Sinkhole susceptibility mapping using analytical hierarchical process (AHP) and probabilistic method: a case study of Kuala Lumpur and Ampang Jaya / Mohd Asri Hakim Mohd Rosdi

• Main Author: Mohd Rosdi, Mohd Asri Hakim
• Format: Thesis
• Language: English
• Published: 2022
• Subjects: Sinkholes; Probabilities
• Online Access: https://ir.uitm.edu.my/id/eprint/75383/1/75383.pdf; https://ir.uitm.edu.my/id/eprint/75383/

Since 1968, the increasing numbers of sinkhole disaster have been reported in Kuala Lumpur and the vicinity areas. As the results, it gives a serious threat for human being, assets and structure of the country especially in the capital city. In order to tackle this situation, a Sinkhole Hazard Model (ShM) was produced with integration of GIS environment by applying Analytical Hierarchical Process (AHP) method and probabilistic method to generate a sinkhole susceptibility hazard map for the particular area. There are five consecutive criteria chosen namely Lithology (LT), Soil Types (ST), Land Use (LU), Groundwater Level Decline (GLD) and Proximity to Groundwater Wells (PGW). Based on the calculation of AHP weightage, LT and GLD give the highest impact to the development of this disaster which is 0.46 and 0.30 respectively while according to probabilistic calculation, GLD and LU give the greatest effect of the sinkhole development which is 4.74 and 3.12 respectively. A sinkhole susceptibility hazard zones for both methods was classified into five classes namely none, low, medium, high and very high. The results obtained were validated with the previous inventory data of 33 sinkholes. From the analysis, it shows that the accuracy assessment of the model indicates 45.45% and 15.16% for AHP method of the sinkhole development fall within high and very high hazard regions respectively. For probabilistic method, the accuracy assessment of the model indicates 36.37% and 39.39% of sinkhole formation fall within high and very high hazard zones respectively. Based on this final outcome, it clearly shows that the integration of GIS, AHP and probabilistic approach is useful to predict natural disaster such as sinkhole hazard development.