Landslides and lineament mapping along the Simpang Pulai to Kg Raja highway, Malaysia

Yusof, Norbazlan Mohd; Ramli, Mohammad Firuz; Pirasteh, Saied; Shafri, H. Z. M.

doi:10.1080/01431161.2010.484434

Cited by 30 publications

(11 citation statements)

References 36 publications

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“…The number of landslides have been increased due to the climate change, deforestation, unplanned urbanisation, etc. (Yusof et al 2011;Pourghasemi et al 2012a, b;Pirasteh et al 2017). Thus, one of the most important actions of reduction of landslide hazards is the identification of landslide-prone areas through LSM by different methods.…”

Section: Introductionmentioning

confidence: 99%

Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method

et al. 2019

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This study presented the prediction capability of three methods including the frequency ratio (FR), fuzzy gamma (FG) and landslide index method (LIM) to produce landslide-prone areas in the Sari-Kiasar watershed, Mazandaran Province of Iran. In the first step, 105 landslide locations were selected and were randomly divided into two groups of 75% (78 locations) and 25% (27 locations) as training and testing datasets. Then the 17 landslide conditioning factors including land use/land cover, Differential Vegetation Index (DVI), lithology and distance from faults, elevation, slope aspect, slope angle, tangential curvature, profile curvature and plane curvature, distance from drainage, rainfall, Stream Power Index, Sediment Transport Index and temperature, and distance from road, density of settlement were considered for the proposed modelling approach. Finally, by applying the training dataset, three landslide susceptibility maps were constructed by using the FR, FG and LIM methods. The prediction capability of the performed model was evaluated by the area under the receiver operating curve or AUC for both training (success rate) and testing (prediction rate) datasets. The results showed that the AUC for success rate of FR, FG and LIM models was 82.04%, 81.08% and 73.61% and for prediction rate was 82.72%, 79.09% and 65.45%, respectively. The results showed that the FR model has a higher prediction accuracy than the FG and LIM methods. This study revealed that the most important factors in landslide occurrence are rainfall, slope and vegetation. The result of the present study can be possibly useful for land use planning and watershed management.

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Section: Introductionmentioning

confidence: 99%

Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method

et al. 2019

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“…From the results of the lineament analysis, the potential landslide area of Tugurejo is between ridge and valley oriented to the Northwest-Southeast direction which is lineament pattern of strike-slip in the north of the study area (Figure. 2). The results of [5] showed a correlation between lineament and landslide vulnerable areas where areas with lineament density are P more susceptible to landslides. The study area has a slope facing the Southeast.…”

Section: Study Areamentioning

confidence: 93%

Landslide Analysis Using Seismic Refraction Tomography And MASW: a Case Study in Ponorogo, East Java, Indonesia

Prayitna

Utama

Warnana

2019

IJOE

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 Ponorogo district morphology form consisting of highlands and hills make this area vulnerable to landslides. Based on the BNPB (National Disaster Management Agency) data from 2013 to 2017, 77 landslides and 35% have occurred in Ngrayun and Slahung sub-districts. One area that has the potential to experience landslides is Tugurejo village in Slahung District. This study aims to determine the physical properties of subsurface rocks using SRT (Seismic Refraction Tomography) and MASW (Multi-channel Analysis of Surface Waves). The seismic velocity distribution defined from the SRT and MASW at the study sites shows a zone with a higher porosity that is interpreted to represent the level and depth of rock mass movement. P-wave velocity and S-wave indicates very low velocity of 500 -1200 m/s and 100 -250 m/s at depths up to 5 m.

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“…Geological lineaments are the manifestation at the earth's surface of deeper geological structures (faults and fractures that have obvious displacement, ruptures that have no significant fracture displacement) (Han, Liu, Ning, & Zhao, 2018). It has been used to investigate the distribution of minerals (Bahiru & Woldai, 2016), determine the potential areas for runoff water harvesting (Elewa, Qaddah, & El-Feel, 2012), the regional distribution of groundwater potential (Magesh et al, 2012), geological disasters (Yusof, Ramli, Pirasteh, & Shafri, 2011), geothermal resources, earthquakes (Elmahdy & Mohamed, 2016) and geomorphology. Moreover, identification of the weathered zones from the lineament density map is useful in soil erosion studies (Krishna, Kishan, & Sarup, 2015).…”

Section: Lineamentmentioning

confidence: 99%

Groundwater potential mapping using geospatial techniques: a case study of Dhungeta-Ramis sub-basin, Ethiopia

Tolche

2020

Geology, Ecology, and Landscapes

122

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The objective of this paper is to exploit the potential application of weighted index overlay analysis for assessing groundwater potential mapping at Dhungeta-Ramis sub-basin, Wabi Shebele basin, Ethiopia using remote sensing and geographic information system (GIS) technique. For this purpose, seven groundwater occurrences and movement controlling factors, including, lithology, slope, land use land cover (LULC), rainfall, lineaments, soil, and drainage density were mapped. Then, weight was assigned to thematic maps, and the groundwater prospective of the sub-basin is qualitatively classified into five classes, namely, very good, good, moderate, poor, and very poor which account for 2.22%, 26.93%, 56.74%, 13.84%, and 0.26% landscape, respectively. The cross-validation of the resultant model was carefully carried out using spring, hand-dug, and deep well data. The result reveals that 89% of springs were overlaying good and/or very good groundwater potential zones and 58% of deep well shows the same scenario, whereas 42% of deep well overlays moderate zone. As a result, the map generated using this platform could be used as a preliminary reference in selecting suitable sites for groundwater resource exploitation.

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Landslides and lineament mapping along the Simpang Pulai to Kg Raja highway, Malaysia

Cited by 30 publications

References 36 publications

Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method

Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method

Landslide Analysis Using Seismic Refraction Tomography And MASW: a Case Study in Ponorogo, East Java, Indonesia

Groundwater potential mapping using geospatial techniques: a case study of Dhungeta-Ramis sub-basin, Ethiopia

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