Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong

Dai, Fuchu; Lee, CF; Li, J; Xu, Zhichang

doi:10.1007/s002540000163

Cited by 500 publications

(56 citation statements)

References 18 publications

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“…Heuristic models rely mainly on the expert knowledge to assign weights to the various conditioning factors (e.g., Dai and Lee 2002;Dahal et al 2008a, b). The heuristic models Landslides Original Paper are highly subjective and depend on the site itself (Dai et al 2001). Deterministic models are completely based on mathematical relationships that depend on the physical laws in which the relation between resisting and driving forces can be calculated for the mass movements.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia

Youssef

Pourghasemi²,

Pourtaghi³

et al. 2015

Landslides

606

246

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Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia Abstract The purpose of the current study is to produce landslide susceptibility maps using different data mining models. Four modeling techniques, namely random forest (RF), boosted regression tree (BRT), classification and regression tree (CART), and general linear (GLM) are used, and their results are compared for landslides susceptibility mapping at the Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslide locations were identified and mapped from the interpretation of different data types, including high-resolution satellite images, topographic maps, historical records, and extensive field surveys. In total, 125 landslide locations were mapped using ArcGIS 10.2, and the locations were divided into two groups; training (70%) and validating (25%), respectively. Eleven layers of landslide-conditioning factors were prepared, including slope aspect, altitude, distance from faults, lithology, plan curvature, profile curvature, rainfall, distance from streams, distance from roads, slope angle, and land use. The relationships between the landslide-conditioning factors and the landslide inventory map were calculated using the mentioned 32 models (RF, BRT, CART, and generalized additive (GAM)). The models' results were compared with landslide locations, which were not used during the models' training. The receiver operating characteristics (ROC), including the area under the curve (AUC), was used to assess the accuracy of the models. The success (training data) and prediction (validation data) rate curves were calculated. The results showed that the AUC for success rates are 0.783 (78.3%), 0.958 (95.8%), 0.816 (81.6%), and 0.821 (82.1%) for RF, BRT, CART, and GLM models, respectively. The prediction rates are 0.812 (81.2%), 0.856 (85.6%), 0.862 (86.2%), and 0.769 (76.9%) for RF, BRT, CART, and GLM models, respectively. Subsequently, landslide susceptibility maps were divided into four classes, including low, moderate, high, and very high susceptibility. The results revealed that the RF, BRT, CART, and GLM models produced reasonable accuracy in landslide susceptibility mapping. The outcome maps would be useful for general planned development activities in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.

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Section: Introductionmentioning

confidence: 99%

Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia

Youssef

Pourghasemi²,

Pourtaghi³

et al. 2015

Landslides

606

246

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“…Lithology plays an important role in landslide studies because different lithological units demonstrate different proceeding to active geomorphological processes like landslides (Carrara et al 1991;Dai et al 2001). Another evaluation that is important from lithologic perspective is related to the weathering degree of the units.…”

Section: Methodsologymentioning

confidence: 99%

“…In literature, the lithology, slope, and land cover have been effectively used in the landslide studies (e.g. Lee and Min 2001;Anbalagan 1992;Gokceoglu and Aksoy 1996;Atkinson and Massari 1998;Dai et al 2001;Saha et al 2002;Nagarajan et al 2000;Cevik and Topal 2003;Gokceoglu et al 2005). Land use and change have been recognized as the most important factor influencing the occurrence and re-activation of landslides triggered by rainfalls.…”

Section: Methodsologymentioning

confidence: 99%

See 1 more Smart Citation

Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey)

Karslı

Atasoy

Yalçın

et al. 2008

Environ Monit Assess

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Various natural hazards such as landslides, avalanches, floods and debris flows can result in enormous property damages and human casualties in Eastern Black Sea region of Turkey. Mountainous topographic character and high frequency of heavy rain are the main factors for landslide occurrence in Ardesen, Rize. For this reason, the main target of the present study is to evaluate the landslide hazards using a sequence of historical aerial photographs in Ardesen (Rize), Turkey, by Photogrammetry and Geographical Information System (GIS). Landslide locations in the study area were identified by interpretation of aerial photographs dated in 1973 and 2002, and by field surveys. In the study, the selected factors conditioning landslides are lithology, slope gradient, slope aspect, vegetation cover, land class, climate, rainfall and proximity to roads. These factors were considered as effective on the occurrence of landslides. The areas under landslide threat were analyzed and mapped considering the landslide conditioning factors. Some of the conditioning factors were investigated and estimated by employing visual interpretation of aerial photos and topographic data. The results showed that the slope, lithology, terrain roughness, proximity to roads, and the cover type played important roles on landslide occurrence. The results also showed that degree of landslides was affected by the number of houses constructed in the region. As a consequence, the method employed in the study provides important benefits for landslide hazard mitigation efforts, because a combination of both photogrammetric techniques and GIS is presented.

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“…Different lithological types are affected by different landslide susceptibilities (Dai et al 2001, Yesilnacar & Topal 2005, Nefeslioglu et al 2008). The lithology layer, rasterized from a 1:250.000-scale geological map of the Veneto region by Dal Piaz (1988), consists of 19 rock units (see Tab.…”

Section: Other Geo-environmental Factorsmentioning

confidence: 99%

Multifactor empirical mapping of the protective function of forests against landslide occurrence: statistical approaches and a case study

Cimini¹,

Portoghesi²,

Madonna³

et al. 2016

iForest

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(3-4) , Piermaria Corona (5) Forests are increasingly valued for services beyond timber and non-timber products including land protection with respect to events such as landslides, soil erosion, floods and avalanches. The most important properties of a protective forest are its ecological and mechanical stability. Planning and implementing multifunctional forest management in protective forests is challenging because of the trade-offs and synergies among the many functions of the forest. In this study, a multifactor empirical method is presented for assessing the protective role of forests on a stand scale with respect to landslide occurrence. Multifactor methodologies typically estimate landslide susceptibility exploiting the relationship between past landslide patterns and site characteristics. Two statistical approaches were here applied to assess the probability of landslide occurrence: the weight-of-evidence technique and the logistic regression technique. Statistical analysis was performed on the basis of landslide detachment zone only. The question of how to estimate protective forest function was answered through the comparison of models established with different sets of predicting factors. This study ultimately aims to provide a decision-support tool focused on mapping the potential role of forests in landslideprone areas. A case study from the Italian Alps was considered. The density of landslide detachment outside forest areas proves to be more than twice than that within forest areas.

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Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong

Cited by 500 publications

References 18 publications

Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia

Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia

Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey)

Multifactor empirical mapping of the protective function of forests against landslide occurrence: statistical approaches and a case study

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