Comparative study of landslides susceptibility mapping methods: Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN)

• Published in: IOP Conference Series: Earth and Environmental Science
• Main Author: Salleh S.A.; Abd Rahman A.S.A.; Othman A.N.; Wan Mohd W.M.N.
• Format: Conference paper
• Language: English
• Published: Institute of Physics Publishing 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044447070&doi=10.1088%2f1755-1315%2f117%2f1%2f012035&partnerID=40&md5=df9d267cba37616c51cb3dbfead14803

As different approach produces different results, it is crucial to determine the methods that are accurate in order to perform analysis towards the event. This research aim is to compare the Rank Reciprocal (MCDM) and Artificial Neural Network (ANN) analysis techniques in determining susceptible zones of landslide hazard. The study is based on data obtained from various sources such as local authority; Dewan Bandaraya Kuala Lumpur (DBKL), Jabatan Kerja Raya (JKR) and other agencies. The data were analysed and processed using Arc GIS. The results were compared by quantifying the risk ranking and area differential. It was also compared with the zonation map classified by DBKL. The results suggested that ANN method gives better accuracy compared to MCDM with 18.18% higher accuracy assessment of the MCDM approach. This indicated that ANN provides more reliable results and it is probably due to its ability to learn from the environment thus portraying realistic and accurate result. © Published under licence by IOP Publishing Ltd.