Slope Assessment Systems: A Review and Evaluation of Current Techniques Used for Cut Slopes in the Mountainous Terrain of West Malaysia

In Malaysia, slope assessment systems (SAS) are widely used in assessing the instability of slopes or the probability of occurrence and likely severity of landslides. These SAS can be derived based on either one particular approach or combination of several approaches of landslide assessments and prediction. This paper overviews five slope assessment systems (SAS) developed in Malaysia for predicting landslide for large-scale assessments. They are the Slope Maintenance System (SMS), Slope Priority Ranking System (SPRS), Slope Information Management System (SIMS), the Slope Management and Risk Tracking System (SMART), and the Landslide Hazard and Risk Assessment (LHRA). An attempt is made to evaluate the accuracy of these SAS in predicting landslides based on slope inventory data from 139 cut slopes in granitic formations, and 47 cut slopes in meta-sediment formations, which are the two most common rock/soil formations found in West Malaysia. Based on this study, it was found that none of the existing SAS is satisfactory for predicting landslides of cut slopes in granitic formations, for various reasons such as the use of a hazard score developed from another country, an insufficient data base, an oversimplified approach, and the use of data base derived from different rock/soil formations. However for the case of cut slopes in meta-sediment, the Slope Management and Risk Tracking System (SMART) was found to be satisfactory with a 90% prediction accuracy. The current database of SMART is largely based on meta-sediment formations from the Kundusang area of Sabah, East Malaysia

Improving landslide detection from airborne laser scanning data using optimized Dempster-Shafer

© 2018 by the authors. A detailed and state-of-the-art landslide inventory map including precise landslide location is greatly required for landslide susceptibility, hazard, and risk assessments. Traditional techniques employed for landslide detection in tropical regions include field surveys, synthetic aperture radar techniques, and optical remote sensing. However, these techniques are time consuming and costly. Furthermore, complications arise for the generation of accurate landslide location maps in these regions due to dense vegetation in tropical forests. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) is typically employed to generate accurate landslide maps. The object-based technique generally consists of many homogeneous pixels grouped together in a meaningful way through image segmentation. In this paper, in order to address the limitations of this approach, the final decision is executed using Dempster-Shafer theory (DST) rule combination based on probabilistic output from object-based support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN) classifiers. Therefore, this research proposes an efficient framework by combining three object-based classifiers using the DST method. Consequently, an existing supervised approach (i.e., fuzzy-based segmentation parameter optimizer) was adopted to optimize multiresolution segmentation parameters such as scale, shape, and compactness. Subsequently, a correlation-based feature selection (CFS) algorithm was employed to select the relevant features. Two study sites were selected to implement the method of landslide detection and evaluation of the proposed method (subset "A" for implementation and subset "B" for the transferrable). The DST method performed well in detecting landslide locations in tropical regions such as Malaysia, with potential applications in other similarly vegetated regions

Evaluation Of Slope Stability Due To Earthquake And Rainfall Occurrences

Malaysia is a tropical country which has high humidity and heavy rainfall. The annual average rainfall for Peninsular Malaysia is 2,300 mm. In other aspect, earthquake is not a frequent natural disaster happened in Malaysia. However, the case of local earthquake displays a slightly increasing trend. Besides, residents often felt tremors of earthquake from neighbouring region such as Sumatra. The 2004 Sumatera earthquake even caused 68 people killed in Penang, Langkawi, and Kedah. It is unknown for the combined effect of rainfall and earthquake to the existing slope structures in Penang Island. This dissertation presents the results on slope stability after the impact of rainfall and earthquake using the GeoStudio software. When considering the single effect of rainfall only, the results show that rainfall with longer duration and higher rainfall intensity will adversely affect the slope stability. The rainfall-induced slope instability often related to the rise of groundwater table level. On the other hand, the factor of safety for selected slope are even lower when both effect of rainfall and earthquake are considered in the analysis since the seismic waves will be imposed as secondary damage to the slope. Deformation is another parameter that can be used to evaluate the stability of slope affected by seismic waves. The results show that a higher deformation is recorded for slope geometry which has bigger slope angle and higher slope height. Reinforcement is essential to improve the slope stability so the factor of safety greater than 1.0 and deformation can be reduced at the same time. One of the reinforcements applied in this research is retaining wall which can improve the slope stability under the effect of rainfall and earthquake. Soil nailing is another reinforcement that can reduce the effect of rainfall since it minimizes the soil displacements through bonding to the surrounding soil. The importance of proper drainage system including surface drainage and subsoil drainage was studied as well

The doctoral research abstracts. Vol:6 2014 / Institute of Graduate Studies, UiTM

Congratulations to Institute of Graduate Studies on the continuous efforts to publish the 6th issue of the Doctoral Research Abstracts which ranged from the discipline of science and technology, business and administration to social science and humanities. This issue captures the novelty of research from 52 PhD doctorates receiving their scrolls in the UiTM’s 81st Convocation. This convocation is very significant especially for UiTM since we are celebrating the success of 52 PhD graduands – the highest number ever conferred at any one time. To the 52 doctorates, I would like it to be known that you have most certainly done UiTM proud by journeying through the scholastic path with its endless challenges and impediments, and by persevering right till the very end. This convocation should not be regarded as the end of your highest scholarly achievement and contribution to the body of knowledge but rather as the beginning of embarking into more innovative research from knowledge gained during this academic journey, for the community and country. As alumni of UiTM, we hold you dear to our hearts. The relationship that was once between a student and supervisor has now matured into comrades, forging and exploring together beyond the frontier of knowledge. We wish you all the best in your endeavour and may I offer my congratulations to all the graduands. ‘UiTM sentiasa dihati ku’ Tan Sri Dato’ Sri Prof Ir Dr Sahol Hamid Abu Bakar , FASc, PEng Vice Chancellor Universiti Teknologi MAR

Google the earth: what's next?

Sensing the Earth has proven to be a tremendously valuable tool for understanding the world around us. Over the last half-century, we have built a sophisticated network of satellites, aircraft, and ground-based remote sensing systems to provide raw information from which we derive and improve our knowledge of the Earth and its phenomena. Through remote sensing, our basic scientific knowledge of the Earth and how it functions has expanded rapidly in the last few decades. Applications of this knowledge, from natural hazard prediction to resource management, have already proven their benefit to society many times over. Today maps and satellite imageries have become an integral part of the developmental process and have also triggered new business opportunities. Maps are essential at all stages of infrastructure development, resource planning and the disaster management cycle. Satellite imagery/data can be used for everything from ground truthing and change detection, to more sophisticated analyses, including feature extraction and natural hazard prediction. As imagery has become more accessible and more affordable in recent years, there is also a growing convergence of imagery and geographic information system (GIS) applications. Geospatial scientists and analysts thus, need to be able to easily access imagery and move seamlessly between GIS and image processing applications to derive the most information possible from them. Technologically, the challenge is to design sensors that exhibit high sensitivity to the parameters of interest while minimizing instrument noise and impacts of other natural variables. The scientific challenge is to develop retrieval algorithms that describe the physical measurement process in sufficient detail, yet are simple enough to allow robust inversion of the remotely sensed signals. Considering the exponential growth of data volumes driven by the rapid progress in sensor and computer technologies in recent years, the future of remotely sensed data should ideally be in automated data processing, development of robust and transferable algorithms and processing chains that require little or no human intervention. In meeting the above mentioned challenges, some research works have been done at Universiti Putra Malaysia. These works cover all aspects of the remote sensing process, from instrument design, image processing, image analysis to the retrieval of geophysical parameters and their application in natural resources planning and disaster management. Some of the major research efforts include feature extraction from satellite imagery; spatial decision support system for oil spill detection, monitoring and contingency planning; fish forecasting; UAV-based remote imaging and natural disaster management and early warning systems for floods and landslides. This lecture concludes that through remote sensing, our basic scientific knowledge of the Earth and how it functions have expanded rapidly in the last few decades. Applications of this knowledge, from natural hazard prediction to resource management, have already proven to be beneficial to society many times over. As the demand for even faster, better and more temporally and spatially variable information grows dramatically, this lectures answers the question of what remote sensing will be like in the coming decades and the new capabilities and challenges that will emerg

Urban morphology analysis by remote sensing and gis technique, case study: Georgetown, Penang

This paper was analysed the potential of applications of satellite remote sensing to urban planning research in urban morphology. Urban morphology is the study of the form of human settlements and the process of their formation and transformation. It is an approach in designing urban form that considers both physical and spatial components of the urban structure. The study conducted in Georgetown, Penang purposely main to identify the evolution of urban morphology and the land use expansion. In addition, Penang is well known for its heritage character, especially in the city of Georgetown with more than 200 years of urban history. Four series of temporal satellite SPOT 5 J on year 2004, 2007, 2009 and 2014 have been used in detecting an expansion of land use development aided by ERDAS IMAGINE 2014. Three types of land uses have been classified namely build-up areas, un-built and water bodies show a good accuracy with achieved above 85%. The result shows the built-up area significantly increased due to the rapid development in urban areas. Simultaneously, this study provides an understanding and strengthening a relation between urban planning and remote sensing applications in creating sustainable and resilience of the city and future societies as well

LANDSLIDE SUSCEPTIBILITY MODELLING UNDER ENVIRONMENTAL CHANGES: A CASE STUDY OF CAMERON HIGHLANDS, MALAYSIA

Modeling landslide susceptibility usually does not include multi temporal factors, e.g. rainfall, especially for medium scale. Landslide occurrences in Cameron Highlands, in particular, and in Peninsular Malaysia, in general, tend to increase during the peak times of monsoonal rainfall. Due to the lack of high spatial resolution of rainfall data, Normalized Different Vegetation Index (NDVI), soil wetness, and LST (Land Surface Temperature) were selected as replacement of multi temporal rainfall data. This research investigated their roles in landslide susceptibility modeling. In doing so, four Landsat 7 Enhanced Multi Temporal Plus (ETM+) images acquired during two peak times of rainy and dry seasons were used to derive multi temporal NDVI, soil wetness, and LST. Topographic, geology, and soil maps were used to derive ‘static’ factors namely slope, slope aspect, curvature, elevation, road network, river/lake, lithology, soil geology lineament maps. Landslide map was used to derive weighting system based on spatial relationship between landslide occurrences and landslide factor using bivariate statistical method. A non-statistical weighting system was also used for comparison purpose. Different scenarios of data processing were applied to allow evaluation on the roles of multi temporal factors in landslide susceptibility modeling in terms of the accuracy of the landslide susceptibility maps (LSMs), the appropriate weighting system of the models, the applicability of the model, the ability to confirm the relation between landslide occurrences and rainfall. The results show that the average accuracy of LSMs produced by the developed models with inclusion of multi temporal factors is 49.1% on the overall. Addition of LST tends to improve the accuracy of LSMs. NDVI can be a suitable replacement for rainfall data since it can explain the relation between landslides occurrences and rainfall cycle. Statistical-based weighting system produced more accurate LSMs than non-statistical-based one and is applicable for landslide susceptibility modeling elsewhere. Significant causative factors were proven to produce more accurate LSMs

Landslide susceptibility mapping using remote sensing data and geographic information system-based algorithms

Whether they occur due to natural triggers or human activities, landslides lead to loss of life and damages to properties which impact infrastructures, road networks and buildings. Landslide Susceptibility Map (LSM) provides the policy and decision makers with some valuable information. This study aims to detect landslide locations by using Sentinel-1 data, the only freely available online Radar imagery, and to map areas prone to landslide using a novel algorithm of AB-ADTree in Cameron Highlands, Pahang, Malaysia. A total of 152 landslide locations were detected by using integration of Interferometry Synthetic Aperture RADAR (InSAR) technique, Google Earth (GE) images and extensive field survey. However, 80% of the data were employed for training the machine learning algorithms and the remaining 20% for validation purposes. Seventeen triggering and conditioning factors, namely slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, Normalized Difference Vegetation Index (NDVI), rainfall, land cover, lithology, soil types, curvature, profile curvature, Stream Power Index (SPI) and Topographic Wetness Index (TWI), were extracted from satellite imageries, digital elevation model (DEM), geological and soil maps. These factors were utilized to generate landslide susceptibility maps using Logistic Regression (LR) model, Logistic Model Tree (LMT), Random Forest (RF), Alternating Decision Tree (ADTree), Adaptive Boosting (AdaBoost) and a novel hybrid model from ADTree and AdaBoost models, namely AB-ADTree model. The validation was based on area under the ROC curve (AUC) and statistical measurements of Positive Predictive Value (PPV), Negative Predictive Value (NPV), sensitivity, specificity, accuracy and Root Mean Square Error (RMSE). The results showed that AUC was 90%, 92%, 88%, 59%, 96% and 94% for LR, LMT, RF, ADTree, AdaBoost and AB-ADTree algorithms, respectively. Non-parametric evaluations of the Friedman and Wilcoxon were also applied to assess the models’ performance: the findings revealed that ADTree is inferior to the other models used in this study. Using a handheld Global Positioning System (GPS), field study and validation were performed for almost 20% (30 locations) of the detected landslide locations and the results revealed that the landslide locations were correctly detected. In conclusion, this study can be applicable for hazard mitigation purposes and regional planning

Development of a local integrated disaster risk assessment framework for Malaysia

This study developed an integrated disaster risk assessment framework (IDRAF) to measure disaster risk at the local administrative boundaries in Malaysia. The proposed framework can enhance government effort for disaster risk reduction by implementing an integrated disaster risk framework and guiding decision makers to properly evaluate and analyze risk for mitigation, preparedness, and planning. The framework was developed, expanding from the multi‐hazard spatial overlapping and Methods for the Improvement of Vulnerability Assessment in Europe (MOVE) theoretical framework. There are five significant phases to develop this framework: problem formulation, data collection, multi‐hazard characterization, multi‐dimensional vulnerability characterization, and weightage determination. The IDRAF proposed for Malaysia consists of eight hazards and six vulnerability dimensions, which consist of 16 factors (or vulnerability group) and 54 indicators. The multi‐hazard characterization has two components: frequency of occurrence and spatial inter-action. The multidimensional vulnerability characterization reduces vulnerability indicators using principal component analysis (PCA). Measuring integrated risk is an effective strategy at the local level or national level to assess the potential disaster impacts in detail and accurately. This study will offer explicit knowledge and boost community competency, creating techniques and tools to analyze various risk factors and vulnerability indicators for decision makers and practitioners