Prevention and mitigation of rainfall induced geological hazards after the Ms=8 Wenchuan earthquake on May 12th, 2008 were significant for rebuild of earthquake hit regions. After the Wenchuan earthquake, there were tens of thousands of fractured slopes which were broken and loosened by the ground shaking, they were very susceptible to heavy rainfall and change forms into potential debris flows. In order to carry out this disaster reduction and prediction effectively in Longmenshan region, careful real-time monitoring and pre-warning of mountain hazards in both regional and site-specific scales is reasonable as alternatives in Wenchuan earthquake regions. For pre-warning the failure of fractured slopes induced by rainfall, the threshold value or the critical value of the precipitation of hazards should be proposed. However, the identification of critical criterion and parameters to pre-warning is the most difficult issue in mountainous hazards monitoring and pre-warning system especially in the elusive and massive fractured slopes widespread in Wenchuan earthquake regions. In this study, a natural coseismic fractured landslide in the Taziping village, Hongkou County, Dujianyan City, was selected to conduct the field experimental test, in order to identify the threshold parameters and critical criterion of the fractured slopes of Taziping. After the field experimental test, the correlation of rainfall intensity, rainfall duration and accumulative rainfall was investigated. The field experimental test was capable of identifying the threshold factors for failure of rainfall-induced fractured slopes after the giant earthquake.
Countering the dangers associated the present extreme climate not only requires continuous improvement of local disaster
prevention engineering infrastructure but also needs an enhanced understanding of the causes of the disasters. This study
investigates the geologic hazard risk of 53 slopeland villages in Pingtung county of southern Taiwan. First, remote sensing
(RS) techniques were utilized to interpret environmental geology and geologic hazard zonation, including dip slope, fault,
landslide and debris flow. GIS map overlay analysis was used to further identify the extent of the geologic hazard zonation.
As a final step, field investigation is used to comprehend geologic, topographic conditions and the geologic hazard risk
specific to each locality. Based on data analysis and field investigation results, this study successfully integrates RS, GIS
and GPS techniques to construct a geologic hazard risk assessment method of slopeland village. The results of this study
can be used to promote support for future disaster prevention and disaster mitigation efforts.
Long-term (21-30 years) erosional responses of rainforest terrain in the Upper Segama catchment, Sabah, to selective logging are assessed at slope, small and large catchment scales. In the 0.44 km(2) Baru catchment, slope erosion measurements over 1990-2010 and sediment fingerprinting indicate that sediment sources 21 years after logging in 1989 are mainly road-linked, including fresh landslips and gullying of scars and toe deposits of 1994-1996 landslides. Analysis and modelling of 5-15 min stream-suspended sediment and discharge data demonstrate a reduction in storm-sediment response between 1996 and 2009, but not yet to pre-logging levels. An unmixing model using bed-sediment geochemical data indicates that 49 per cent of the 216 t km(-2) a(-1) 2009 sediment yield comes from 10 per cent of its area affected by road-linked landslides. Fallout (210)Pb and (137)Cs values from a lateral bench core indicate that sedimentation rates in the 721 km(2) Upper Segama catchment less than doubled with initially highly selective, low-slope logging in the 1980s, but rose 7-13 times when steep terrain was logged in 1992-1993 and 1999-2000. The need to keep steeplands under forest is emphasized if landsliding associated with current and predicted rises in extreme rainstorm magnitude-frequency is to be reduced in scale.
This paper discusses the outcome of a research that examines the relationships between vulnerability and adaptation of urban dwellers to the slope failure threat in the Klang Valley Region. Intense urban landuse expansions in the Klang Valley Region have increased urban dwellers vulnerability to slope failures in recent years. The Klang Valley Region was chosen as the study area due to the increasing intensities and frequencies of slope failures threat. This paper examines urban dwellers vulnerability based on their (1) population and demographics characteristics, (2) the state of physical structures of dwellings and (3) the situation of the immediate environment threatened by slope failures. The locations of slope failure incidents were identified, mapped and examined followed with a detailed field study to identified areas. The results identified significant relationships between vulnerability indicators and slope failures in the Klang Valley Region. The findings of the study are envisaged to give valuable insights on addressing the threat of slope failures in the Klang Valley Region.
Submarine landslides, also known as submarine mass failures (SMFs), are major natural marine disasters that could critically damage coastal facilities such as nuclear power plants and oil and gas platforms. It is therefore essential to investigate submarine landslides for potential tsunami hazard assessment. Three-dimensional seismic data from offshore Brunei have revealed a giant seabed mass deposited by a previous SMF. The submarine mass extends over 120 km from the continental slope of the Baram Canyon at 200 m water depth to the deep basin floor of the Northwest Borneo Trough. A suite of in-house two-dimensional depth-averaged tsunami simulation model TUNA (Tsunami-tracking Utilities and Application) is developed to assess the vulnerability of coastal communities in Sabah and Sarawak subject to potential SMF tsunami. The submarine slide is modeled as a rigid body moving along a planar slope with the center of mass motion parallel to the planar slope and subject to external forces due to added mass, gravity, and dissipation. The nonlinear shallow water equations are utilized to simulate tsunami propagation from deepwater up to the shallow offshore areas. A wetting-drying algorithm is used when a tsunami wave reaches the shoreline to compute run up of tsunami along the shoreline. Run-up wave height and inundation maps are provided for seven densely populated locations in Sabah and Sarawak to highlight potential risks at each location, subject to two scenarios of slide slopes: 2° and 4°. The first wave may arrive at Kudat as early as 0.4 h after the SMF, giving local communities little time to evacuate. Over a small area, maximum inundated depths reaching 20.3 m at Kudat, 26.1 m at Kota Kinabalu, and 15.5 m at Miri are projected, while the maximum inundation distance of 4.86 km is expected at Miri due to its low-lying coast. In view of the vulnerability of some locations to the SMF tsunami, it is important to develop and implement community resilience program to reduce the potential damage that could be inflicted by SMF tsunamis.
In the literatures, discussions on the accuracy of different models for landslide analysis have been discussed widely.
However, to date, arguments on the type of input data (landslides in the form of point or polygon) and how they affect
the accuracy of these models can hardly be found. This study assesses how different types of data (point or polygon)
applied to the same model influence the accuracy of the model in determining areas susceptible to landsliding. A total
of 137 landslides was digitised as polygon (areal) units and then transformed into points; forming two separate datasets
both representing the same landslides within the study area. These datasets were later separated into training and
validation datasets. The polygon unit dataset uses the area density technique reported as percentage, while the point
data uses the landslide density technique, as means of assigning weighting to landslide factor maps to generate the
landslide susceptibility map that is based on the analytical hierarchy process (AHP) model. Both data groups show striking
differences in terms of mapping accuracy for both training and validation datasets. The final landslide susceptibility
map using area density (polygon) as input only has 48% (training) and 35% (validation) accuracy. The accuracy for
the susceptibility map using the landslide density as input data achieved 89% and 82% for both training and validation
datasets, respectively. This result showed that the selection of the type of data for landslide analysis can be critical in
producing an acceptable level of accuracy for the landslide susceptibility map. The authors hope that the finding of this
research will assist landslide investigators to determine the appropriateness of the type of landslide data because it will
influence the accuracy of the final landslide potential map.
In this paper, with debris flow in Zhouqu as the research object, combined with experiments such as cation exchange capacity (CEC), mineral chemical composition and water quality analysis, relation between water and salt in solid source forming debris flow was studied via soil column leaching test and soluble salt analysis, and internal characteristics of debris flow was accordingly showed. It was found that, the soil was loose, and the content of gravel and sand was high, and the content of fine particle was low. The soluble contents at the slope of the accumulation body were described as, collapsed accumulation body > landslide accumulation body, slope toe > slope top, gentle slope > steep slope, also related to length of the slope. The results indicated that accumulations released a large number of base ion after intense weathering, which migrated with water, concentrated and enriched at the slope toe. Saline soil with high salt content collapsed when encountering water and then formed mudflow, thus becoming the internal power to trigger and initiate debris flow to some extent.
This study was carried out on the hilly topographic area in Kundasang, Sabah. This area is known to be extremely prone to landslides that occurred either naturally or by human interference to natural slopes. Aerial photographs interpretation was conducted in order to identify landslide distributions across three assessment years (2012, 2009 and 1984). These datasets were classified into two landslides groups based on their occurrences; natural and artificial. A total of 362 naturally occurring landslides were identified and another 133 are artificial slope landslides. Physical parameters which include lithology, slope angle, slope aspect and soil series were analyzed with each landslide group to examine the different influence of these parameters on each of the group. From the analysis, the landslide density for the natural landslide group shows that more than 35° slope angle and slope aspect facing east and southwest are prone to landslides. In terms of geological materials, high landslide density is recorded in the phyllite, shale, siltstone and sandstone lithologies group and the Pinosuk, Kepayan and Trusmadi soil series. In contrast, for the artificial
slope landslide, high landslide density is observed in the 25°-35° slope angle and similar density in every slope aspect classes. The geological materials however have similar landslide density across their factors’ classes. The landslide density technique was also used to generate the landslide susceptibility maps for both landslide conditions. Validation of the maps shows acceptable accuracy of 71% and 74%, respectively, for both natural and artificial slope landslide susceptibility maps and this shows that these maps can be used for future land use planning.
In this paper, an attempt has been made to assess, prognosis and observe dynamism of soil erosion by universal soil loss equation (USLE) method at Penang Island, Malaysia. Multi-source (map-, space- and ground-based) datasets were used to obtain both static and dynamic factors of USLE, and an integrated analysis was carried out in raster format of GIS. A landslide location map was generated on the basis of image elements interpretation from aerial photos, satellite data and field observations and was used to validate soil erosion intensity in the study area. Further, a statistical-based frequency ratio analysis was carried out in the study area for correlation purposes. The results of the statistical correlation showed a satisfactory agreement between the prepared USLE-based soil erosion map and landslide events/locations, and are directly proportional to each other. Prognosis analysis on soil erosion helps the user agencies/decision makers to design proper conservation planning program to reduce soil erosion. Temporal statistics on soil erosion in these dynamic and rapid developments in Penang Island indicate the co-existence and balance of ecosystem.
Matched MeSH terms: Landslides/statistics & numerical data
Degradation or decline of soil quality that cause shallow slope failure may occur due to physical or chemical processes. It can be triggered off by natural phenomena, or induced by human activity through misuse of land resources, excessive development and urbanization leading to deforestation and erosion of covered soil masses causing serious threat to slopes. The extent of damage of the slopes can be minimized if a long-term early warning system is predicted in the landslide prone areas. The aim of the study was to characterize chemical properties of stable and unstable slope along selected highways of Malaysia which can be manipulated as indicator to forecast shallow slope failure. The elements in soil chemical properties contributed to each other as binding agents that affected the existing soil structure. It could make the soil structure strong or weak. Indicators that can be used to predict shallow slope failure were low content in iron, lead, aluminum, chromium, zinc, low content of organic carbon and CEC.
Slope failure has become a major concern in Malaysia due to the rapid development and urbanisation in the country. It poses severe threats to any highway construction industry, residential areas, natural resources and tourism activities. The extent of damages that resulted from this catastrophe can be lessened if a long-term early warning system to predict landslide prone areas is implemented. Thus, this study aims to characterise the relationship between Oxisols properties and soil colour variables to be manipulated as key indicators to forecast shallow slope failure. The concentration of each soil property in slope soil was evaluated from two different localities that consist of 120 soil samples from stable and unstable slopes located along the North-South Highway (PLUS) and East-West Highway (LPT). Analysis of variance established highly significant difference (P
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.
The aim of this study is to investigate the factors that cause landslides in the area along the new road between Cameron Highlands and Gua Musang. Landslide factors such as lineaments have been extracted from remote sensing data (Landsat TM image) using ERDAS software. A soil map has been produced using field work and laboratory analysis, and the lithology, roads, drainage pattern and rainfall have been digitized using ILWIS software together with the slope angle and elevation from the Digital Elevation Model (DEM). All these parameters, which are vital for landslide hazard assessment, have been integrated into the geographical information system (GIS) for further data processing. Weightage for these landslide relevant factors related to their influence in landslide occurrence using the heuristic method has been carried out. The results from this combination through a modified ‘index overlay with multi class maps’ model was used to produce a landslide hazard zonation map. Five classes of potential landslide hazard have been derived as the following: very low hazard zone 17.27%, low hazard zone 39.35%, medium hazard zone 25.1%, high hazard zone 15.35% and very high hazard zone 2.93%. The results from this work have been checked through the landslide inventory using available aerial photos interpretation and field work, and show that the slope and elevation have the most direct affect on landslide occurrence.
Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models.
Forest ownership is considered as a vital aspect for sustainable management of forest and its associated biodiversity. The Global Forest Resources Assessment 2015 reported that privately owned forest area are increasing on a global scale, but deforestation was found very active in privately owned hill forest areas of Malaysia. Penang State was purposively chosen as it has been experiencing rapid and radical changes due to urban expansion over the last three decades. In this study, analyses of land-use changes were done by PCI Geomatica using Landsat images from 1991 to 2015, future trends of land-use change were assessed using EXCEL forecast function, and its impact on the surrounding environment were conducted by reviewing already published articles on changing environment of the study area. This study revealed an annual deforestation rate of 1.4% in Penang Island since 1991. Trend analysis forecasted a forest area smaller than the current forest reserves by the year 2039. Impact analysis revealed a rapid biodiversity loss with increasing landslides, mudflows, water pollution, flash flood, and health hazard. An immediate ban over hill-land development is crucial for overall environmental safety.
Many residential areas of Kuala Lumpur are susceptible to landslides; this is seen in the frequency of landslide occurences in these areas. The objective of this study is to delineate landslide risk areas in support of development planning, monitoring and control of unstable areas. In this study, five landslide causative factors were extracted from satellite imagery and maps provided by the Geological Survey Department of Malaysia. Factors included in the study including land use, river density and lineament derived from Landsat ETM image, precipitation amount from rain gauge stations and lithology, were extracted from the geological map of the study area. Layers were analyzed and divided into subclasses. An average weightage score was applied to calculate the subclasses into percentage weights of influence on landslide. Overlay, geo-processing and geo-statistic techniques in GIS were used to discriminate these weighted subclasses into landslide susceptibility at low, medium and high levels of risk areas. Results showed very high susceptible areas covering 0.21% of Kuala Lumpur of which 5.02% were found in the highly urbanized areas. Meanwhile, a landslide susceptibility map was generated to show low, medium and high susceptible areas in Kuala Lumpur. Results were verified using recorded cases of landslides in Kuala Lumpur which showed a 77% agreement with the study.
Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer's V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.
The objective of this study was to investigate the capabilities of low-cost digital cameras in volume determination. Low-cost digital cameras are capable of many applications including aerial photogrammetry and close-range photogrammetry. Low-cost digital cameras have the potential to be used in landslide monitoring and mapping. In this study, a low-cost digital camera was used as a tool to acquire digital images of a model of a simulated landslide. The model was constructed using cement and sand with the dimensions of 3m in length and 1m width. Digital images of the simulated model were acquired using the technique of aerial photogrammetry and were subsequently processed using digital photogrammetric software. A portion of the simulated model was excavated to simulate a landslide and volume determination was carried out for the excavated sand. The results showed that low-cost digital cameras can be used in photogrammetric application including volume determination.
Analysis of data, recorded on March 8th 2014 at the Comprehensive Nuclear-Test-Ban Treaty Organisation's hydroacoustic stations off Cape Leeuwin Western Australia, and at Diego Garcia, reveal unique pressure signatures that could be associated with objects impacting at the sea surface, such as falling meteorites, or the missing Malaysian Aeroplane MH370. To examine the recorded signatures, we carried out experiments with spheres impacting at the surface of a water tank, where we observed almost identical pressure signature structures. While the pressure structure is unique to impacting objects, the evolution of the radiated acoustic waves carries information on the source. Employing acoustic-gravity wave theory we present an analytical inverse method to retrieve the impact time and location. The solution was validated using field observations of recent earthquakes, where we were able to calculate the eruption time and location to a satisfactory degree of accuracy. Moreover, numerical validations confirm an error below 0.02% for events at relatively large distances of over 1000 km. The method can be developed to calculate other essential properties such as impact duration and geometry. Besides impacting objects and earthquakes, the method could help in identifying the location of underwater explosions and landslides.