Assessment of urban growth changes in Klang District using Support Vector Machine by different kernel

• Published in: IOP Conference Series: Earth and Environmental Science
• Main Author: Saraf N.M.; Lokman M.F.; Abdul Rasam A.R.; Hashim N.
• Format: Conference paper
• Language: English
• Published: Institute of Physics 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135467797&doi=10.1088%2f1755-1315%2f1051%2f1%2f012023&partnerID=40&md5=8e124ed04f1cddaad40bc5fd051ce456

The growth of urbanization in Klang District was considered to be fast and has increased the concern of policy makers and town planners. This paper assess the changes of urban development in Klang District using Support Vector Machine (SVM) classification by different kernel for the purpose of studying the built up area changes within the year 2017 to 2021. At the initial stage of image processing, Land Use Land Cover (LULC) has been classified based on the use of SVM by different kernel (RBF, Polynomial, Linear, and Sigmoid) which was then reclassify into the built up and non built up after the most accurate kernel has been identified, thus the study was focused on the growth of urbanization. As results, the highest accuracy is RBF Kernel which the LULC that has been classified were 88% in 2017 and 90% in 2021. The RBF Kernel was then used for the classification of built up area and also for the analysis of urban growth. It can be seen that there have been changes for every land use, particularly urban growth by 9.39% (5451.77 Ha). Hence, the pattern of urban sprawl would assist planners and policymakers in planning and managing a better city. © Published under licence by IOP Publishing Ltd.