logistic Regression (LR) analysis was implemented at a national and local level. The Receiver Operating Characteristic indicator is used to understand the goodness of a LR model. The LR models are defined by a dependent variable, presence–absence of landslide points, versus a set of independent environmental variables. The results demonstrate the relevance of a multi-scale approach, at national level the biophysical variables are able to detect landslide hotspot areas, while at sub-regional level geomorphological aspects, like land cover, topographic wetness, and local climatic condition have greater explanatory power.

Engineering geological investigation for landslide hazard zonation in the Sino-Nepal Road corridors.

2020 ◽  
Author(s):  
Basanta Raj Adhikari ◽  
Bingwei Tian ◽  
Feiyu Chen ◽  
Xiaoyun Gou ◽  
Suraj Gautam ◽  
...  
Keyword(s):  
Large Scale ◽  
Mass Movement ◽  
Road Construction ◽  
Landslide Hazard ◽  
Mitigation Measures ◽  
Main Central Thrust ◽  
Geological Investigation ◽  
The Road ◽  
Road Corridors ◽  
The Himalaya

<p>Road construction in the Trans-Himalaya is always challenging task because of having fragile and rugged topography with the strong influence of monsoon. Three different road corridors namely Kaligandaki (Pokhara-Jomsoom-Zhongba), Trishuali (Kathamndu-Trishuli-Gyirong) and Bhotekoshi rivers (Kathmandu-Tatopani-Nyalam) cross the Himalaya with different geological discontinuities i.e. South Tibetan Detachment System (STDS), Main Central Thrust (MCT). The Himalayan range is acting a topographic barrier resulting different climate in the southern and northern part. These three roads are very strategic for the connectivity between Trans-Himalaya and midland. People have been living in these valleys for a long time. After the road construction, people have started to build houses along this road. However, people have are often forgetting the influence of these large scale mass movement that occurred in the past. Therefore, an attempt has been done to analyze these past events and their impacts. Preparation of engineering geological map, landslide inventories and investigation of large scale past mass movement have been done in detailed field investigations in 2018 and 2019 supported by remote sensing. Slope stability analysis has been done in different critical sections for the landslide hazard assessment. It is clearly seen that the road passes some of these large scale paleo-landslides and responsible for toe cutting. The road sections are critical in all three roads but more vulnerable in the southern slope of the Himalaya. The road between Beni to Larjung of the Kaligandaki has critical slope and susceptible for landslide occurrences. Therefore, proper mitigation measures have to be implemented for the stabilization of these mountain slope.</p>

scholarly journals Landslide size matters: a new spatial predictive paradigm

2021 ◽  
Author(s):  
Luigi Lombardo ◽  
Hakan Tanyas ◽  
Raphaël Huser ◽  
Fausto Guzzetti ◽  
Daniela Castro Camilo
Keyword(s):  
Gaussian Model ◽  
Additive Model ◽  
Landslide Hazard ◽  
Spatial Uncertainty ◽  
Landslide Risk ◽  
Catchment Scale ◽  
Landslide Occurrence ◽  
Slope Unit ◽  
Landslide Event ◽  
Physically Based

<p>The standard definition of landslide hazard requires the estimation of where, when (or how frequently) and how large a given landslide event may be. The geomorphological community involved in statistical models has addressed the component pertaining to how large a landslide event may be by introducing the concept of landslide-event magnitude scale. This scale, which depends on the planimetric area of the given population of landslides, in analogy to the earthquake magnitude, has been expressed with a single value per landslide event. As a result, the geographic or spatially-distributed estimation of how large a population of landslide may be when considered at the slope scale, has been disregarded in statistically-based landslide hazard studies. Conversely, the estimation of the landslide extent has been commonly part of physically-based applications, though their implementation is often limited to very small regions.</p><p> </p><p>In this work, we initially present a review of methods developed for landslide hazard assessment since its first conception decades ago. Subsequently, we introduce for the first time a statistically-based model able to estimate the planimetric area of landslides aggregated per slope units. More specifically, we implemented a Bayesian version of a Generalized Additive Model where the maximum landslide sizes per slope unit and the sum of all landslide sizes per slope unit are predicted via a Log-Gaussian model. These ''max'' and ''sum'' models capture the spatial distribution of landslide sizes. We tested these models on a global dataset expressing the distribution of co-seismic landslides due to 24 earthquakes across the globe. The two models we present are both evaluated on a suite of performance diagnostics that suggest our models suitably predict the aggregated landslide extent per slope unit. In addition to a complex procedure involving variable selection and a spatial uncertainty estimation, we built our model over slopes where landslides triggered in response to seismic shaking, and simulated the expected failing surface over slopes where the landslides did not occur in the past.  </p><p> </p><p>What we achieved is the first statistically-based model in the literature able to provide information about the extent of the failed surface across a given landscape. This information is vital in landslide hazard studies and should be combined with the estimation of landslide occurrence locations. This could ensure that governmental and territorial agencies have a complete probabilistic overview of how a population of landslides could behave in response to a specific trigger.</p><p>The predictive models we present are currently valid only for the 24 cases we tested. Statistically estimating landslide extents is still at its infancy stage. Many more applications should be successfully validated before considering such models in an operational way. For instance, the validity of our models should still be verified at the regional or catchment scale, as much as it needs to be tested for different landslide types and triggers. However, we envision that this new spatial predictive paradigm could be a breakthrough in the literature and, in time, could even become part of official landslide risk assessment protocols.</p>

Landslide hazard assessment of the 2008 Wenchuan earthquake: a case study in Beichuan area

2011 ◽  
Vol 48 (1) ◽  
pp. 128-145 ◽  
Author(s):  
Chuan Tang ◽  
Jing Zhu ◽  
Xin Qi
Keyword(s):  
Wenchuan Earthquake ◽  
Large Scale ◽  
Landslide Hazard ◽  
Landslide Risk ◽  
Reconstruction Process ◽  
2008 Wenchuan Earthquake ◽  
Landslide Occurrence ◽  
The Wenchuan Earthquake ◽  
Very High ◽  
Earthquake Area

The Wenchuan earthquake (magnitude Ms = 8.0) of 12 May 2008 triggered widespread and large-scale landslides over an area of about 50 000 km2. A study was undertaken to determine the primary factors associated with seismic landslide occurrence. An index-based approach used to assess earthquake-triggered landslide hazard in the central part of the Wenchuan earthquake area affected is described. Slope gradient, relief amplitude, lithology, bedding–slope relations, fault proximity, stream proximity, and antecedent rainfall are recognized as factors that may have had an important influence on landslide occurrence. The assessment of the influence of each of these factors is presented through use of a series of maps showing areas of low, moderate, high, and very high landslide hazard. Areas identified as having “very high and high landslide hazard” were located along the earthquake-source fault and along both banks of the Jian River. The role of rainfall is very significant for future landslide occurrence in the earthquake area. The results of this study will assist decision makers in the selection of safe sites during the reconstruction process. The maps can also be used for landslide risk management in the study area.

scholarly journals Influence of Cumulative Rainfall on the Occurrence of Landslides in Korea

2016 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Ma Hoseop ◽  
Kang Wonseok ◽  
Ettagbor Hans Enukwa
Keyword(s):  
Daily Rainfall ◽  
Cumulative Number ◽  
Landslide Occurrence ◽  
Cumulative Rainfall ◽  
Landslide Forecasting ◽  
The Impact

This study presents the impact of cumulative rainfall on landslides, following the analysis of cumulative rainfall for 20 days before the landslide. For the 1520 landslides analyzed, the highest amount of average daily rainfall of 52.9mm occurred the day before the landslide, and the least amount of 6.1mm was experienced 20 days before the landslide. The least number of landslides (263 landslides) occurred when the cumulative rainfall is less than 20mm, and increased to 316 landslides in less than 30mm rainfall, 514 landslides in less than 80mm, 842 landslides in less than 150mm, and 678 landslides in 150mm and above. Considering the landslide occurrence in relation to the cumulative rainfall and the cumulative number of days, 986 landslides (64.9%) of the 1520 landslides were triggered by the 3 days cumulative rainfall for the 100mm rainfall and below, and 60% of landslides at the 5 days cumulative rainfall, indicating that the impact of cumulative rainfall on landslides was high in the 3 days and 5 days cumulative rainfall. More landslides occurred for the 101mm-200mm rainfall at the 10 days cumulative rainfall, more landslides for the 201mm-300mm rainfall at the 14 days cumulative rainfall, and more landslides for the 301mm-400mm rainfall at the 18 days cumulative rainfall. Three typologies of cumulative rainfall triggers are evident in Korea which includes: the early stacked rainfall accumulation type; the long-term intensive rainfall accumulation type; the continuous daily rainfall accumulation type. Cumulative rainfall is thus a major factor causing landslides. It is therefore imperative to take into consideration cumulative rainfall and the cumulative number of days as important triggers of landslides, as this could help contribute in landslide forecasting, thus putting in place measures to minimize the damage caused to life and property by landslides.

scholarly journals Regional landslide forecasting in Piemonte (Italy) and in Norway: experiences from 2013 late spring

2017 ◽  
Author(s):  
Davide Tiranti ◽  
Graziella Devoli ◽  
Roberto Cremonini ◽  
Monica Sund ◽  
Søren Boje
Keyword(s):  
National Level ◽  
Daily Rainfall ◽  
Landslide Hazard ◽  
Weather Type ◽  
Late Spring ◽  
Large Area ◽  
Pressure System ◽  
Weather System ◽  
Landslide Forecasting ◽  
Hazard Assessments

Abstract. A few countries in the world operate systematically national and regional forecasting services for rainfall-induced landslides (i.e. shallow landslides, debris flows and debris avalanches), among them: Norway and Italy. In Norway, the Norwegian Water Resources and Energy Directorate (NVE) operates a landslide forecasting service at national level. A daily national hazard assessment is performed, describing both expected awareness level and type of landslide hazard for a selected warning region. In Italy, each administrative region has its own regional environmental agency (Regional Agency for Environmental Protection, ARPA) that is responsible of the daily landslide hazard assessments and emission of landslide warnings for one or more catchments within the region. One of these agencies, the ARPA Piemonte, is responsible for issuing landslide warnings for the Piemonte region, located in Northwestern Italy. Both services provide regular landslide hazard assessments founded on a combination of quantitative thresholds and daily rainfall forecasts together with qualitative expert analysis. Daily warning reports are published at http://www.arpa.piemonte.gov.it/rischinaturali and www.varsom.no. On spring 2013, the ARPA Piemonte, and the NVE issued warnings for hydro-meteorological hazards due to the arrival of a deep and large low-pressure system, called herein Vb cyclone. This kind of weather system is known to produce the largest floods in Europe. Less known is that the weather type can trigger landslides as well. In this study, we present the experiences acquired in late spring 2013 by NVE and ARPA Piemonte. From 27th April to 19nd May 2013, more than 400 mm rain in Piemonte caused severe floods and diffused landslides. In Norway, the same weather type lasted from 15th May to 2nd June 2013 and brought warm winds with high temperatures that caused intense snow melt over a large area, and brought a lot of rain in the Southeastern Norway, initiating large flood along Glomma river and several landslides. Floods and landslides produced significant damages to roads and railways along with buildings and other infrastructure in both countries.

scholarly journals Susceptibility mapping and estimation of rainfall threshold using space based input for assessment of landslide hazard in Guwahati city in North East India

2014 ◽  
Vol XL-8 ◽  
pp. 15-19 ◽  
Author(s):  
K. Bhusan ◽  
S. S. Kundu ◽  
K. Goswami ◽  
S. Sudhakar
Keyword(s):  
High Resolution ◽  
Rainfall Threshold ◽  
Landslide Hazard ◽  
Capital City ◽  
Landslide Risk ◽  
Eastern Region ◽  
North East ◽  
Hourly Rainfall ◽  
Guwahati City ◽  
Rainfall Estimates

Slopes are the most common landforms in North Eastern Region (NER) of India and because of its relatively immature topography, active tectonics, and intense rainfall activities; the region is susceptible to landslide incidences. The scenario is further aggravated due to unscientific human activities leading to destabilization of slopes. Guwahati, the capital city of Assam also experiences similar hazardous situation especially during monsoon season thus demanding a systematic study towards landslide risk reduction. A systematic assessment of landslide hazard requires understanding of two components, "where" and "when" that landslides may occur. Presently no such system exists for Guwahati city due to lack of landslide inventory data, high resolution thematic maps, DEM, sparse rain gauge network, etc. The present study elucidates the potential of space-based inputs in addressing the problem in absence of field-based observing networks. First, Landslide susceptibility map in 1 : 10,000 scale was derived by integrating geospatial datasets interpreted from high resolution satellite data. Secondly, the rainfall threshold for dynamic triggering of landslide was estimated using rainfall estimates from Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis. The 3B41RT data for 1 hourly rainfall estimates were used to make Intensity-Duration plot. Critical rainfall was estimated for every incidence by analysing cumulative rainfall leading to a landslide for total of 19 incidences and an empirical rainfall intensity-duration threshold for triggering shallow debris slides was developed (Intensity = 5.9 Duration-0.479).

scholarly journals Dendrochronological dating as the basis for developing a landslide hazard map – An example from the Western Carpathians, Poland

2018 ◽  
Vol 45 (1) ◽  
pp. 173-184 ◽  
Author(s):  
Katarzyna Łuszczyńska ◽  
Małgorzata Wistuba ◽  
Ireneusz Malik ◽  
Marek Krąpiec ◽  
Bartłomiej Szypuła
Keyword(s):  
Low Cost ◽  
Sampling Site ◽  
Ring Width ◽  
Landslide Hazard ◽  
Hazard Map ◽  
Large Area ◽  
Landslide Occurrence ◽  
Landslide Activity ◽  
Landslide Event ◽  
Landslide Hazard Map

Abstract Most landslide hazard maps are developed on the basis of an area’s susceptibility to a landslide occurrence, but dendrochronological techniques allows one to develop maps based on past landslide activity. The aim of the study was to use dendrochronological techniques to develop a landslide hazard map for a large area, covering 3.75 km2. We collected cores from 131 trees growing on 46 sampling sites, measured tree-ring width, and dated growth eccentricity events (which occur when tree rings of different widths are formed on opposite sides of a trunk), recording the landslide events which had occurred over the previous several dozen years. Then, the number of landslide events per decade was calculated at every sampling site. We interpolated the values obtained, added layers with houses and roads, and developed a landslide hazard map. The map highlights areas which are potentially safe for existing buildings, roads and future development. The main advantage of a landslide hazard map developed on the basis of dendrochronological data is the possibility of acquiring long series of data on landslide activity over large areas at a relatively low cost. The main disadvantage is that the results obtained relate to the measurement of anatomical changes and the macroscopic characteristics of the ring structure occurring in the wood of tilted trees, and these factors merely provide indirect information about the time of the landslide event occurrence.

scholarly journals Machine learning-based prediction system for rainfall-induced landslides in Benguet First Engineering District

2019 ◽  
Author(s):  
Zanya Reubenne D. Omadlao ◽  
Nica Magdalena A. Tuguinay ◽  
Ricarido Maglaqui Saturay
Keyword(s):  
Machine Learning ◽  
Daily Rainfall ◽  
Predictive Performance ◽  
Data Sets ◽  
Prediction System ◽  
True Positive ◽  
Rainfall Thresholds ◽  
Cumulative Rainfall ◽  
Testing Data ◽  
Positive Rate

A machine learning-based prediction system for rainfall-induced landslides in Benguet First Engineering District is proposed to address the landslide risk due to the climate and topography of Benguet province. It is intended to improve the decision support system for road management with regards to landslides, as implemented by the Department of Public Works and Highways Benguet First District Engineering Office. Supervised classification was applied to daily rainfall and landslide data for the Benguet First Engineering District covering the years 2014 to 2018 using scikit-learn. Various forms of cumulative rainfall values were used to predict landslide occurrence for a given day. Following typical machine learning workflows, rainfall-landslide data set was divided into training and testing data sets. Machine learning algorithms such as K-Nearest Neighbors, Gaussian Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, Decision Tree, and AdaBoost were trained using the training data sets, and the trained models were used to make predictions based on the testing data sets. Predictive performance of the models vis-a-vis the testing data sets were compared using true positive rates, false positive rates, and the area under the Receiver Operating Characteristic Curve. Predictive performance of these models were then compared to 1-day cumulative rainfall thresholds commonly used for landslide predictions. Among the machine learning models evaluated, Gaussian Naïve Bayes has the best performance, with mean false positive rate, true positive rate and area under the curve scores of 7%, 76%, and 84% respectively. It also performs better than the 1-day cumulative rainfall thresholds. This research demonstrates the potential of machine learning for identifying temporal patterns in rainfall-induced landslides using minimal data input -- daily rainfall from a single synoptic station, and highway maintenance records. Such an approach may be tested and applied to similar problems in the field of disaster risk reduction and management.

scholarly journals Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica

2021 ◽  
Author(s):  
Erin Bryce ◽  
Luigi Lombardo ◽  
Cees van Westen ◽  
Hakan Tanyas ◽  
Daniela Castro-Camilo
Keyword(s):  
Spatial Information ◽  
Landslide Hazard ◽  
Sufficient Information ◽  
Tropical Regions ◽  
Landslide Occurrence ◽  
Hurdle Models ◽  
Modelling Framework ◽  
Markovian Representation ◽  
Binary Classifiers ◽  
A Chain

Abstract Climatically-induced natural hazards are a threat to communities. They can cause life losses and heavy damage to infrastructure, and due to climate change, they have become increasingly frequent. This is especially in tropical regions, where major hurricanes have consistently appeared in recent history. Such events induce damage due to the high wind speed they carry, and the high intensity/duration rainfall they discharge can further induce a chain of hydro-morphological hazards in the form of widespread debris slides/flows. The way the scientific community has developed preparatory steps to mitigate the potential damage of these hydro-morphological threats includes assessing where they are likely to manifest across a given landscape. This concept is referred to as susceptibility, and it is commonly achieved by implementing binary classifiers to estimate probabilities of landslide occurrences. However, predicting where landslides can occur may not be sufficient information, for it fails to convey how large landslides may be. This work proposes using a flexible Bernoulli-log-Gaussian hurdle model to simultaneously model landslide occurrence and size per areal unit. Covariate and spatial information are introduced using a generalised additive modelling framework. To cope with the high spatial resolution of the data, our model uses a Markovian representation of the Matérn covariance function based on the stochastic partial differential equation (SPDE) approach. Assuming Gaussian priors, our model can be integrated into the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. We use our modelling approach in Dominica, where Hurricane Maria (September 2017) induced thousands of shallow flow-like landslides passing over the island. Our results show that we can not only estimate where landslides may occur and how large they may be, but we can also combine this information in a unified landslide hazard model, which is the first of its kind.

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