Debris-flow susceptibility analysis using fluvio-morphological parameters and data mining: application to the Central-Eastern Pyrenees

2013 ◽  
Vol 67 (2) ◽  
pp. 213-238 ◽  
Author(s):  
G. G. Chevalier ◽  
V. Medina ◽  
M. Hürlimann ◽  
A. Bateman
Keyword(s):  
Data Mining ◽  
Debris Flow ◽  
Morphological Parameters ◽  
Susceptibility Analysis ◽  
Eastern Pyrenees ◽  
Central Eastern ◽  
Data Mining Application ◽  
Debris Flow Susceptibility

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

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3451 ◽  
Author(s):  
Usman Salihu Lay ◽  
Biswajeet Pradhan ◽  
Zainuddin Bin Md Yusoff ◽  
Ahmad Fikri Bin Abdallah ◽  
Jagannath Aryal ◽  
...  
Keyword(s):  
Data Mining ◽  
Debris Flow ◽  
Airborne Lidar ◽  
Multivariate Adaptive Regression Splines ◽  
Slope Aspect ◽  
Support Vector ◽  
Mountainous Area ◽  
Topographic Wetness Index ◽  
Conditioning Factors ◽  
Debris Flow Susceptibility

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.

Impact of spatial data uncertainty in global debris flow susceptibility analysis

2021 ◽  
Author(s):  
Laurie Kurilla ◽  
Giandomenico Fubelli
Keyword(s):  
Debris Flow ◽  
Spatial Data ◽  
Debris Flows ◽  
Predisposing Factors ◽  
Susceptibility Analysis ◽  
Factors Associated ◽  
Event Location ◽  
The Impact ◽  
Debris Flow Susceptibility

<p>There are many types and degrees of uncertainty associated with spatial data and processes. </p><p>There are many factors and attributes associated with debris flow analyses which are prone to uncertainty.  For simplicity, in this presentation, only two attributes of debris flow events are investigated along with the impact of their uncertainty on the determination of environmental predisposing factors.    These two attributes, critical to debris flow susceptibility analyses, are landslide classification and event location.  The associated predisposing factors studied herein are lithology, soils, climate, ecophysiographic units, topography, hydrology, and tectonics.</p><p>In a landslide susceptibility analysis, landslide event location accuracy is paramount yet often inaccurately known.  Landslide inventories are often constructed based on mapping from aerial imagery, media reports, and field work by third party sources; and in a data-driven approach to debris flow susceptibility analysis the landslide type is important in modeling the relevant predisposing factors distinctive to each landslide type. </p><p>In a study of global debris flow susceptibility an analysis of the impact between known location and a location accuracy offset, and landslide categorization uncertainty demonstrates the impact of uncertainty in defining the appropriate predisposing factors associated with debris flows.</p><p>This analysis is part of a larger debris flow global susceptibility determination which trains on known debris flow events and the predisposing factors associated with them to identify potential areas that may be susceptible to debris flows.  This study looks at the impact/differences that mis-categorization or location uncertainty have on the determination of predisposing factors, along with methods of conveying uncertainty information. </p>

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

AN EVALUATION OF POST-WILDFIRE DEBRIS FLOW SUSCEPTIBILITY AND HAZARD MODELS

2016 ◽  
Author(s):  
Jordan Carey ◽  
◽  
Nicholas Pinter ◽  
Andrew L. Nichols
Keyword(s):  
Debris Flow ◽  
Hazard Models ◽  
Debris Flow Susceptibility

Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility

2021 ◽  
Vol 106 (1) ◽  
pp. 881-912
Author(s):  
Jingbo Sun ◽  
Shengwu Qin ◽  
Shuangshuang Qiao ◽  
Yang Chen ◽  
Gang Su ◽  
...  
Keyword(s):  
Debris Flow ◽  
Physical Model ◽  
Statistical Methods ◽  
Regional Scale ◽  
The Impact ◽  
Debris Flow Susceptibility
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