The accelerating economic growth has assisted rapid urban development and expansion of construction sites into the landslide-vulnerable zones in Malaysia. Thus landslide susceptibility mapping has now become an important part of project designing work for landslide zone areas. There are several models that are used for susceptibility mapping, especially in the peninsular region. Every model has its own set of selected computing variables and characteristics to generate a map. To date, there is no single method applicable to assess and predict all landslides, as there are variations of geomorphological conditions set by the nature. This paper has reviewed recent research publications on landslide susceptibility mapping in Malaysia. Results show that there are 16 models that are being used to describe landslide risk mapping and among them, the Fuzzy model, Neural Network combined with Fuzzy logic, evidential belief function model, probability analysis (e.g. Weights-of-Evidence, and regression), and Support Vector Machine models are proved to be effective even in the areas with limited information. It is observed that most of the susceptible models use curvature, slope angles, distance from drainage, altitude, slope gradient, road distance, aspects as variable factors, and prolonged rainfall as the prime triggering factors. Furthermore, it is observed that the maximum number of research has been conducted in Cameron Highlands (28%) and Penang (20%), because of their high frequencies of landslide occurring and vulnerabilities. Sabah and Sarawak are covered by a negligible number of susceptibility research. Further, a comparison study between the selected models presents the limitations of each model and their benefits and some suggestions are also made based on the authors recommendations works.
Landslide is now a considerable geological hazard (Nhu et al., 2020b; Selamat et al., 2022) that causes dam-ages to the built-environment and fatalities, and poten-tial to cause a catastrophic disaster (Tien Bui et al., 2018) when it occurs in some highly developed region such as Peninsular Malaysia (Nhu et al., 2020a). Ac-cording to the report presented in EM-DAT, (2015) landslide and mass movement is 10.4% and associate mortality is 24.3% among all the disasters in this area. However, 90% of landslide damages can be avoided if the prediction is made before the occurrence (Brabb, 1993). Hence, landslide susceptibility mapping is very crucial for the landslide vulnerable areas (Al-Najjar et al., 2021; Selamat et al., 2022). Susceptibility mapping is an identification of susceptible zone which possesses some inherent characteristics potential of landslide (Tien Bui et al., 2018; Nhu et al., 2020b; Hashim et al., 2018; Shahabi et al., 2015). For this reason, this sus- ceptibility calculation is very common practice in sup-porting planning and development projects (Giraud & Shaw, 2007). To reduce the landslide risk, different organizations such as Malaysian Centre for Remote Sen- sing (MACRES), Public Work Department (PWD), Malaysian Meteorological Service (MMS), Drainage and Irrigation Department, Social Welfare Department, Special Malaysia Disaster Assistance and Rescue Team, National Security Council, Civil Defense Department, national and international corporation, the local Autho-rities, Non - Governmental Organization (NGO) are wor-king by operating and functioning in landslide moni-toring, predicting, forecasting or warning by the map of landslide susceptibility. For this reason, the avail-able database from the published articles needs to be reviewed to recognize the development points in land-slide susceptibility mapping methodology in Malaysia.
A lot of works have been conducted on the area of susceptibility mapping and those relevant literatures are used to understand current works. From the survey of literature on susceptibility mapping on Malaysian Landslides that found 16 distinct models that are being used to describe how the risk mapped and computing techniques namely, 1-Neuro-fuzzy model, 2- Eviden-tial Believe Function Model, 3- Decision Tree 4-Suppert Vector model, 5-Adaptive Neuro-Fuzzy Inter-ference system 6-Probablistic based frequency ratio model, 7-Advanced fuzzy logic model 8-artificial neural Network model 9-Back propagation artificial neural Network model 10-Multivariate logistic regre-ssion model 11-Geographic Information system and Remote sensing 12-Digital elevation model 13-Bino-mial Logistic regression model 14-TRIGRS model 15- Spatial Based Statistical Model 16-Weighted Spatial probability Modeling. It is observed that susceptible models are being done using curvature, slope gradient, slope angles, distance from drainage, & distance from road, precipitation distribution, and distance from faults, soil type, aspects, altitude, surface roughness and land cover as variable factors. Instead, prolonged rainfall as the prime triggering factors is considered by most of the research (Nhu et al., 2020). The susceptibility map-ping covered all the part of Cameron Highlands, Penang, Selangor, Kuala Lumpur, Hulu Kelang, Kelang Valley and Phang because of their higher number of experience and vulnerabilities. This study reviews firstly the recent advancement in mapping methods for both qualitative and quantitative models, the factor used in the model as a triggering and transgenic factor, pur-pose of the map development and distribution of papers. Secondly, it summarizes the results on number of vari-ables, their accuracy percentages and area coverage.
Further, a list of benefit and limitations of better per-forming models has been presented. To the best of our knowledge, this is the first review on landslide suscep-tibility mapping on Malaysian perspective. Finally, a direction on future research by the authors is provided. The rest of the paper is organized orderly as follows: chapter-2 provides landslide susceptibility mapping approaches, chapter-3 methodology, chapter- 4 land-slide susceptibility mapping in Malaysia, chapter-5 distri-bution of paper based on the locality, chapter- 6 com-parative analysis of techniques and chapter- 7 conclu-sions followed by a future recommendation.
Landside Susceptibility Mapping Approaches
Landslide susceptibility mapping can be done in diver-sified ways (Selamat et al., 2022; Tien Bui et al., 2018) dependent on the particular landscape, use and financial resource to provide the work. In generally, a landslide susceptibility mapping can be either by direct or indirect method. In direct mapping a geo-morpho-logist, based on his/her wisdom and experience of the topography conditions regulates the degree of suscepti-bility directly (Van-Westen et al., 2003a). This direct method often referred to as distribution approach or qualitative approach, which is simply obtained through field survey mapping and historical records well known as landslide inventory (Pardeshi et al., 2013). In indirect mapping, statistical or deterministic models are used to predict the landslide prone areas based on the inform-ation obtained from the interrelation among the land-slide conditioning factors (Van-Westen et al., 2003b). This quantitative method can be broadly categorized in to three categories namely deterministic analysis, statis-tical method and artificial intelligence technique (Sonam et al., 2015). The Malaysian landslide susceptibility mapping research grouped in to the following cate-gories by (Kanungo et al., 2009). The advantage of this classification is easy stated as a taxonomic develop-ment and separate sequencing of each qualitative and quantitative approach which might helped to sub divide the susceptibility mapping research conducted in Malaysia.
Qualitative Analysis
This method includes a lot of prejudice during prepara-tion, numerous thematic data layers which contributes a landslide occurrence are integrated. This is an early stage assessment of landslide susceptibility mapping when soft computing system and mathematical theory were not in practices. Distribution analysis, geomor-phic analysis and map combination are included in qualitative approaches (Kanungo et al., 2009).
Distribution Analysis
Distribution analysis which is known as landslide inven-tory shows the distribution of current landslides map-ped from aerial photographs field survey and historical data on landslide occurrences. This map is used as a basis of other landslide susceptibility mapping of spatial distribution of other similar circumstances (Kanungo et al., 2009).
Geomorphic Analysis
Geomorphological analysis is a direct approach in which a detailed field recording and field survey are required to produce the map. Professional ability based descriptive information is being produced by expert decision. This mapping varies expert to expert with the varying of experience and subjects that are considered (Bhusan et al., 2022; Kanungo et al., 2009).
Map combination
Map combination has been done on the basis of selection of causative factors and preparation of thematic data layer with the assistance of those factors. Commonly these factors include lithology, lineament, slope, aspect, land use, land cover and drainage. After then giving a weight and rating the factors, integration of data layer can produce the susceptibility map (Kanungo et al., 2009; Yusof et al., 2011) used this approach for early stage planning along the Simpang Pulai to Kg Raja Highway in Malaysia.
Quantitative Analysis
This method includes computing tools to produce the susceptibility map. One of the key purposes of this approach is to lessen the subjectivity or relay on exp-ertise. Quantitative analysis has been developed by sum-marizing a degree of hybridization of Statistical ana-lysis, probabilistic approaches and distribution - free app-roaches. This is a broadly used approach due to its available computing packages and even if available infor-mation is limited, it can produce a map. It may be a power-ful tool if combined with information obtained by any statistical or mathematical analytical approach (Geolo-gical Survey of Ireland, 2011).
Statistical Analysis
This approach ensures the prediction of future land-slide using statistics of variables. The review of the recent literature has identified 15 commonly used statis-tical or mathematical techniques for susceptibility map development even though there are variations in algor-ithms implemented. This can be further sub-categori-zed as bivariate statistical analysis and multivariate statistical analysis (Table 1).
Bivariate Statistical Analysis
In bivariate statistical analysis usually used techniques are weight of evidence, evidence value and frequency ratio. Pradhan et al. using this approach to produce pre-diction map in Ulu kelang, Klang valley, and Cameron Highlands respectively (Pradhan et al., 2012; Pradhan and Lee, 2010; Pradham, 2010). Lee has also used the same model for Selangor area (Lee and Pradhan, 2007).
Multivariate Statistical Analysis
In multivariate statistical analysis, normally used tech-niques are logistic regression, discriminant analysis and cluster analysis. Pradhan and Lee used this approach combined with other models to produce the landslide susceptibility map (Pradhan and Lee, 2010; Lee and Pradhan, 2007; Pradhan et al., 2008). Some research articles (Zulhaidi et al., 2010; Moussa et al., 2010; Saadatkhan et al., 2014; Pradhan and Youseef, 2010; Razak et al., 2013) are commonly used GIS and Remote Sensing information by using TRIGRS model and spatial-based statistical model to produce hazards map for Malaysia.
Probabilistic Approach
The probabilistic approach relates the spatial distri-bution of landslide in relation to different causative factors in a probabilistic framework. In probabilistic approach, mostly used techniques are probability models, weight of evidence methods certainty factor method under favorability mapping model and evidential believe functional model. Althuwaynee et al. (2012) used evid-ential believe functional model. Pradhan used this app-roach combined with airborne LiDAR derived para-meters and evidential believe functional model (Prad-han et al., 2010; Pradhan et al., 2014).
Elmahdy & Mostafa practiced weighted spatial prob-ability modeling with the digital elevation model to produce susceptibility mapping in Kualalumpur (El-mahdy and Mostafa, 2013). Jebur et al. utilized novel ensemble evidential believes model united with support vector machine model to produce the map (Jebur et al., 2015).
Distribution-free/ soft computing Techniques
To reduce the complexity of landslide phenomena in prediction, application of various soft computing tech-niques has been used in recent times. Actually, the trend of practicing such techniques has been largely used. However, success of these approaches is greater than any other conventional techniques.
In this case fuzzy set based approach and artificial neural network have been implemented to map the susceptibility of landslide. In recent times, fuzzy set and neural network combined to get more precious data to predict future landslide.
Fuzzy set based & Artificial Neural Network based Approach
These two (2) approaches are found as recent trend of susceptibility mapping in Malaysian Landslide. Most of the researchs are conducted by (Pradhan et al., 2010; Pradhan, 2013; Oh and Pradhan, 2011; Pradhan, 2010a; Pradhan and Lee, 2010; Pradhan, 2010b; Pradhan and Buchroithhner, 2010; Selamat et al., 2022) also use Artificial Neural Network approach for the assessment of landslide susceptibility in Langat river basin, Selan-gor, Malaysia.
Remote Sensing and Machine Learning Approach
Machine learning algorithm in combination with remote sensing techniques is the most protruding and newly used tools for landslide susceptibility mapping in Mala-ysia. Nhu et al. uses this method for mapping the landslide susceptibility in the Cameron Highland, Malaysia (Nhu et al., 2020a; Nhu et al., 2020b). Tien Bui et al. also used remote sensing techniques for map-ping the landslide susceptibility in the Cameron High-land by Support Vector Machine (SVM) and Entropy Models (Tien Bui et al., 2018).
Other than these approaches physics based model or Slop stability Analysis are also used for special cases. This approach has very limited use because of its capa-bility in slope stability analyses. The model has been used to evaluate the stability of peat in Penang Island (Oh and Pradhan, 2011a; Pradhan et al., 2010; and Lee and Pradhan, 2007).
A systematic reviews on landslide susceptibility map-ping focusing on Malysian experience was conducted by a searching the web of Science (WoS) publications database (apps.webofknowledge.com) in September 2015 to complete the study. The multi-disciplinary data-base was used to identify different models used in the landslide susceptibility mapping; literatures within the 10 years time frame between 2005 and 2015 were surveyed.
The following term was used as key words for sear-ching the articles. “Landslide Susceptibility Mapping, Malaysia.” The yield was again filtered by following the titles possessing the key word Susceptibility Map-ping.
Landslide Susceptibility Mapping
Twenty three susceptibility mapping analysis have been organized, reviewed, analyzed and presented in the Table 1. The table contains authors name, using models, number of factors, region under study, no. of events so far consider in the investigation, study loca-tion accuracy percentage, application and uses of the models and references. After the tabulation the infor-mation of the study has analyzed, the results are discussed in the next sessions.
Table 1: Feature presented among the techniques used in the landslide susceptibility mapping system.
Table 2: Contribution of authors for susceptibility map- ping.
From Table 2, Pradhan et al. has contributed mostly in the susceptibility mapping (56.6 %). And the rest of the authors contributed as 4.34 % of each.
Table 3: Model types used in the review.
From the Table 3 Other than GIS and Remote sensing methods the Malaysian landslide susceptibility map-ping techniques used mostly quantitative approach and very less of qualitative approach. This information indicated that, there is a good chance to combine with quantitative approach with qualitative approach to get a good model for future prediction. From the Fig. 2, we have found that, Fuzzy logic and neural network both of the model are used under 5 studies of each as single and combined mode.
Fig. 2: Model used in the study how many times.
Whereas, evidential believe function model have been used by 4 authors. Probability analysis and Support vector machine model have 2 users for each under the current study. So, a message received from here that fuzzy logic and neural network based model is popular and most used in Malaysian landslide susceptibility analysis. On the other hand, it can be specified that combining the fuzzy logic and Neural network model with others like evidential believe functional model, probability analysis and support vector machine can have a better predictable capacity.
Table 4: Number of factors used in the study.
Here in Table 4 have discussed the frequency of fac-tors is used by this study. The following factors are very commonly used by the susceptible mapping rese-archers like curvature (8), slope angle (7), distance from drainage (6), altitude (5), slope gradient (4), distance from road (4), aspects (4), and distance from faults (3), difference vegetation index (3), soil type (3) and land cover (2). Which indicates a chronological importance of each factor for susceptibility mapping. So, shows a clear idea about selecting the factors for future map-ping.
Table 5: Average accuracy of the model.
Table 5 discuses about the average accuracy percent-ages of the each model in the review. It pointing to mixing capacity and clearly have a preliminary idea about the mixing performance of each model. Many susceptibility mapping works have covered (Fig. 3) Cameron Highlands (28%) and Penang (20%) hilly areas followed by Selangor (16%), Kuala Lumpur (12%) and Hulu Kelang (12%).
Fig. 3: Distribution of papers on the basis of susceptibility map.
But few works has been completed in the part of Kelang Valley (8%) and Pahang (4%). Considering the other two states of Malaysia Sabah and Sarawak, the study matched no research regarding susceptibility map-ping analysis for landslides.
Comparative Analysis of Techniques
The purpose of the benefit-limitation (Table 6) is to visualize the combining performance with each other model. Because merging one or more model to produce susceptible map can easily avoid the present limit-ations in future research.
Table 6: Benefit-Limitation visualizing table.
Susceptibility mapping has made contributions to natio-nal acts and policies for preparation of hillside area guide line, national building code, recommended terrain hazard zonation map for landslide risk reduction. In fact, Mal-aysia is one of the signatory nations who committed it to reducing the land slide risk by taking structural measures approaches. From the literature survey, it can be determined that no single method may be termed as the most suited best to landslide susceptible mapping.
A future direction collected from the authors
Actions towards acquiring high temporal resolution with high degree of confidence, the Evidential Belief Functional model can provide planners with a quick yet comprehensive assessment of future failure and- a guide for future zoning issues (Althuwaynee et al., 2012). More landslide data are needed and more case studies should be conducted for covering the whole areas (Oh and Pradhan, 2011). In order to obtain higher prediction accuracy, it is recommended to use a sui-table set of landslide data (Pradhan and Lee, 2010). It is necessary to investigate the landslide causative para-meters and their direct relationship with the triggering factors of future landslides (Pradhan et al., 2010). An assessment of available factors relevant to the vulner-ability of buildings and other property would result in a valuable risk analysis (Pradhan and Buchroithner, 2010). Every used model should be verified in different geological and environmental settings (Pradhan and Lee, 2010). It would be ideal to develop hybrid model which model will accumulate the beneficial side of each and try to overcome the limitations by it.
The authors appreciate all those who participated in this review work. We are also grateful to the respective Editor and Reviewers for spending their valuable time on our paper. They critically upgraded this research work.
The authors declared no possible conflicts of the interest with respect to the research, authorship and publication of this article.
Academic Editor
Dr. Antonio Russo, Professor, Dept. of Moral Philosophy, Faculty of Humanities, University of Trieste, Friuli-Venezia Giulia, Italy.
Assistant Professor, Dept. of Emergency Management, Faculty of Environmental Science and Disaster Management, Patuakhali Science and Technology University, Dumki, Patuakhali-8602, Bangladesh
Akter A, Parvez A, Rasheduzzaaman M, Hasan MM, and Maksudul. (2022). A review on landslide susceptibility mapping in Malaysia: recent trend and approaches, Asian J. Soc. Sci. Leg. Stud., 4(5), 199-208. https://doi.org/10.34104/ajssls.022.01990208