Population increase and the demand for infrastructure development such as construction of highways and road widening are intangible, leading up to mass land clearing. As flat terrains become scarce, infrastructure expansions have moved on to hilly terrains, cutting through slopes and forests. Unvegetated or bare slopes are prone to erosion due to the lack of or insufficient surface cover. The combination of exposed slope, uncontrolled slope management practices, poor slope planning and high rainfall as in Malaysia could steer towards slope failures which then results in landslides under acute situation. Moreover, due to the tropical weather, the soils undergo intense chemical weathering and leaching that elevates soil erosion and surface runoff. Mitigation measures are vital to address slope failures as they lead to economic loss and loss of lives. Since there is minimal or limited information and investigations on slope stabilization methods in Malaysia, this review deciphers into the current slope management practices such as geotextiles, brush layering, live poles, rock buttress and concrete structures. However, these methods have their drawbacks. Thus, as a way forward, we highlight the potential application of soil bioengineering methods especially on the use of whole plants. Here, we discuss the general attributions of a plant in slope stabilization including its mechanical, hydrological and hydraulic effects. Subsequently, we focus on species selection, and engineering properties of vegetation especially rooting structures and architecture. Finally, the review will dissect and assess the ecological principles for vegetation establishment with an emphasis on adopting the mix-culture approach as a slope failure mitigation measure. Nevertheless, the use of soil bioengineering is limited to low to moderate risk slopes only, while in high-risk slopes, the use of traditional engineering measure is deemed more appropriate and remain to be the solution for slope stabilization.
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.
Globally, there has been an increase in the frequency of landslides which is the result of slope failures. The combination of high intensity rainfall and high temperature resulted in the formation of acidic soil which is detrimental to the healthy growth of plants. Proper plant coverage on slopes is a prerequisite to mitigate and rehabilitate the soil. However, not all plant species are able to grow in marginal land. Thus, this study was undertaken to find a suitable slope plant species. We aimed to evaluate the effect of different soil pH on root profiles and growth of three different potential slope plant species namely, Melastoma malabathricum, Hibiscus rosa-sinensis and Syzygium campanulatum. M. malabathricum showed the highest tolerance to acidic soil as it recorded the highest plant height and photosynthetic rate. The root systems of M. malabathricum, H. rosa-sinensis and S. campanulatum were identified as M, VH- and R-types, respectively. The study proposed M. malabathricum which possessed dense and shallow roots to be planted at the toe or top of the slope while H. rosa-sinensis and S. campanulatum to be planted in the middle of a slope. S. campanulatum consistently recorded high root length and root length density across all three types of soil pH while M. malabathricum showed progressive increase in length as the soil pH increased. The root average diameter and root volume of M. malabathricum outperformed the other two plant species irrespective of soil pH. In terms of biomass, M. malabathricum exhibited the highest root and shoot dry weights followed by S. campanulatum. Thus, we propose M. malabathricum to be planted on slopes as a form of soil rehabilitation. The plant species displayed denser rooting, hence a stronger root anchorage that can hold the soil particles together which will be beneficial for slope stabilization.
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.
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.
Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.
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.
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
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.
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.
Preparation of natural hazards maps are vital and essential for urban development. The main scope of this study is to synthesize natural hazard maps in a single multi-hazard map and thus to identify suitable areas for the urban development. The study area is the drainage basin of Xerias stream (Northeastern Peloponnesus, Greece) that has frequently suffered damages from landslides, floods and earthquakes. Landslide, flood and seismic hazard assessment maps were separately generated and further combined by applying the Analytical Hierarchy Process (AHP) and utilizing a Geographical Information System (GIS) to produce a multi-hazard map. This map represents the potential suitability map for urban development in the study area and was evaluated by means of uncertainty analysis. The outcome revealed that the most suitable areas are distributed in the southern part of the study area, where the landslide, flood and seismic hazards are at low and very low level. The uncertainty analysis shows small differences on the spatial distribution of the suitability zones. The produced suitability map for urban development proves a satisfactory agreement between the suitability zones and the landslide and flood phenomena that have affected the study area. Finally, 40% of the existing urban pattern boundaries and 60% of the current road network are located within the limits of low and very low suitability zones.
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, 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.
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.
On the basis of landslide surge model test by adopting generalized simulation of waterways, this paper, for the first time, established a four-dimensional mathematical model between wave height transmissibility rate and the initial wave height, water depth, azimuth angle as well as propagation distance through utilizing the method of tensor space mapping. Using the new model, we proposed an empirical wave field covering all areas of the channel including the attenuation area within the width of a landslide mass, the straight channel attenuation area outside the width of the landslide mass, the curved channel attenuation area and the after-curve attenuation area, which comprehensively reflects the progressive changes of surge wave factors. The transmissibility of wave height and propagation distance are in a bivariate negative exponential distribution, and the wave height gradually reduces and the attenuation also slows down as the propagation distance increases; wave height transmissibility rate, azimuth and propagation distance are in a trivariate negative exponential distribution, the attenuation of the wave height in the straight channel within the width of the landslide mass was the slowest, followed by that of wave in the straight channel outside the width of the landslide mass, and the attenuation of the wave height in the curved channel is the greatest. This empirical wave field was based on test data, scientifically abstracted the general regularity of the propagation and attenuation of landslide surge, which can be applied to similar analyses and forecasts on landslide surge and can scientifically and accurately determine the damage range of landslide surge.
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.
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.
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.
This article presents a preliminary investigation on the motivations for and the barriers that hinder preparedness toward disasters in a community. Survey questionnaires were distributed to local individuals in the nine districts of Selangor state in Malaysia. A total of 402 usable questionnaires were analyzed. The initial findings revealed that community members are motivated for disaster preparedness mainly for family safety reason. However, generally they do not know how to be prepared. This article concludes by highlighting the importance of knowledge and information in community preparedness. This research is limited to one state in Malaysia. However, the chosen state has a large effect on the Malaysian gross domestic product; hence, lack of preparedness poses a critical risk to its large population. This study on motivation and barriers for disaster preparedness is intended to increase the effectiveness of community readiness as a whole toward major disasters such as landslide and flood. The result of this study is valuable to the scientific community within the disaster management domain, the government agencies for policy and strategy formulations, and the local community to preempt, deal with, and ultimately survive disasters. This research aims to ensure that the community is continuously prepared and able to meet the evolving needs of the individual citizen as the nation strives toward promoting a knowledgeable society.