Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are three main stages involved in this study. The first stage is the extraction of extra topographic factors. Previous landslide studies had identified mainly six topographic factors. Seven new additional factors have been proposed in this study. They are longitude curvature, tangential curvature, cross section curvature, surface area, diagonal line length, surface roughness, and rugosity. The second stage is the specification of the weight of each factor using two methods. The methods are multilayer perceptron (MLP) network classification accuracy and Zhou's algorithm. At the third stage, the factors with higher weights were used to improve the MLP performance. Out of the thirteen factors, eight factors were considered as important factors, which are surface area, longitude curvature, diagonal length, slope angle, elevation, slope aspect, rugosity, and profile curvature. The classification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors.
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.
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.
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.
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.
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.
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.
The 2004 Banda Aceh earthquake and ensuing Andaman mega tsunami that killed a quarter million people worldwide is a wake-up call to many. Active research was initiated in Universiti Sains Malaysia (USM) immediately after the infamous event with the aims to help develop human capacity and resources, and to mitigate any future earthquake and tsunami. The Disaster Research Nexus (DRN) was formed recently within the School of Civil Engineering, USM, to facilitate active collaborative research on earthquakes and tsunamis, as well as on other natural disasters, such as landslides. This paper begins with an introduction to DRN. This is followed by a description of some research achievements undertaken by DRN staff. A concise exposition on the tsunami simulation model TUNA developed by the authors and its application to the 2004 Andaman tsunami are given to illustrate the capability of TUNA. The role of mangrove in reducing the impact of tsunami is then modelled. Tsunami may inundate coastal plain with large quantity of saline water, changing the salinity regimes in the soil and inducing vegetative succession changes. A model called MANHAM was developed to simulate the salinity changes and its associated vegetative evolution to assist in the rehabilitation of vegetation destroyed by tsunami. Meanwhile, an earthquake risk analysis for the Upper Pandas Dam in Sabah is then presented, and this is followed by a model estimation of tsunami forces on the coastal structures. The main objective of this paper is to reach out to research scientists and onsite risk reduction professionals to collaborate towards the development of a vibrant research culture to face future natural disasters such as earthquakes and tsunamis. It is hoped that DRN will move forward to further enhance active collaborations with other research and operational institutions worldwide towards developing earthquake and tsunami resilient communities.
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.
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.
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.
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.
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.
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.
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.
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.
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
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