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Abstract: Landslide-susceptibility mapping is one of the most critical issues in Malaysia. These landslides can be systematically assessed and mapped through a traditional mapping framework that uses geoinformation technologies (GIT). The main purpose of this paper is to investigate the possible application of an artificial neural network model and its cross-application of weights at three study areas in Malaysia, Penang Island, Cameron Highland and Selangor. Landslide locations were identified in the study areas from the interpretation of aerial photographs, field surveys and inventory reports. A landslide-related spatial database was constructed from topographic, soil, geology, and land-cover maps. For the calculation of the relative weight and importance of each factor to a particular landslide occurrence, an artificial neural network (ANN) method was applied. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the artificial neural network model. Then, the landslide-susceptibility analysis results were validated and cross-validated using the landslide locations as study areas. Different training sites were randomly selected to train the neural network, and nine sets of landslide-susceptibility maps were prepared. The paper then illustrates the verification of those maps using an “area under the curve” (AUC) method. The verification results show that the case of the weight using the same test area showed slightly higher accuracy than the weight used for the cross-applied area. Among the three studied areas, the verification results showed similar accuracy trends while using the weight for the study area itself. Cameron showed the best accuracy and Penang showed the worst accuracy. Generally, the verification results showed satisfactory agreement between the susceptibility map and the existing data on the landslide location. [Copyright &y& Elsevier]

This paper focuses on the observation and behaviours of green turtles for one breeding season at Penang Island. Green turtle breeding behaviours and morphological characteristics were studied at Pantai Kerachut and Teluk Kampi, situated at Penang Island, the northern island of Peninsular Malaysia between December 2012 and August 2013. The findings revealed that from the eight green turtles landed; 106 were emergences, 38 were nests, and 158 digging attempts were made over the duration of one breeding season. The total clutch size deposited ranged from 170 to 979 eggs per turtle, and total nests ranged from three to eight nests per turtle. The inter-nesting interval ranged from 9 to 29 days, and the average was 13.5 days. Longer inter-nesting intervals were due to disturbances from feral dogs, wild pigs, sea-coated otters and night activities of fishermen (use of wide torch lights, sounds of boat engines) especially at Teluk Kampi. Lack of full-time personnel to control at Teluk Kampi was found to be a cause to nest poaching. Based on these findings and observation, the study suggests that Kerachut Turtle Conservation Centre should hire sufficient staffs to patrol the two beaches in order to prevent from human poachers and animal disturbances. [ABSTRACT FROM AUTHOR]

This paper proposes a decision treemodel for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-squareAutomatic InteractionDetector (CHAID), ExhaustiveCHAID, Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). Twenty-one factors were extracted using digital elevation models (DEMs) and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold crossvalidation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0%) compared to CHAID (81.9%), CRT (75.6%), and QUEST (74.0%) model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature [ABSTRACT FROM AUTHOR]

The Green Turtle (Chelonia mydas) and Olive Ridley Turtle (Lepidochelys olivacea) are the only sea turtles with recorded landings in the Pulau Pinang coastal area. The Green Turtle has been the most abundant and widely distributed sea turtle in this area since it was first surveyed in 1995. Statistical analysis by the Pulau Pinang Department of Fisheries on the distribution of sea turtles from 2001 through 2009 has identified Pantai Kerachut and Telok Kampi as the most strongly preferred beaches for Green Turtle landings, with records for almost every month in every year. Green Turtle tracks and nests have also been found along the coast of Pulau Pinang at Batu Ferringhi, Tanjong Bungah, Pantai Medan, Pantai Belanda, Telok Kumbar, Gertak Sanggul, Moonlight Beach, Telok Duyung, Telok Aling, Telok Bahang and Telok Katapang. The Olive Ridley Turtle is present in smaller numbers; landing and nesting have only been recorded on a few beaches. There are no previous records of Olive Ridley landings at Pantai Kerachut and Telok Kampi, but tracks and nests have been found at Telok Kumbar, Tanjong Bungah, Pantai Medan, Telok Duyung and Gertak Sanggul. A Turtle Conservation Centre has been established at Pantai Kerachut to protect these species from extinction in Pulau Pinang. This paper presents details of the records and distribution of sea turtles in Pulau Pinang from 1995 through 2009. [ABSTRACT FROM AUTHOR]

Development of hilly areas requiring deforestation and increased human activities has raised many environmental issues in the world. This paper presents the results of investigation to assess the parameters that contribute to the risk to environment by reviewing available reports from Penang Island and surrounding areas. Nine parameters were identified that mostly contribute to the environmental risk. These are casualties of people, soil properties, earth coverage, soil grading characteristics, land use suitability, factor of safety, blasting area, distance between proposed structure to slope and geometry of slope. From these parameters, the rated risk approaches were proposed based on the prediction of occurrences of environmental issues. The rated risks were categorised from very high (5) to very low (1) with high, medium and low in between. The assessments were reviewed and evaluated based on rated risk for the chosen parameters. A map was developed identifying areas of environmental risk. The map can be used to quantify risk to the environment based on the parameters due to the proposed development. The overall result shows that areas PD3 of Tanjung Bungah and PD3, PD8 and PD9 of Paya Terubong have high-rated risk. No development area is classified as very high risk and very low-risk areas. These rating approaches can be used to develop potential risk zoning of proposed development areas. From this zoning guideline or standard, the viability of any proposed development can be measured. [ABSTRACT FROM AUTHOR]

This paper presents the application of remote sensing techniques, digital image analysis and Geographic Information System tools to delineate the degree of landslide hazard and risk areas in the Balik Pulau area in Penang Island, Malaysia. Its causes were analysed through various thematic attribute data layers for the study area. Firstly, landslide locations were identified in the study area from the interpretation of aerial photographs, satellite imageries, field surveys, reports and previous landslide inventories. Topographic, geologic, soil and satellite images were collected and processed using Geographic Information System and image processing tools. There are 12 landslide-inducing parameters considered for the landslide hazard analyses. These parameters are: topographic slope, topographic aspect, plan curvature, distance to drainage and distance to roads, all derived from the topographic database; geology and distance to faults, derived from the geological database; landuse/landcover, derived from Landsat satellite images; soil, derived from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value, derived from SPOT satellite images. In addition, hazard analyses were performed using landslide-occurrence factors with the aid of a statistically based frequency ratio model. Further, landslide risk analysis was carried out using hazard map and socio-economic factors using a geospatial model. This landslide risk map could be used to estimate the risk to population, property and existing infrastructure like transportation networks. Finally, to check the accuracy of the success-rate prediction, the hazard map was validated using the area under curve method. The prediction accuracy of the hazard map was 89%. Based on these results the authors conclude that frequency ratio models can be used to mitigate hazards related to landslides and can aid in land-use planning. [ABSTRACT FROM AUTHOR]

This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs and field surveys. Topographical/ geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%. The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network. [ABSTRACT FROM AUTHOR]