Journal Paper Format

International Journal of Advanced Science and Technology Vol. 28, No. 18, (2019), pp. 01-26

Landslide Occurrences in Malaysia Based on Soil Series and Lithology Factors

Mohd Sofiyan Sulaiman1*, Amir Nazaruddin2, Noorbaya Mohd Salleh3, Roslan Zainal Abidin4, Nirwani Devi Miniandi5 and Abdul Hafidz Yusoff6 1,5Faculty of Ocean Engineering Technology and Informatics, University Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu, Malaysia 2Faculty of Civil Engineering, Universiti Teknologi MARA Cawangan Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia 3Infrastructure University Kuala Lumpur, 43000 Kajang, Selangor, Malaysia 4 Nilai University, 71800 Nilai, Negeri Sembilan, Malaysia 6Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Kampus, Locked Bag No. 100, 17600 Jeli, Kelantan, Malaysia

Abstract Past researchers have created various techniques to analyse landslide occurrences. Those techniques utilize qualitative, quantitative or semi-quantitative approaches. Each technique poses advantages and disadvantages depending on the available information, landslide inventory or area of interest. In Malaysia, landslides have become an alarming issue and fatalities are increasing in every event. These fatalities can be reduced if the landslide prone areas are mapped using zonation techniques. The main aim of this research is to produce a hazard risk map using the susceptibility index based on soil series and strata lithology in the state of Selangor, Malaysia. Over the past 20 years, this state has developed rapidly, and many housing schemes have been developed to accommodate high market demand. Moreover, this state has contributed the largest number of landslide tragedies in the past compared to the other states. Susceptibility index based on soil series and strata lithology provides high reliability after validation with past tragedies. Analytical Hierarchal Index (AHP) has been deployed to find the ranking and susceptibility index for those two factors. It was found that urban land soil series and acid intrusive lithology provide higher weightage of landslide susceptibility compared to other series or lithology. Any locations with those series and lithology will pose a critical level of landslide vulnerability. The overlaying of various series and lithology on the state of Selangor map reveals that three provinces, namely Gombak, Petaling and Hulu Langat should be given special attention should future development is to be carried out in these territories.

Keywords: landslide, soil series, stratigraphy, risk, analytical hierarchal index

1. Introduction Past researchers have made four fundamental assumptions pertaining to landslide assessment and occurrence [4], [15], [34]. The assumptions are as such: a) landslides will always occur in the same geological, geomorphological, hydrogeological and climatic conditions as in the past; b) the main conditions that cause landslides are controlled by identifiable physical factors; c) the degree of hazard can be evaluated; d) all types of slope failures can be identified and classified. The first assumption is emphasized in this paper. Those four assumptions have led to the development of qualitative and quantitative landslide hazard assessments by researchers. Zonation approach or landslide susceptibility map has been applied to indicate areas where landslides are likely to occur in the future, by correlating some of the factors that contribute to landslides with historical distributions of slope failures [3]. To do so, a few techniques have been proposed, which can be grouped under two broad categories: quantitative and qualitative. In-depth discussion by

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[4] revealed that qualitative technique has the same approach as geomorphological approach, where the degree of stability is determined by the provision of the needed information. On the other hand, quantitative approach deals with weightage and statistical measures. The latest approaches make full use of the logical-analytical model and neural network technique to assess landslide occurrences. In [22] however provides another discussion on the quantitative and qualitative approach by naming the techniques for each approach. As such, quantitative approaches include fuzzy logic, analytic hierarchy process, analytic network process, logistic regression, multivariate statistical approach and weighted linear combination. The qualitative models are derived from expert views or integration of ranking and weighting. In [3] added a new concept of heuristic approach in qualitative model. The heuristic method uses expert opinions to categorize landslide- prone areas by terms such as “very low,” “low,” “moderate,” “high,” and “very high.” This method is considered useful for producing qualitative landslide susceptibility maps for large areas [7]. Table 1 shows a summary of the available approaches to produce landslide susceptibility zoning that have been practiced worldwide. Landslide susceptibility zoning using lithology as key parameters has been deployed widely by past researchers [3], [6], [10], [17]-[18]. In [6], [10] pointed out that stratigraphy played a key role in the spatial and temporal evolution of pore water pressure during rainfall, and the onset of local instabilities. Thus, stratigraphy is an important parameter in determining landslide susceptibility. In general, factors causing landslides can be grouped into two: external and internal factors. It is widely recognized that geological factors greatly influence the occurrence of landslides, because lithological and structural variations often lead to a difference in strength and permeability of rocks and soils [29]. Among other factors (such as slope angle, slope aspect, distance to roads and faults, land use and land cover), lithology with respect to type, physical and chemical characteristics, and mineralogy play a significant role. There are two reasons for the increasing international interests in landslides: the awareness of the socio-economic significance, and the increasing pressure of development and urbanisation on the environment [4]. As development increases on sloping urban areas, more incidences of deep and shallow landslides have been reported. Over the past decade, there have been dramatic increase in cases of erosion induced landslides in Malaysia. Soil erosion, a natural process that continuously occurs without any symptoms or warning signs, has been identified as a serious issue for decades, and might become even more critical in the future as a result of uncontrolled developments [2].

Table 1. Various Techniques of Landslide Susceptibility Zoning After [3], [28] No. Broad Technique Sub-Technique Sub-Sub Category Technique 1 Qualitative Distribution (inventory) - - approach Heuristic method - - 2 Quantitative Statistical approach Bivariate statistical Weights of analysis evidence model Weighted overlay method Frequency ratio approach Information value method Multivariate statistical Logistic analysis regression model

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Discriminant analysis Multiple regression models conditional analysis Artificial Neural Networks (ANN) Probabilistic Bayesian probability - approach Certainty factor - Favourability function - Multi criteria Analytical Hierarchy - decision making Process Rainfall threshold - model - 3 Semi- GIS-based heuristic Analytical hierarchy - quantitative approaches process (AHP) and Weighted linear - combination models According to the National Slope Master Plan (NSMP) 2009-2023, Selangor and the Federal Territory of Kuala Lumpur have experienced the most landslides since the 1970s, followed by Pahang, Penang and Sabah. Until November 2011, 600 deaths have been recorded since 1961. The highest fatality for a single landslide event was recorded on Dec 26, 1996, involving 302 people in Keningau, Sabah. Economic losses from landslides totalled almost RM 3 billion (S$1.2 billion) from 1961 to 2007 [30]. Having a tropical climate, Malaysia is prone to soil erosion due to its hot and humid condition throughout the year. The average annual rainfall in Malaysia exceeds 2,000 mm, which is above the global average. The highest annual rainfall ever recorded in the history of Malaysia was 5,293 mm [25]. Heavy rainfall can have adverse effects on soil particles because it amplifies the ability of raindrops to detach soil particles. Soil resistance against erosion is termed as soil erodibility, and its value depends on several factors such as soil structure, infiltration level and organic matter content. It is important to measure soil susceptibility to water erosion, as it is an essential parameter needed for soil erosion prediction. In [11] had produced a diagram for the determination of soil erodibility factor (K). Obtaining the soil erodibility factor (K) is crucial for the calculation of soil loss and sediment yield computation. The K factor makes full use of the soil percentage (% sand, % silt, % clay) and soil type. However, soil erodibility formula (K) is a continuous type of formula and is not meant for predicting the probability of landslide occurrence. In [1] have made the efforts of creating a binary approach by converting the soil erodibility into the probability of landslide occurrence (known as “ROM” scale). Many studies have benefited from this “ROM” scale. As such, in [2] created the river bank erosion index using the percentage of occupation for sand, silt and clay towards finding the erosion risk level. However, there is no evidence of forecasting the landslide susceptibility using the soil series and strata lithology information. This gap of knowledge provides a new avenue for landslide study in Malaysia. Although there are a lot of factors influenced landslide occurrences, the main focus for this paper is to investigate the influence of soil series and lithology on landslide occurrence. Rainfall erosivity and soil composition will be used to verify the outcomes of susceptibility index. Thus, the main objective of this research is to develop an index of landslide susceptibility by fitting soil series and stratigraphic types in Peninsular Malaysia. A combination of analytical hierarchy process (AHP) and heuristic method are used to develop an index of landslide susceptibility. Worked example of computations are shown to illustrate the viability of AHP technique to make multiple decision making. The application of this index is shown by producing a hazard zoning map using GIS software

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for the Selangor state in Malaysia. This index may benefit the policy makers or authorities in making early decisions on the landslide prone locations in Peninsular Malaysia.

2. Methodology

2.1. Soil Series and Stratigraphic Classification in Malaysia Soil is a natural body with unique morphologies and different parent material due to the interactions of different climates, living matters, parent materials and age of landforms. The morphology of each soil, as expressed by a vertical section through the differing horizons, reflects the combined effects of the particular set of genetic factors responsible for its development [33]. In [12] pointed out that the upper boundary of soil is the boundary between soil and air, shallow water, live plants, or plant materials, while the lower boundary consists of the horizons near the earth’s surface that is arbitrarily set at 200 cm for classification purpose. The lower boundary is difficult to discern because of alteration due to climate, biological activities and living organisms over time. These two boundaries differentiate the soil series classification and soil stratigraphy classification for communication purpose. To classify the complex soil properties, USDA has proposed the taxonomy level for soil classification. The main objective of soil taxonomy is to establish hierarchies of class that permits relationship among soil and characteristics that differentiate them [33]. Besides, this taxonomy can provide a means of communication among the discipline of soil science. In [27] described the history of soil taxonomy classification in Malaysia. A soil survey was conducted in the later part of the nineteenth century in Malaysia to accommodate agricultural and development planning in Malaysia. Then, a systematic soil survey was carried out in the middle of the twentieth century, and a systematic reconnaissance soil survey of Peninsular Malaysia was completed in 1955. Malaysia utilizes the USDA soil taxonomy for classification purpose. The parent material, time, climate, organisms and topography are considered the five factors of soil formation, and these five factors determine the series of soil in Malaysia. Based on the USDA classification, there are 6 categories in soil taxonomy namely order, suborder, great group, subgroup, family, and series. The series is the lowest category in this system. In the United States alone, more than 19,000 series have been recognized [33], and over 260 series have been identified in Peninsular Malaysia [24]. Stratigraphy can be considered as the relationship between rocks and time. The geologist is concerned with the observation, description and interpretation of direct and tangible evidence in rocks to determine the history of the Earth. Stratigraphy provides the temporal and spatial framework for the origin of the Earth. In [26] pointed out that the relative ages of rocks can be determined by simple stratigraphic relationship, the presence of fossils and the decaying process of radioactive elements that allow the classification of rock units. Stratigraphy can also be defined as the nomenclature for rock units of all ages. Peninsular Malaysia forms part of the Sundaland which is the South-East Asian part of the Eurasian Plate. The peninsular is elongated in shape, generally trending in the NNW-SSE direction, parallel to the major structural features. Based on the 9th edition of geological map of Peninsular Malaysia, the rock units range from Cambrian to Quaternary in age. Depositional basins are unstable, and major breaks are apparent within and in between the Palaeozoic, Mesozoic and Cenozoic rocks. Stratigraphically, the peninsular can be subdivided into three belts namely the Western Belt, Central Belt and Eastern Belt. The belts are trending almost parallel with the elongated orientation of the peninsular [23]. The lithology for peninsular can be grouped into four categories, namely unconsolidated deposits, sedimentary and metamorphic rocks, extrusive rocks and intrusive rocks.

2.2. Landslide Inventory: Case Study for Selangor State A case study has been conducted for the Selangor state in Peninsular Malaysia. Selangor state consists of nine provinces namely Gombak, Klang, Kuala Langat, Kuala

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Selangor, Petaling, Sabak Bernam, Sepang, Ulu Langat and Ulu Selangor. Generally, geology condition is differed even though it is located in the same state. Clay deposits are noticeable at the depth of 1 to 25 m below ground surface while alluvium which represent clay, silt, sand and gravel deposit can be seen through the depth of 25 to 90 m [29]. Almost all the landslides at these areas occurred on cut slopes or embankments alongside roads and highways in mountainous areas. A few landslides have occurred near the residential areas as well. In [29] concluded that tropical rainfall and flash floods are the main catalyst that triggered landslides in Malaysia. These heavy downpours will cause rock surface failure along fracture, joint, and cleavage planes. In addition, geomorphological settings of Malaysia are quite stable but deforestation, weathering and erosion of the covered soil masses causing serious threat to slopes. 14 cases of landslide have been recorded in the Selangor state alone, the highest number of major landslide throughout the Peninsular Malaysia. Figure 1 shows the superimposed layer of landslide locations on the Selangor state map. It can be concluded that most of the landslide occurrences took place in the central of the Selangor state. In addition, soil series and lithologies for the Selangor state were analysed and mapped with the previous landslide locations. 14 soil series and 7 lithologies have been identified in the Selangor state. The soil series are Beriah-organic clay & muck, Keranji, Melaka-Tavy-Gajah Mati, Mined land, Munchong-Seremban, Peat, Prang, Rengam-Jerangau, Selangor-Kangkong, Serdang-Bungor-Munchong, Serdang-Kedah, Steepland, Telemong-Akob-Lundang and Urban land. Meanwhile, the lithologies for the Selangor state are Acid intrusives (undifferentiated), clay-silt-sand-gravel (undifferentiated), Limestone-Marble, Sandstone with subordinate shale-mudstone-siltstone-conglomerate-volcanics, Peat-humic clay-silts, Schist-Gneiss and Vein-Quartz. However, those 14 landslide cases only occurred at specific soil series and lithologies. 6 soil series and 4 lithologies have been repeatedly identified as having multiple landslide occurrences respectively. The locations of the 14 cases of landslide events were superimposed atop of the soil map and lithology map to extract the information pertaining to the associated series and lithologies. Figure 2 and Figure 3 show the locations of previous landslide event atop of the soil series map and lithologies map, respectively. It was found that Urban land dominates the soil series while Acid intrusive dominates the soil lithology at the locations of previous landslide events. However, other series and lithologies are also present, as shown in Table 2. The weightage and ranking for these series and lithologies were determined to produce the index of landslide susceptibility. Besides, rainfall and soil composition data at the landslide site were obtained and sampled to compute rainfall erosivity and soil erodibility factor. Soil erodibility and rainfall erosivity play key role at triggering landslide occurrences in the past [2]. Rainfall data was transformed into rainfall erosivity while soil composition data (percentage of occupy for sand, silt and clay) was transformed into a fraction between percentage of silt and sand as numerator and percentage of clay as denominator. Table 3 shows the summary of rainfall erosivity and soil composition fraction for study area. In [30] postulated that the soil composition fraction can be divided into five categories namely low risk (<1.5), moderate risk (1.5 – 4.0), high risk (4.0 – 8.0), very high risk (8.0 – 12.0) and critical (>12.0). Meanwhile, rainfall erosivity can be grouped into 5 categories as well; low risk (<500 ton.m2/ha.hr), moderate risk (500-1000 ton.m2/ha.hr), high risk (1000-1500 ton.m2/ha.hr), very high risk (1500-2000 ton.m2/ha.hr) and critical (>2000 ton.m2/ha.hr). With the exception of cases no. 7 and 10, other cases depict either rainfall erosivity at critical/high level or soil fraction at critical/high level. It is proven that critical rainfall erosivity or critical fraction of soil composition is susceptible towards landslide occurrences. Cross correlation plot shows that rainfall erosivity contribute huge impact on landslide occurrences in the Selangor state (see Figure 4). While the soil composition may contribute to the slope instability, the combination of high rainfall expediate the landslide process.

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Table 2. Mapping of Landslide Occurrences and Their Associates Soil Series and Lithology

No. Location and Date Coordinate Soil Series [ ] Lithology [ ] Main Cause of Types of Landslide Landslides Occurrence 1 17 February, 2006 3°19'40.93" N Mainly sandstone with  Heavy rainfall Bandar Country Serdang-Bungor- 101°31'52.49" E subordinate shale, mudstone, Homes [shallow Muchong siltstone, conglomerate and slides] [S-B-M] volcanics [M-S] 2 11 December, 3°10'35.72" N  Heavy rainfall Highland Tower 1993 101°45'43.35" E Acid intrusive  Inability to identify Urban land [U-L] [shallow slides] (undifferentiated) [A-I] groundwater movement 3 Puncak 15 May, 1999 3°11'10.29" N  Erosion due to Rengam-Jerangau Acid intrusive Athenaeum 101°46'13.29" E weathering process [R-J] (undifferentiated) [A-I] [shallow slides] 4 Kg Pasir Hulu 1 June, 2006 3°11'51.64" N  Heavy rainfall Kelang [shallow 101°45'47.59" E Urban land [U-L] Schist and Gneiss [S-G] slides] 5 Kg Sungai 10 November, 3°08'43.19" N  Heavy rainfall Acid intrusive Bukit Putih 2006 101°47'21.36" E Urban land [U-L] (undifferentiated) [A-I] [shallow slides] 6 Section 10 3 October, 2006 3°12'13.55" N  Heavy rainfall Wangsa Maju 101°44'12.25" E Urban land [U-L] Schist and Gneiss [S-G] [shallow slides] 7 Taman Beringin 13 April, 2006 3°13'20.10" N  Erosion due to Jinjang Utara 101°37'41.37" E Urban land [U-L] Limestone/Marble [L-M] weathering process [shallow slides] 8 Taman Bukit 17 May, 2006 3°01'47.43" N Acid intrusive  Heavy rainfall Urban land [U-L] Belimbing 101°43'59.12" E (undifferentiated) [A-I]  Slope becomes

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Balakong saturated with [shallow slides] water 23 November, 3°01'31.41" N Mainly sandstone with  Heavy rainfall Taman Bukit 9 2006 101°41'46.88" E subordinate shale, mudstone,  Slope becomes Serdang Serdang-Kedah [S-K] siltstone, conglomerate and saturated with [shallow slides] volcanics [M-S] water 15 January, 2006 3°06'23.88" N Mainly sandstone with  Heavy rainfall 10 Taman Desa 101°40'42.79" E subordinate shale, mudstone,  Slope becomes Urban land [U-L] [shallow slides] siltstone, conglomerate and saturated with volcanics [M-S] water 11 Taman 6 November, 2004 3°13'49.07" N  Heavy rainfall Harmonis 101°42'57.73" E Telemong-Akob-Lundang  Slope becomes Schist and Gneiss [S-G] Gombak [T-A-L] saturated with [Debris flow] water 12 20 November, 3°10'51.10" N  Heavy rainfall Taman Hillview 2002 101°45'41.91" E Acid intrusive  Slope becomes Hulu Kelang Urban land [U-L] (undifferentiated) [A-I] saturated with [shallow slides] water 13 Taman 26 September, 3°12'34.75" N  Heavy rainfall Melawati Bukit 2007 101°45'06.62" E Acid intrusive  Slope becomes Steepland [S] Mas [shallow (undifferentiated) [A-I] saturated with slides] water 27 February, 2007 3°17'50.24" N Mainly sandstone with  Heavy rainfall Taman Pelangi Serdang-Bungor- 14 101°33'25.89" E subordinate shale, mudstone,  Slope becomes Rawang Muchong siltstone, conglomerate and saturated with [shallow slides] [S-B-M] volcanics [M-S] water

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Table 3. Summary of Rainfall Erosivity and Soil Fraction for Study Area

No. Location Fraction of Soil Rainfall Composition, Erosivity

1 Bandar Country Homes 15 (critical) 1469 (high) 2 Highland Tower 15 (critical) 868 (moderate) 3 Puncak Athenaeum 7 (high) 1157 (high) 4 Kg Pasir Hulu Kelang 3 (moderate) 1469 (high) 5 Kg Sungai Bukit Putih 4 (moderate) 3886 (critical) 6 Section 10 Wangsa Maju 21 (critical) 2354 (critical) 7 Taman Beringin Jinjang 4 (moderate) 870 (moderate) Utara 8 Taman Bukit Belimbing 10 (very high) 3560 (critical) Balakong 9 Taman Bukit Serdang 4 (moderate) 1434 (high) 10 Taman Desa 2 (moderate) 733 (moderate) 11 Taman Harmonis Gombak 20 (critical) 2870 (critical) 12 Taman Hillview Hulu 4 (moderate) 2574 (critical) Kelang 13 Taman Melawati Bukit Mas 20 (critical) 2045 (critical) 14 Taman Pelangi Rawang 15 (critical) 977 (moderate)

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\ Figure 1. Previous Cases of Major Landslides in Selangor States

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Figure 2. Locations of Previous Landslides Atop of Soil Series in Selangor

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Figure 3. Locations of Previous Landslides Atop of Strata Lithology in Selangor

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Figure 4. Cross Correlation Plot Between Rainfall Erosivity and Soil Fraction (Numbering Represent the Case No.

3. Results and Analysis

3.1. Development of Hierarchy Index The landslide susceptibility index for the Selangor state was developed using a combination of heuristic method and AHP method. The AHP process involves four main steps, as illustrated by [31]: 1) Knowing the problem statement and the type of decision to be made; 2) Creating the decision hierarchy by segregating different levels of judgement; 3) Constructing a set of pairwise comparison matrices; 4) Assigning the weightage priorities for each element. In [5] explained in detail the steps taken to complete the implementation of AHP technique. At the first stage, the weightage of soil series- lithology prone landslide was assigned by inferring previous occurrences of landslides in the Selangor state (by referring to landslide inventory). Then, the level of susceptibility (heuristic approach) was deployed throughout all provinces by the means of a risk map. The weightage assignment using AHP techniques is shown in stepwise below to illustrate the AHP concept. The first step in the AHP technique is the development of a graphical representation of the goal and hierarchy of the problems. The main goal is to categorize the risk level of landslide susceptibility based on the following criteria: critical, very high, high, moderate and low. The main goal can be achieved by knowing the criteria and sub- criteria of the decision making. Two main criteria were identified; soil series and strata lithology. The sub-criteria were extracted from Table 3, where previous landslide occurrences were identified. The classifying hierarchy must be structural; and alternatives must be provided to that graphical representation. The decision must start from the bottom level such that decision alternatives → sub-criteria → criteria → overall goal. The graphical representation is shown in Figure 5.

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Figure 5. Graphical Representation of Criteria and Sub-Criteria

Two main criteria were considered prior to index creation: soil series and strata lithology. There were 6 sub-criteria for soil series and 4 sub-criteria for strata lithology. Pairwise comparison is made between two components within the same hierarchy. Pairwise comparison is a technique of comparing elements in pairs for the same criteria. The preference scales (1-9) were used for this operation. Number 1 represents ‘equal importance while number 9 represents ‘extreme important’. Bi-polar questionnaire was used to obtain expert’s opinion (in this case, on the previous landslide inventories) to determine the degree of preference for each criteria. The number of questions for each criterion can be determined using the equation

(1)

where n is the number of sub-criteria for each main-criteria. For soil series, the number of bi-polar questions is 6(6-1)⁄2=15, while for strata lithology criteria, the number of bipolar questions is 4(4-1)⁄2=6. An Excel spreadsheet was created to find the preference for each criteria. After the pairwise comparison matrix has been obtained, the relative priority for each decision alternative must be calculated to get normalized principal eigenvectors. Three major operations are involved in getting the eigenvector values. First, the values in each column of the pairwise comparison matrix must be summed. Then, each element in the pairwise matrix is divided by its column total. Finally, the average of the elements in each row of the normalized matrix is computed to get the final normalized principal eigenvector. Figure 6 and 7 show the eigenvector for soil series and strata lithology,

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respectively. This eigenvector can be defined as the ranking for each element. The eigenvalue with the highest percentage ranks the first in the hierarchy.

Figure 6. Normalized Principal Eigenvector for Soil Series

Figure 7. Normalized Principal Eigenvector for Strata Lithology

Consolidation phase is the last technique in AHP. The main purpose of consolidation is to obtain the set of overall priorities for a decision problem. It is done by assigning weightage to give a single number to indicate the priority of each element. This step is also known as index development using a local weight (LW) and global weight (GW) percentage. In the landslide susceptibility index, it was assumed that soil series and strata

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lithology share an equal weightage (50% each) to the overall prediction. Both main criteria were assigned 50% each for LW. The percentage from normalized principal eigenvector gave the LW for the sub-criteria, and GW was obtained by multiplying these two LWs. Table 4 shows the final GW for this research. This GW provides a basis for landslide susceptibility index in the Selangor state.

Table 4. Final Local Weightage (LW) and Global Weightage (GW) Goal Criteria LW Sub-Criteria LW GW Susceptibility Soil series Serdang-Bungor- 14.29% 7.15% Index Munchong Urban-Land 57.14% 28.57% 50% Rengam-Jerangau 7.14% 3.57% Serdang-Kedah 7.14% 3.57% Telemong-Akob- 7.14% 3.57% Lundang Steepland 7.14% 3.57% Total 100% 50% Strata Mainly-Sandstone 24.55% 12.28% Lithology Acid Intrusive 45.57% 22.79% 50% Limestone/Marble 7.09% 3.55% Schist and Gneiss 22.79% 11.4% Total 100% 50% In order to categorize soil series and strata lithology using heuristic approach, the index level must be formulated to sort the probability of occurrence under different levels of risk. A scale of either ‘0’ or ‘1’ is assigned to the occurrence of respective elements to obtain the score. If any locations depict the presence of any series or lithology, then scale ‘1’ is assigned. Otherwise, scale ‘0’ is assigned. The final score is tallied to get the final classification. Since each element is normalized to 50%, then the maximum score is expected to be ≥0.5 and the minimum score is expected to be ≤0. As demonstrated by Likert scale, the overall tallied values are categorized as follows:

0.00 - 0.09 → Low risk 0.10 - 0.19 → Moderate risk 0.20 - 0.29 → High risk (2) 0.30 - 0.49 → Very high risk ≥ 0.50 → Critical risk

An example of such tally is shown in Table 5. Say that location A has a soil series of Serdang-Bungor-Munchong (S-B-M), and its strata lithology is mainly sand stone (M-S). Then, the scale of ‘1’ is assigned to the respective series and lithology while the rest is assigned as zero (0) values. The total score for the combination of S-B-M and M-S is 0.19 which falls under moderate risk. The same operation was used for the probability of other combinations in order to produce the risk levels.

Table 5. Example of Total Score

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Goal Criteria LW Sub-criteria LW GW Occurrence Score S-B-M 14.29% 7.15% 1 0.07145 U-L 57.14% 28.57% 0 0 50% R-J 7.14% 3.57% 0 0 Soil series S-K 7.14% 3.57% 0 0 Susceptibility T-A-L 7.14% 3.57% 0 0 Index S 7.14% 3.57% 0 0 M-S 24.55% 12.28% 1 0.12275 A-I 45.57% 22.79% 0 0 Strata Lithology 50% L-M 7.09% 3.55% 0 0 S-G 22.79% 11.40% 0 0 Moderate 0.1942

Table 5 forms a basis for the susceptibility index. Any series and lithology in the Selangor state can be fitted with this index to find the risk level. Verification was made on those 14 landslides in the inventory to determine the risk level for each cases. Table 6 shows the verification using the newly developed index. It can be concluded that majority of the cases were at the high, very high and critical level (10 cases out of 14). This verification somehow suggests that this index can be used to find the susceptibility index for any areas in the Selangor state for landslide mitigation works in the future.

Table 6. Index Verification Against the Landslide Inventories No. Location Soil series Lithology [ Total Risk [ ] ] Score Level 1 Bandar Country Homes [S-B-M] [M-S] 0.194 Moderate 2 Highland Tower [U-L] [A-I] 0.514 Critical 3 Puncak Athenaeum [R-J] [A-I] 0.264 High 4 Kg Pasir Hulu Kelang [U-L] [S-G] 0.400 Very High 5 Kg Sungai Bukit Putih [U-L] [A-I] 0.514 Critical 6 Section 10 Wangsa Maju [U-L] [S-G] 0.400 Very High 7 Taman Beringin Jinjang 0.321 Very high [U-L] [L-M] Utara 8 Taman Bukit Belimbing 0.514 Critical [U-L] [A-I] Balakong 9 Taman Bukit Serdang [S-K] [M-S] 0.158 Moderate 10 Taman Desa [U-L] [M-S] 0.408 Very high 11 Taman Harmonis Gombak [T-A-L] [S-G] 0.150 Moderate 12 Taman Hillview Hulu 0.514 Critical [U-L] [A-I] Kelang 13 Taman Melawati Bukit Mas [S] [A-I] 0.264 High 14 Taman Pelangi Rawang [S-B-M] [M-S] 0.194 Moderate Based on the landslides inventory, 6 soil series and 4 strata lithology were identified as having previous landslide occurrences. A possible 24 combination was made between these two factors to observe the total tallied score, as shown in Table 7. It was found that four combinations fall under “low risk”, 11 combinations fall under “moderate risk”, five combinations fall under “high risk”, three combinations fall under “very high risk” and one combination fall under “critical risk”. Fitting with previous records revealed that 78% of previous landslide events fall under “high risk”, “very high risk” and “critical risk” level.

Table 7. Final Susceptibility Class with Respect to Risk Level Risk Level Combination (24) Previous

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Soil Series Strata Lithology Recorded Cases in Selangor State (14 Numbers) Rengam- Limestone/Marble No records Low Jerangau Serdang-Kedah Limestone/Marble No records Telemong-Akob- Limestone/Marble No records Lundang Steepland Limestone/Marble No records Serdang-Bungor- Mainly-Sandstone 2 Munchong Rengam- Mainly-Sandstone No records Moderate Jerangau Serdang-Kedah Mainly-Sandstone 1 Telemong-Akob- Mainly-Sandstone No records Lundang Steepland Mainly-Sandstone No records Serdang-Bungor- Limestone/Marble No records Munchong Serdang-Bungor- Schist and Gneiss No records Munchong Rengam- Schist and Gneiss No records Jerangau Serdang-Kedah Schist and Gneiss Telemong-Akob- Schist and Gneiss 1 Lundang Steepland Schist and Gneiss Serdang-Bungor- Acid Intrusive No records High Munchong Rengam- Acid Intrusive 1 Jerangau Serdang-Kedah Acid Intrusive No records Telemong-Akob- Acid Intrusive No records Lundang Steepland Acid Intrusive 1 Urban-Land Mainly-Sandstone 1 Very High Urban Land Schist and Gneiss 2 Urban-Land Limestone/Marble 1 Critical Urban-Land Acid Intrusive 4

3.2. Consistency Checking The quality of the ultimate decision was related to the consistency of judgments demonstrated during the series of pairwise comparisons. Consistency ratio (CR) exceeding 0.10 is indicative of inconsistent judgments [21]. A sample of inconsistency is given such that: If, A > B; B > C, then A > C. If A < C, then inconsistency is said to be present.

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Each value in the first column of the pairwise comparison matrix is multiplied by the relative priority of the first item considered. The same procedure prevails for other criteria. The values are summed across the rows to obtain a vector of values, labelled as “weighted sum.”

(3)

The elements of the vector of weighted sums are divided by the corresponding priority value such that

(4)

Then, the average value in (4) is computed such that

(5)

Consistency Index (CI) is calculated such that utilizing lambda values from (5)

(6)

Consistency Ratio (CR) can be obtained by dividing CI from (6) by random matrix (RI) given by [31]

(7)

Since the CR for soil series was less than 0.10, it can be concluded that the pairwise comparison was made correctly and consistently. The CR for strata lithology was checked using the same approach. The value for CR was 0.00082 which is less than 0.10. Overall, it can be concluded that the bi-polar expert judgment has been carried out consistently.

3.3. Application: Risk Map for Selangor State The creation of susceptibility index was followed by the construction of hazard risk map for the Selangor state. The construction of hazard map used the same principle as the suitability map where reclassification and weighted option in GIS tools were activated.

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Instead of suitability map, the hazard map was produced to indicate the worst possible locations for future landslide occurrences. Weighted overlay analysis is suitable to be used for multi-criteria problem and efficient for large area. Three main operations are involved in weighted overlay analysis, namely reclassification of values in the input rasters into the common preference scales; multiplication of cell values of each input rasters and production of output rasters. The creations of rasters are very important as the tool only accepts integers in rasters as inputs [8]. The first step in implementing weighted overlay is the selection of an evaluation scale. The lowest scale represents the least risk of possible landslide occurrences, and higher scale indicates the higher degree of landslide susceptibility. Table 8 shows the arrangement of evaluation scale for both soil series and strata lithology. The first rank in each soil series or strata lithology was assigned with the highest evaluation to be fitted in the GIS application; whereas, other series or strata lithologies which were not recorded in the inventory were assigned as zero. After the scale was defined, the add raster button was activated to open the Add Weighted Overlay dialog box. The reclassification must be performed to convert the continuous rasters into group. Each input rasters can be weighted based on its importance. Finally, the cell values for each input rasters were multiplied by the raster’s weight to produce the final raster product.

Table 8. Evaluation Scale for Soil Series and Strata Lithology Criteria Sub-Criteria LW GW Ranking Evaluation Scale Soil series Serdang-Bungor- 14.29% 7.15% 2 2 Munchong Urban-Land 57.14% 28.57% 1 3 Rengam-Jerangau 7.14% 3.57% 3 1 Serdang-Kedah 7.14% 3.57% 3 1 Telemong-Akob- 7.14% 3.57% 3 1 Lundang Steepland 7.14% 3.57% 3 1 Other types of 0 % 0 % 4 0 series Strata Mainly-Sandstone 24.55% 12.28% 2 3 Lithology Acid Intrusive 45.57% 22.79% 1 4 Limestone/Marble 7.09% 3.55% 4 1 Schist and Gneiss 22.79% 11.4% 3 2 Other types of 0 % 0 % 5 0 lithology The final hazard map was shown as the output of raster product. It was elucidated that the central part of Selangor state has higher landslide susceptibility compared to the northern, southern or western part. The landslide locations in the hazard map is consistent with the previous landslide events where the distribution of previous cases fall in the red coded area (see Figure 8). Gombak, Petaling and Ulu Langat district are prone to landslide occurrence due to the presence of urban land and acid intrusive lithology (see Figure 9). The raster multiplication products are consistent with the susceptibility index, where the integer raster product of urban land and acid intrusive is the highest compared to others (see Table 9). The integer of 12 represents the multiplication of 4 and 3 for the evaluation scale. The susceptible chart in Table 9 and Figure 9 can provide a basis for landslide mitigation by authority in the future development of area.

Table 9. Raster Multiplication Product Acid 4 0 4 4 4 4 8 12

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Intrusive Mainly- 3 0 3 3 3 3 6 9 Sandstone Schist and 2 0 2 2 2 2 4 6 Gneiss Limestones 1 0 1 1 1 1 2 3 / Marble Other 0 0 0 0 0 0 0 0 Types of Lithology 0 1 1 1 1 2 3

Other Rengam Serdang Telemong Steepland Serdang – Urban Types - - Kedah – Akob - Bungor - - of Jerangau Lundang Munchong Land Soils

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Figure 8. Superimposition of Previous Landslide Occurrences on Hazard Map

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Figure 9. Final Hazard Map for Selangor

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3.4. Discussion Past researchers have found out the relationship between soil survey and strata lithology, and the landslide occurrences. However, most of their researches focused on either soil survey or strata lithology exclusively to determine the landslide susceptibility. In [19] postulated that soil type does not have influences on landslide, but soil textures do. Soil horizon stratification, depth to bedrock, landuse and slope shape gave significance influences on the landslide occurrences. This conclusion makes sense for soil series in Malaysia where different soil series have different soil textures and horizon stratifications. Based on the landslide susceptibility table (see Table 6 and 8), urban land and Serdang- Bungor-Muchong are categorized as having ‘very high’ and ‘critical’ risk levels, as opposed to Rengam-Jerangau or Serdang-Kedah at ‘low’ risk level. Soil series at ‘higher’ to ‘critical’ risk levels consist of clay or clay loam type of soil texture, while those of ‘low’ to ‘moderate’ risk levels consist of sandy loam or sandy clay loam type of texture. These soil textures play important role in the soil erodibility. The work by [24] revealed that clay or clay loam type of soil textures have high soil erodibility values and are susceptible to soil erosion. The soil erodibility, K for clay ranged from 0.042 (ton.ha) (ha.hr/MJ.mm) to 0.065 (ton.ha) (ha.hr/MJ.mm), which is more susceptible to soil erosion. In the meantime, the soil erodibility of sandy clay ranged from 0.031 to 0.043 (ton.ha) (ha.hr/MJ.mm), which is moderately susceptible to erosion. In [32] postulated that lithology and structural characteristics are two main factors in geology that influence landslide occurrences. The same conclusion had been made by [16], [20] that 75% of landslide occurrences in Nepal were due to geological structure and lithology. Acid intrusive, limestone/marble and sandstone are susceptible to landslides as a result of fractures, shear zones and dib of the beds. In [13] pointed out that although landslides can occur anywhere, most of the occurrences happened at the preferential locations. The hard rocks such as sandstone or limestone are more permeable than soft rocks. The permeability of these rocks will concentrate water towards the joints, fractures or high permeability zones. A remodelling of geological map by [14] revealed the same conclusion as [13]. The hard rock (e.g.: sandstone etc.) contributed to the highest number of landslide occurrences. For the Selangor state, acid intrusive (undifferentiated) dominates the landslide occurrences due to discontinuities. Discontinuities provide the slope with strong mechanical and hydrogeological anisotropy that controls the geometry and development of failures [9], [13], [35]. Based on the zoning map, 74% of Selangor state fall under low risk (LR), 0.3% at medium risk (MR), 4.5% at high risk (HR), 1.3% at very high risk (VHR) and the remaining of 18.95 fall under critical risk (CR). Table 10 shows the distribution of risk level percentage based on each district. Gombak, Ulu Selangor and Ulu Langat recorded the highest percentage of possible landslide occurrences in the future that stood at 43.6%, 41.6% and 44.8% respectively. Besides the nature of soil series and lithology, these 3 districts are located at steeper slopes opposed to Klang, Kuala Langat, Kuala Selangor, Sabak Bernam and Sepang which were located at lower gradient then the former.

Table 10. Percentage Distribution of Risk Level Based on Available Districts in Selangor District Total % % % % % Area Low Risk Moderate High Risk Very High Critical Risk (km2) (Multiplica Risk (Multiplicati Risk Moderate tion (Multiplicat on Product (Multiplicat Risk Product = ion Product = 3) ion Product (Multiplicati 0) = 1 & 2) = 6 & 8) on Product = 9 & 12) Gombak 626 49.8 0.0 4.5 2.1 43.6

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Ulu Selango 1759 43.9 0.5 12.3 1.8 41.6 r Klang 637 91.2 0.0 8.8 0.0 0.0 Kuala 864 98.0 1.5 0.5 0.0 0.0 Langat Kuala Selango 1195 97.0 0.0 2.8 0.1 0.0 r Petaling 498 80.0 0.2 3.4 4.2 12.2 Sabak 986 99.8 0.0 0.2 0.0 0.0 Bernam Sepang 606 97.7 0.7 1.5 0.1 0.0 Ulu 849 51.9 0.0 0.0 3.3 44.8 Langat

4. Conclusion A few conclusions can be drawn from this research. Although the detailed survey and mapping for soil series and lithology were not conducted thoroughly, a rapid assessment on landslide susceptibility was made by assigning the weightage for each soil series and lithology. A combination of AHP technique and heuristic approach was used by categorizing the rank and assigning the risk level as illustrated in Table 6-8. The key points for this research are as follows: i. A few qualitative and quantitative techniques are available to predict landslide susceptibility in the past and future. Each technique and approach has its own advantages and disadvantages. ii. Malaysia consistently receives high intensity of rainfall throughout the year. The erosivity patterns of the rainfall are more pronounced in Malaysia than in the arid countries. Rainfall erosivity in Table 3 proofs that Malaysia relatively received higher rainfall intensity throughout the year. Thus, landslides can occur more frequently with high severity. The Selangor state has recorded the highest number of landslide occurrences in Malaysia. Located at the central of Malaysia, the Selangor state has lost billions of dollar due to this catastrophe. The development of a landslide inventory in Malaysia is quite new, and lack of expertise on landslide study has motivated the authors to produce a landslide susceptibility index. iii. The maps of soil series and lithology were made available to the public through the related agencies. The landslide inventories associated with the respected soil series and lithology were produced for the Selangor state. iv. A combination of AHP method and heuristic approach was successfully applied to produce the index. The weightage and rank were assigned for the respective soil series and lithology to get the eigenvalues (see Table 4). The heuristic approach was then used by grouping each series and lithology into “Low”, “Moderate”, “High”, “Very High” and “Critical” risk levels (see Table 7). The grouping of these levels was made using the Global Weight values of the AHP index. By inferring to the Likert scale, each series and lithology was successfully categorized into those levels. v. Urban land and acid intrusive are the most dominant soil series and lithology that influence the locations of landslide occurrences. The soil texture of urban land, which is clay and clay loam are believed to have high erodibility values. Thus, high erosion rates are expected with this kind of soil texture. The discontinuities and high permeability patterns in acid intrusive lithology dominated the “very high” risk and

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“critical” risk levels. The discontinuities controlled the development of failures and high permeability will concentrate the water towards the joints of highly permeable layer. It is proven from Table 3 that higher fraction values are related to higher proportion of numerator (% sand) which is believed promotes high permeability in soils. vi. Consistency checking has been made via statistical calculation. The index seems sound and reliable from the statistical point of view. The susceptibility index in Table 8 can be used as a basis to create the landslide risks map for the Selangor state. The critical area can be known instantly by inferring to those associated soil series and lithology. A hazard map using GIS was created to assist the authorities or developers in decision making or making landslide mitigation measures at the critical risk areas. The hazard map shows that the central part of Selangor has a high tendency towards landslide occurrences compared to the eastern and western part of the state. This is due to the presence of urban land and acid intrusive in these areas. Implementation of AHP techniques successfully elucidate the most critical hierarchy that susceptible to landslides. Meanwhile, hazard mapping using GIS application provides ease towards rapid assessment for landslides occurrences.

Acknowledgments Special thanks to members of I-Geo Disaster Research Centre, Infrastructure University Kuala Lumpur: Dyg Siti Quraisyah, Nik Nuraini Azhari, Khairunnisah Kamaruzzaman for their support and encouragement to conduct this research successfully

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