Debris Flow Susceptibility Assessment Using Airborne Laser Scanning Data

Pradhan, Biswajeet; Kalantar, Bahareh; Abdulwahid, Waleed M.; Dieu, Bui Tien

doi:10.1007/978-3-319-55342-9_14

Cited by 5 publications

(6 citation statements)

References 35 publications

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“…Studies such as [11,55,115,160,197,198,199] have confirmed the significance of slope aspect, profile curve, total curve and slope gradient to geo-hazard modelling. They observed the significant contribution of these conditioning factors (slope angle, plan curvature and flow accumulation) to debris flow initiation.…”

Section: Discussionmentioning

confidence: 94%

“…[47]. The geological formation comprises of granites of various categories of megacrystic biotite and associated micro-granite (muscovite) [48,55,131] and meta-sediments comprising of slate, (Sedimentary rock) phyllite, limestone, sandstone and schist (metamorphic rock) [61]. The mean annual rainfall records range from 2500 to 3000 mm per annum.…”

Section: Methodsmentioning

confidence: 99%

“…There are two raining seasons in the study area, which include; the September to December regime and the February to May regime but the peaks are recorded between March to May and November to December respectively. During the peak period, rainfall of between 88 mm to 100 mm in a single day usually triggers landslides [6,11,50,52,55]. When the local rainfall rate exceeds a distinctive intensity threshold, a runoff-generated debris flow may likely be initiated.…”

Section: Methodsmentioning

confidence: 99%

“…The 640 points were labelled 1′s and 0′s for the classification process where the former indicates the presence of debris flow (in the past) and the latter represent the absence of debris flow [55]. For each point location, corresponding cell values of the normalized conditioning factor datasets were extracted using multi-values to point tools in ArcGIS 10.6 software and randomly split into training and test datasets using ratio 70% (448) to 30% (192) to develop and validate the resulting debris flow susceptibility model [38,55,78,104,111,112,115,154,173,174,175]. As earlier mentioned, MARS and SVR data mining techniques were adopted and these MLAs often accept binary classifications [107].…”

Section: Methodsmentioning

confidence: 99%

“…In Peninsular Malaysia, LiDAR technology has been used for landslide hazard mapping, modeling and other related phenomena e.g., [6,47,48,49,50,51,52]. However, only very few studies have been carried out on debris flow susceptibility mapping using LiDAR data in this region e.g., [11,47,53,54,55,56,57]. LiDAR DEM dataset allows retrieving primary and secondary landslides conditioning factors to predict susceptible and hazard zones.…”

Section: Introductionmentioning

confidence: 99%

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Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Lay

Pradhan

Yusoff

et al. 2019

Sensors

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Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.

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Section: Discussionmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Lay

Pradhan

Yusoff

et al. 2019

Sensors

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Identification of Debris Flow Initiation Zones Using Topographic Model and Airborne Laser Scanning Data

Lay

Pradhan

2018

Gcec 2017

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Empirical multivariate predictive models represent an important tool to estimate debris flow initiation areas. Most of the approaches used in modelling debris flows propagation and deposit phases required identifying release (starting point) area or source area. Initiation areas offer a good overview to point out where field investigation should be conducted to establish a detailed hazard map. These zones, usually, are arbitrarily chosen which affect the model outputs; hence, there is a need to have accurate and automated means of identifying the release area. In addition to this, the resolution of the terrain dataset also affects the results of the detection of source areas. In this study, airborne laser scanning (ALS) data was used because of its robustness in providing detailed terrain attributes at high resolution. Primary and secondary conditioning parameters were derived from digital elevation model (DEM) as input into the modelling process. Three models were executed at different spatial resolution scales: 5, 10 and 15 m, respectively. MARSpline multivariate data mining predictive approach was implemented using morphometric indices and topographical derived parameter as independent variables. A statistics validation was calculated to estimate the optimal pixel size, 1200 randomly sample data were generated from existing inventory data. Debris flows and no-debris flows were categorized, and the transform to continuous integer (1 and 0), respectively. To achieve this, the data set was divided into two, 70% (840) for the training dataset and 30% (360) for validation. The best model was selected based on the model performance using the generalized cross validation (GCV) and the receiver operating characteristic (ROC) curve/area under curve (AUC) values. Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm). The three most influencing topographic parameters identified are topographic roughness index (TRI), slope angle, and specific catchment area (SCA) with the percentage values of participation in the model of 100, 93, and 86%, respectively. The chosen function appeared to describe the analysed correlation sufficiently well. Consequently, three stages of optimization were made to determine the optimized source areas is possible with 10 m pixel size, 200 maximum basis functions and 3 maximum interactions, resulting into 82% ROC train and 80% test, GCV 0.189 and 85% correlation coefficient. The result will be of great contribution to the advancement of a broad understanding of the dynamics of debris flows hazard and mitigations at regional level which; that is resourceful for comprehensive slope management for safe urban planning decisionmaking process and debris flow disaster management.

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Geomorphic controls on debris flow activity in the paraglacial zone of the Southeast Tibetan Plateau

Wang

Jiang

et al. 2023

Nat Hazards

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On the southeastern Tibetan Plateau, which is an area widely covered by alpine glaciers, two types of debris flow generally occur: glacier-related debris flows (GDFs) and rainfall-related debris flows (RDFs). It is widely accepted that topographic conditions influence debris flow activities; however, few studies have examined the differences between such influence on GDFs and RDFs. This study investigated the GDFs and RDFs in the periglacial area of the Parlung Tsangpo Basin, and calculated 12 geomorphic indexes to reveal the topographic features associated with these two types of debris flow. It was found revealed that lower values in the drainage area, main channel length, and relative elevation occurred in RDFs compared to the GDFs, whereas higher values in the channel gradient, relief ratio, and effective basin area appeared in RDFs. The discrepancy is mainly related to the different topographic and geomorphic shaping of modern glaciers. According to its geomorphological characteristics, the Parlung Tsangpo Basin can be divided into three sections: the upper V-shaped canyon section, middle wide valley section, and lower steep canyon section. The scale and frequency of debris flows in the upstream canyon region are substantially lower than those of debris flows in the downstream canyon region. Moreover, the frequency and scale of RDFs are substantially different to those of GDFs, primarily because of the different geomorphic evolutionary stages of debris flows gullies in different regions.

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Debris Flow Susceptibility Assessment Using Airborne Laser Scanning Data

Cited by 5 publications

References 35 publications

Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Identification of Debris Flow Initiation Zones Using Topographic Model and Airborne Laser Scanning Data

Geomorphic controls on debris flow activity in the paraglacial zone of the Southeast Tibetan Plateau

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