PERTANIKA JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

Landslide is one of the natural disasters that commonly occurs in terrestrial environments with slopes throughout the world. Located among countries with tropical climates, the hot and humid conditions expose Kuala Lumpur, Malaysia, to the risk of landslides. This paper aims to delineate the influencing physical characteristics of landslide occurrences in Kuala Lumpur. In this study, a 100 landslides historical data set and eight landslide factors were obtained from proper field validation and maps provided by those concerned in the government, such as distance to roads, distance to streams, elevation, slope angle, curvature, slope aspect, land use, and lithology. These factors were processed using GIS as geospatial analysis provides a useful tool for planning, disaster management, and hazard mitigation. By using ArcMap 10.8.2, a GIS software, different spatial analyses in which maps for each physical factor were layered with landslide events distribution. The weights for each factor were determined using the ANN approach resulting in the slope angle having the highest relative importance with a 100.0% value. In comparison, 8.3% represents the slope aspect as the most insignificant factor out of the eight selected characteristics for this study area. Therefore, a proper perspective and a thorough understanding of the certain slope condition have to be established for future mitigation action to support the agenda of SDG 15.

• Alnaimat, A., Choy, L. K., & Jaafar, M. (2017). An assessment of current practices on landslides risk management: A Case of Kuala Lumpur Territory. Geografia, 13(2), 1-12.

• Althuwaynee, O. F., & Pradhan, B. (2017). Semi-quantitative landslide risk assessment using GIS-based exposure analysis in Kuala Lumpur City. Geomatics, Natural Hazards and Risk, 8(2), 706-732. https://doi.org/10.1080/19475705.2016.1255670

• Capitani, M., Ribolini, A., & Bini, M. (2013). The slope aspect: A predisposing factor for landsliding? Comptes Rendus Geoscience, 345(11-12), 427-438. https://doi.org/10.1016/j.crte.2013.11.002

• Department of Irrigation and Drainage. (2018). Laporan banjir tahunan 2017/2018 [Annual flood report 2017/2018]. Ministry of Natural Resources, Environment and Climate Change. http://h2o.water.gov.my/man_hp1/LBT2017-2018.pdf

• Department of Statistics Malaysia. (2020). Lateststatistics 2019. Prime Minister’s Department. https://www.mycensus.gov.my/

• Department of Statistics Malaysia. (2022). Malaysia current population estimates, 2021. Prime Minister’s Department. https://www.dosm.gov.my/v1/index.php?r=column/cthemeByCat&cat=155&bul_id=ZjJOSnpJR21sQWVUcUp6ODRudm5JZz09&menu_id=L0pheU43NWJwRWVSZklWdzQ4TlhUUT09#:~:text=Kuala%20Lumpur%20had%20the%20highest,1%2C691%20people)%20per%20square%20kilometre.

• Dhianaufal, D., Kristyanto, T. H. W., Indra, T. L., & Syahputra, R. (2018). Fuzzy logic method for landslide susceptibility mapping in volcanic sediment area in Western Bogor. AIP Conference Proceedings 2023, Article 020190. https://doi.org/10.1063/1.5064187

• Erener, A., & Duzgun, H. S. B. (2010). Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides, 7, 55-68. https://doi.org/10.1007/s10346-009-0188-x

• Gue, S. S., & Tan, Y. C. (2003, August 19-20). The engineering aspects of hill-site development. [Paper presentation]. Hillside Development–Issues and challenges, Kuala Lumpur, Malaysia.

• Gue, S. S., & Wong, S. Y. (2009, August 26-27). Slope engineering design and construction practice in Malaysia. [Paper presentation]. CIE-IEM Joint Seminar on Geotechnical Engineering, Yilan, Taiwan.

• Hong, L. J., & Hong, K. A. (2016). Flood forecasting for Klang River at Kuala Lumpur using artificial neural networks. International Journal of Hybrid Information Technology, 9(3), 39-60. https://doi.org/10.14257/ijhit.2016.9.3.05

• Huang, H. F., Yi, W., Yi, Q. L., & Zhang, G. D. (2012). Analysis of landslide surface deformation using geographically weighted regression. Advanced Materials Research, 594-597, 2406-2409. https://doi.org/10.4028/www.scientific.net/amr.594-597.2406

• Ibrahim, M. B., Mustaffa, Z., Balogun, A. L., & Sati, H. H. I. (2022). Landslide risk analysis using machine learning principles: A case study of Bukit Antarabangsa landslide incidence. Journal of Hunan University Natural Sciences, 49(5), 112-126. https://doi.org/10.55463/issn.1674-2974.49.5.13

• Ismail, N. I., & Yaacob, W. Z. W. (2018). An investigation of landslides in Bukit Aman and Puncak Setiawangsa, Kuala Lumpur, Malaysia. AIP Conference Proceedings, 1940, Article 020031. https://doi.org/10.1063/1.5027946

• Jaafari, A., Najafi, A., Pourghasemi, H. R., Rezaeian, J., & Sattarian, A. (2014). GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian Forest, Northern Iran. International Journal of Environment Science and Technology, 11, 909-926. https://doi.org/10.1007/s13762-013-0464-0

• Jebur, M. N., Pradhan, B., & Tehrany, M. S. (2014). Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sensing Environment, 152, 150-165. https://doi.org/10.1016/j.rse.2014.05.013

• Kyriou, A., Nikolakopoulos, K., Koukouvelas, I., & Lampropoulou, P. (2021). Repeated UAV campaigns, GNSS measurements, GIS, and petrographic analyses for landslide mapping and monitoring. Minerals, 11(3), Article 300. https://doi.org/10.3390/min11030300

• Lee, S., RyuRyu, J. H., Min, K, & WonWon, J. S. (2003). Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes and Landforms, 28, 1361-1376. https://doi.org/10.1002/esp.593

• Lee, S., & Thalib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47, 982-990. https://doi.org/10.1007/s00254-005-1228-z

• Mahmud, A. R., Awad, A., & Billa, R. (2013). Landslide susceptibility mapping using averaged weightage score and GIS: A case study at Kuala. Pertanika Journal of Science and Technology, 21(2), 473-486.

• Majid, N. A., & Ibrahim, W. M. M. W. (2015, August 19-20). GIS in studying slope failure in Penang: Challenges and potential [Paper presentation]. Proceedings of International Conference on Development and Socio Spatial Inequalities, Penang, Malaysia.

• Majid, N. A., Rainis, R., & Ibrahim, W. M. M. W. (2017). Pemodelan ruangan pelbagai jenis kegagalan cerun di Pulau Pinang menggunakan kaedah nisbah kekerapan [Spatial modeling of various slope failures in Pulau Pinang using frequency ratio method]. Geografi, 5(2), 13-26.

• Majid, N. A., Rainis, R., & Ibrahim, W. M. M. W. (2018). Pemodelan ruangan pelbagai jenis kegagalan cerun menggunakan rangkaian saraf buatan (ANN) di Pulau Pinang, Malaysia [Spatial modeling various types of slope failure using artificial neural network (ANN) in Pulau Pinang, Malaysia]. Jurnal Teknologi, 80(4), 135-146.

• Majid, N. A., & Rainis, R. (2019). Application of geographical information systems (GIS) and discriminant analysis in modelling slope failure incidence in Pulau Pinang, Malaysia. Sains Malaysiana, 48(7), 1367-1381. http://dx.doi.org/10.17576/jsm-2019-4807-06

• Mandal, S., & Mondal, S. (2019). Artificial neural network (ANN) model and landslide susceptibility. In Statistical Approaches for Landslide Susceptibility Assessment and Prediction 4 (pp. 123-133). Springer. https://doi.org/10.1007/978-3-319-93897-4_5-

• Mersha, T., & Meten, M. (2020). GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenvironmental Disasters, 7, Article 20. https://doi.org/10.1186/s40677-020-00155-x

• NASA. (2020). Global landslide catalog: Rainfall-triggered landslide events around the world. Data World. https://data.world/nasa/global-landslide-catalog

• Naseer, S., Haq, T. U., Khan, A., Tanoli, J. I., Khan, N. G., Qaiser, F. R., & Shah, S. T. H. (2021). GIS-based spatial landslide distribution analysis of district Neelum, AJ&K, Pakistan. Nat Hazards, 106, 965-989. https://doi.org/10.1007/s11069-021-04502-5

• Palansamy, Y. (2021). DBKL: Seri Duta 1 condo unsafe after partial landslide, situation being monitored. Malay Mail. https://www.malaymail.com/news/malaysia/2021/12/27/dbkl-seri-duta-1-condo-unsafe-after-partial-landslide-situation-being-monit/2031312

• Paudel, U., Oguchi, T., & Hayakawa, Y. (2016). Multi-resolution landslide susceptibility analysis using a DEM and random forest. International Journal of Geosciences, 7(5), 1-18. https://doi.org/10.4236/ijg.2016.75056

• Pawluszek, K., & Borkowski, A. (2017). Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland. Natural Hazards, 86, 919-952. https://doi.org/10.1007/s11069-016-2725-y

• Pradhan, B., & Lee, S. (2009). Landslide risk analysis using artificial neural network model focussing on different training sites. International Journal of Physical Sciences, 4(1), 001-015.

• Psomiadis, E., Papazachariou, A., Soulis, K. X., Alexiou, D. S., & Charalampopoulos, I. (2020). Landslide mapping and susceptibility assessment using geospatial analysis and earth observation data. Land, 9(5), Article 133. https://doi.org/10.3390/LAND9050133

• Rahim, A. F. A., Rafek, A. G. M., Serasa, A. S., Abdullah, R. A., Rahim, A., Sami, W. H. W., Foong, S. W., Abdurrahman, M., Lee, K. E., Nguyen, X. H., Tran, V. X., & Goh, T. L. (2022). Application of a comprehensive rock slope stability assessment approach for selected Malaysian granitic rock slopes. Sains Malaysiana, 51(2), 421-436. http://doi.org/10.17576/jsm-2022-5102-08

• Rahman, A. A. A., Majid, N. A., & Selamat, S. N. (2020). A comprehensive deriving the factors of landslide happened in Malaysia. International Journal on Emerging Technologies, 11(5), 310-314.

• Rahmati, O., Haghizadeh, A., Pourghasemi, H. R., & Noormohamadi, F. (2016). Gully erosion susceptibility mapping: The role of GIS-based bivariate statistical models and their comparison. Natural Hazards, 82, 1231-1258. https://doi.org/10.1007/s11069-016-2239-7

• Rasyid, A. R., Bhandary, N. P., & Yatabe, R. (2016). Performance of frequency ratio and logistic regression model in creating GIS-based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenvironmental Disasters, 3(19), 1-16. https://doi.org/10.1186/s40677-016-0053-x

• Ritchie, H., Rosado, P., & Roser, M. 2014. Natural disasters. Our World in Data. https://ourworldindata.org/natural-disasters

• Saadatkhah, N., Kassim, A., & Lee, M. L. (2014). Spatial patterns of precipitation, altitude and monsoon directions in Hulu Kelang area, Malaysia. Electronic Journal of Geotechnical Engineering, 19(C), 521-534.

• Sanusi, M. S. M., Ramli, A. T., Hassan, W. M. S. W., Lee, M. H., Izham, A., Said, M. N., Wagiran, H., & Heryanshah, A. (2017). Assessment of impact of urbanisation on background radiation exposure and human health risk estimation in Kuala Lumpur, Malaysia. Environment International, 104, 91-101. https://doi.org/10.1016/j.envint.2017.01.009

• Selamat, S. N., Majid, N. A., Taha, M. R., & Osman, A. (2022). Landslide susceptibility model using artificial neural network (ANN) approach in Langat River Basin, Selangor, Malaysia. Land, 11(6), Article 833. https://doi.org/10.3390/land11060833

• Shahabi, H., & Hashim, M. (2015). Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Scientific Reports, 5, Article 9899. https://doi.org/10.1038/srep09899

• Sharir, K., Roslee, R., Lee, K. E., & Simon, N. (2017). Landslide factors and susceptibility mapping on natural and artificial slopes in Kundasang, Sabah. Sains Malaysiana, 46(9), 1531-1540. http://dx.doi.org/10.17576/jsm-2017-4609-23

• Silalahi, F. E. S., Pamela, Arifianti, Y., & Hidayat, F. (2019). Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geoscience Letters, 6(10), 1-17. https://doi.org/10.1186/s40562-019-0140-4

• Sim, L. L., Adrian C., & Trisha N. (2018, December 4). Malaysia among countries especially prone to landslides. The Star. https://www.thestar.com.my/news/nation/2018/12/04/msia-ranks-highly-for-landslides-country-experienced-185-occurrences-annually-in-past-10-years#:~:text=Based%20on%20Nasa’s%20GLC%20website,)%20and%20Selangor%20(eight).

• Simon, N., De Roiste, M., Crozier, M., & Rafek, A. G. (2017). Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps. Sains Malaysiana, 46(1), 27-34. https://doi.org/10.17576/jsm-2017-4601-04

• Skilodimou, H. D., Bathrellos, G. D., Koskeridou, E., Soukis, K., & Rozos, D. (2018). Physical and anthropogenic factors related to landslide activity in the Northern Peloponnese, Greece. Land, 7(3), Article 85. https://doi.org/10.3390/land7030085

• Song, Z. C., Li, X., Lizárraga, J. J., Zhao, L., & Buscarnera, G. (2020). Spatially distributed landslide triggering analyses accounting for coupled infiltration and volume change. Landslides, 17, 2811-2824. https://doi.org/10.1007/s10346-020-01451-1

• The Start. (2022, January 6). 12 more landslide incidents reported, says NADMA. The Star. https://www.thestar.com.my/news/nation/2022/01/06/12-more-landslide-incidents-reported-says-nadma

• Wen, B. P., & Jiang, X. Z. (2016). Effect of gravel content on creep behavior of clayey soil at residual state: Implication for its role in slow-moving landslides. Landslides, 14, 559-576. https://doi.org/10.1007/s10346-016-0709-3

• Yuri, G., & Andrii, V. (2021). Implications of slope aspect for landslide risk assessment: A case study of Hurricane Maria in Puerto Rico in 2017. Geomorphology, 391, Article 107874. https://doi.org/10.1016/j.geomorph.2021.107874

• Yusoff, Z. M., Raju, G., & Nahazanan, H. (2016). Static and dynamic behaviour of Kuala Lumpur limestone. Malaysian Journal of Civil Engineering, 28(1), 18-25.

• Zhang, S., Bai, L., Li, Y., Li, W. L., & Xie, M. (2022). Learning models in landslide susceptibility mapping: A case study in Wenchuan County. Frontier in Environment Science, 10, Article 886841. https://doi.org/10.3389/fenvs.2022.886841