Key Human Activity Variable for Landslide Prediction in Western Sarawak

NUR HISYAM RAMLI; Siti Noor Linda Taib; Norazzlina M. Sa’don; Raudhah Ahmadi; Imtiyaz Akbar Najar; Dayangku Salma Awang Ismail; Rosmina Ahmad Bustami; Tarmiji Masron; Nazeri Abdul Rahman

doi:10.58915/amci.v15i2.1819

Authors

Nur Hisyam Ramli Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Sarawak, MALAYSIA https://orcid.org/0009-0001-2379-5630
Siti Noor Linda Taib Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Sarawak, Malaysia
Norazzlina M. Sa’don Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Sarawak, Malaysia.
Raudhah Ahmadi Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Sarawak, Malaysia.
Imtiyaz Akbar Najar Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Sarawak, Malaysia.
Dayangku Salma Awang Ismail Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Sarawak, Malaysia.
Rosmina Ahmad Bustami UNIMAS Water Centre (UWC), Faculty of Engineering, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
Tarmiji Masron Centre for Spatially Integrated Digital Humanities (CSIDH), Faculty of Social Sciences and Humanities, Universiti Malaysia Sarawak, 94300, Sarawak, Malaysia.
Nazeri Abdul Rahman Department of Chemical Engineering and Energy Sustainability, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Sarawak, Malaysia.

DOI:

https://doi.org/10.58915/amci.v15i2.1819

Keywords:

Landslide, Machine Learning, Neural Network, Sarawak, Spatial

Abstract

Landslide is a common form of natural disaster in a tropical country such as Malaysia, and its presence is mostly concentrated during the wet season. Human activities have been known to influence landslides as development often causes displacement of the original slope. Western Sarawak is a region where development is currently taking place at a steady rate, with the highest population density in Sarawak settlement is expected to increase in the coming years, increasing the impact of human activity on landslide occurrences in the region. However, the most prevalent representation of human activity in the area has not been discussed. Through a Machine Learning approach of an Artificial Neural Network, the commonly used human activity is represented by Distance from Road (DFR), Normalised Density Vegetation Index (NDVI), and Land Use and Land Cover (LULC) for landslide predictions. It was determined through Garson’s algorithm that the most prevalent human activity variable in the region is best represented by LULC, followed by NDVI, and DFR with a weightage of 0.235, 0.063, and 0.042 respectively.