Norbazlan Mohd Yusof
Head, Database Management Department
PLUS Expressways
Malaysia

Biography
Norbazlan Mohd Yusof holds a Master of Remote Sensing and GIS from University Putra Malaysia in 2009 and Bachelor Degree Honours in Surveying and Mapping Sciences from the University of East London in the United Kingdom in 1996. He has more than 18 years of working experience in GIS particularly in highway asset management and currently holds the post as Head of Department where he is responsible for all matter pertaining to the Geoinformation technology in PLUS Berhad.

Abstract
Landslide Hazard Analysis at Jelapang of North-South Expressway in Malaysia using High-Resolution Airborne LiDAR Data

Co-Authors:
Biswajeet Pradhan, Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering, University Putra Malaysia
Helmi Zulhaidi Mohd Shafri, Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering, University Putra Malaysia
Mustafa Neamah Jebur, Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering, University Putra Malaysia
Zainuddin Yusoff, Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering, University Putra Malaysia

PLUS Berhad holds the concession for a total of 987 km of toll expressways in Malaysia, the longest of which is the North-South Expressway or NSE. Acting as the 'backbone' of the west coast of the peninsula, the NSE stretches from the Malaysian-Thai border in the north to the border with neighbouring Singapore in the south, linking several major cities and towns along the way. North-South Expressway in Malaysia contributes to the country economic development through trade, social and tourism sector. Presently, the highway is good in terms of its condition however some locations need more attention. Stability of slopes at these locations is of most concern as any instability can cause danger to the motorist.

Mapping landslide-prone regions are crucial in natural hazard management and urban development activities in hilly and tropical regions. This research aimed to delineate a spatial prediction of landslide hazard areas along the Jelapang Corridor of the North-South Expressway in Malaysia by using two statistical models, namely, logistic regression (LR) and evidential belief function (EBF). Landslides result in high economic and social loses in Malaysia, particularly to highway concessionaries such as PLUS Berhad. LR and EBF determine the correlation between conditioning factors and landslide occurrence. EBF can also be applied in bivariate statistical analysis. Thus, EBF can be used to assess the effect of each class of conditioning factors on landslide occurrence. A landslide inventory map with 26 landslide sites was recorded using field measurements. Subsequently, the landslide inventory was randomly divided into two data sets.

Approximately 70 % of the data were used for training the models, and 30 % were used for validating the results. Eight landslide conditioning factors were prepared for landslide susceptibility analysis: altitude, slope, aspect, curvature, stream power index, topographic wetness index, terrain roughness index, and distance from river. The landslide probability index was derived from both methods and subsequently classified into five susceptible classes by using the quantile method. The resultant landslide susceptibility maps were evaluated using the area under the curve technique. Results revealed the proficiency of the LR method in landslide susceptibility mapping. The achieved success and prediction rates for LR were 90 % and 88 %, respectively. However, EBF was not successful in providing reasonable accurate results. The acquired success and prediction rates for EBF were 53 % and 50 %, respectively. Hence, the LR technique can be utilized in landslide hazard studies for land use management and planning.