An Overview: Relationship of Geological Condition and Rainfall with Landslide Events at East Malaysia

Rosly, Mohammad Haziq; Mohamad, Habib Musa; Bolong, Nurmin; Harith, Noor Sheena Herayani

doi:10.48048/tis.2022.3464

Cited by 9 publications

(7 citation statements)

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“…Additionally, most of the landslides that occur in Langat River Basin are caused by heavy rain, which occurs most frequently from May to September in the southwest monsoon and from October to March in the northeast monsoon. These findings are consistent with [85,116,117], which found that increasing rainfall intensity throughout the monsoon season will lead to an increase in landslide occurrences. The high intensity of rainfall is frequently related to the slope stability, where it affects the run-off water pressure.…”

Section: Discussionsupporting

confidence: 90%

“…Rainfall is the triggering factor that causes landslide occurrences, raising the groundwater level and increasing pore water pressure beneath the surface [82,83]. Over the last decade, previous research has found the presence of an empirical relationship between rainfall and landslide occurrences [84,85]. Rainfall is classified as a natural phenomenon where it is regarded as a potential contributor that significantly impacts the occurrence of landslides [86].…”

Section: Rainfallmentioning

confidence: 99%

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Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

Selamat

Majid

Taha

et al. 2022

Land

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Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, and preventing landslides occurrences to minimize losses. A variety of techniques are employed to map landslide susceptibility; however, their capability differs depending on the studies. The aim of the research is to produce a landslide susceptibility map for the Langat River Basin in Selangor, Malaysia, using an Artificial Neural Network (ANN). A landslide inventory map contained a total of 140 landslide locations which were randomly separated into training and testing with ratio 70:30. Nine landslide conditioning factors were selected as model input, including: elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), distance to road, distance to river, lithology, and rainfall. The area under the curve (AUC) and several statistical measures of analyses (sensitivity, specificity, accuracy, positive predictive value, and negative predictive value) were used to validate the landslide predictive model. The ANN predictive model was considered and achieved very good results on validation assessment, with an AUC value of 0.940 for both training and testing datasets. This study found rainfall to be the most crucial factor affecting landslide occurrence in the Langat River Basin, with a 0.248 weight index, followed by distance to road (0.200) and elevation (0.136). The results showed that the most susceptible area is located in the north-east of the Langat River Basin. This map might be useful for development planning and management to prevent landslide occurrences in Langat River Basin.

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Section: Discussionsupporting

confidence: 90%

Section: Rainfallmentioning

confidence: 99%

Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

Selamat

Majid

Taha

et al. 2022

Land

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“…Based on the fuzzy ANP model dark gray shale formation and based on fuzzy TOPSIS the Lowlevel valley terrace had the highest effect on landslides occurrence while based on the fuzzy ANP model Orbit Olin limestone formation and based on fuzzy TOPSIS the Upper Cretaceous formation had the least effect to landslide occurrence. Rosly et al [87] concluded that the areas that have been covered by shale interbedded with sandstone are more prone to landslide occurrence. In fact, shale has a high amount of clay and is classified as highly plastic soil with increasing pore water pressure from rainfall, the amount of infiltration and consequently matric suction are decreased and finally, the shear strength of the soil is diminished and landslide would be occurred [87].…”

Section: Discussionmentioning

confidence: 99%

“…Rosly et al [87] concluded that the areas that have been covered by shale interbedded with sandstone are more prone to landslide occurrence. In fact, shale has a high amount of clay and is classified as highly plastic soil with increasing pore water pressure from rainfall, the amount of infiltration and consequently matric suction are decreased and finally, the shear strength of the soil is diminished and landslide would be occurred [87].…”

Section: Discussionmentioning

confidence: 99%

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Shahabi¹,

Tavakolifar²,

Alizadeh³

et al. 2023

Preprint

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Landslides around the main roads in the mountains not only cause fatal events but also cause ecosystem damage, including land degradation. This study aims to map the susceptibility of the landslides around the Saqqez-Marivan main rod of Kurdistan province, Iran, using ensemble Fuzzy logic with Analytic Network Process (Fuzzy Logic-ANP; FLANP), and with TOPSIS (Fuzzy Logic-TOPSIS; FLTOPSIS). A total of 100 landslides were first recognized by field surveys and then they were randomly divided into a 70% dataset (70 locations) and a 30% dataset (30 locations), respectively, for training and validating the methods. Eleven landslide conditioning factors, including slope, aspect, elevation, lithology, land use, distance to fault, distance to a river, distance to road, soil type, curvature, and precipitation were used. The performance of the methods was checked by the areas under the receiver operating curve (AUCROC). Results concluded that the prediction accuracy based on validating datasets were, respectively, 0.882 and 0.918 for FLANP and FLTOPSIS methods. Our findings demonstrated that although both models were known as promising techniques, the FLTOPSIS method had a better capacity for predicting the susceptibility of landslides in the studied area. Therefore, the susceptibility map developed by the FLTOPSIS method can be used for the proper management of areas with high landslide potential and also for managers and planners during the implementation of land allocation and development projects, especially in mountainous areas.

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“…In most cases, these landslides occur on slopes where large amounts of soil mass are moving rapidly. The problem is exacerbated by the rapid development of new infrastructure and housing, which has resulted in a rise in landslides on unstable slopes [9]. This is primarily due to the soil erosion, soil compaction and removal of vegetation caused by human activities [10].…”

Section: Introductionmentioning

confidence: 99%

Soil Detachment Rate of a Rainfall-Induced Landslide Soil

Batumalai,

Mohd Nazer,

Simon

et al. 2023

Water

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In recent decades, the number of rainfall-induced landslides has increased significantly in many parts of Malaysia, especially in the urbanized and hilly areas. The disturbance of hilly morphology as a result of human activities has increased the potential for erosion on man-made slopes, especially during extreme rainfall during rain events. Most hilly areas in Malaysia are covered by a thick layer of soil, which is known to have a significant impact on soil erosion. However, little is known about how soil erosion and rainfall could be the driving force behind landslide initiation, especially on stabilized slopes. Therefore, this study focuses on the soil detachment rate of landslides triggered by rainfall at different rainfall intensities. A sandbox model is used to represent real slope conditions. The relationship between the soil detachment capacity, soil properties (water content, slope, clay layers and soil compaction), hydraulic parameters (flow shear stress and stream power) and rainfall intensities (low, medium and high) was investigated. The results showed that the hydraulic parameters and the rainfall intensity are directly proportional to the detachment rate of the soil. Water content and slope show a higher soil detachment rate and a lower critical flow shear stress than other soil properties. It can be concluded that high saturation and steep slope increase the risk of soil erosion because the cohesion and friction of the soil are significantly reduced, leading to a weakening of the soil structure at the surface. The results of this study can feed into the existing analysis of slope stability and formulate the onset of a landslide triggered by rainfall, especially in eroded soils.

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An Overview: Relationship of Geological Condition and Rainfall with Landslide Events at East Malaysia

Cited by 9 publications

References 0 publications

Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Soil Detachment Rate of a Rainfall-Induced Landslide Soil

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