Vulnerability assessment of rainfall-induced debris flows in Taiwan

Lu, George Y.; Chiu, Long S.; Wong, David W. S.

doi:10.1007/s11069-006-9105-y

Cited by 31 publications

(13 citation statements)

References 49 publications

(64 reference statements)

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“…Globally, landslides cause thousands of deaths and billions of dollars of damage each year (Robinson 34 and Spieker, 1978; Nilsen et al, 1979;Brabb, 1993;Brabb, 1991;Dilley et al, 2005;Lu et al, 2007). 35 Triggers of landslides include an increase in pore water pressure, earthquake shaking and human 36 activity (Popescu, 2001; Bommer and Rodriguez, 2002;Smith and Petley, 2009).…”

Section: Introduction 33mentioning

confidence: 99%

A systematic review of landslide probability mapping using logistic regression

2015

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11Logistic regression studies which assess landslide susceptibility are widely available in the literature. 12However, a global review of these studies to synthesise and compare the results does not exist. There 13 are currently no guidelines for selection of covariates to be used in logistic regression analysis and as 14 such, the covariates selected vary widely between studies. An inventory of significant covariates 15 associated with landsliding produced from the full set of such studies globally would be a useful aid to 16 the selection of covariates in future logistic regression studies. Thus, studies using logistic regression 17 for landslide susceptibility estimation published in the literature were collated and a database created 18 of the significant factors affecting the generation of landslides. The database records the paper the 19 data were taken from, the year of publication, the approximate longitude and latitude of the study 20 area, the trigger method (where appropriate), and the most dominant type of landslides occurring in 21 the study area. The significant and non-significant (at the 95% confidence level) covariates were 22 recorded, as well as their coefficient, statistical significance, and unit of measurement. The most 23 common statistically significant covariate used in landslide logistic regression was slope, followed by 24 aspect. The significant covariates related to landsliding varied for earthquake-induced landslides 25 compared to rainfall-induced landslides, and between landslide type. More importantly, the full range 26 of covariates used was identified along with their frequencies of inclusion. The analysis showed that 27 2 there needs to be more clarity and consistency in the methodology for selecting covariates for logistic 28 regression analysis and in the metrics included when presenting the results. Several recommendations 29 for future studies were given. 30 31

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Section: Introduction 33mentioning

confidence: 99%

A systematic review of landslide probability mapping using logistic regression

2015

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“…Heuristic models are based on expertise and knowledge as well as interpretation of remote sensing data and field data to create a hazard assessment scoring system. The nature of the process means that the result can be potentially unreliable due to subjectivity involved in the process (Lu et al 2007). The study was based in two separate watersheds both of which are prone to disastrous landslide events.…”

Section: Landslidesmentioning

confidence: 99%

Applications of geographical information systems and remote sensing in natural disaster hazard assessment and mitigation in Taiwan

Merrett

Chen

2013

Geomatics, Natural Hazards and Risk

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Geographic Information Systems (GISs) provide a powerful tool in managing and analysing spatial data. Geographic Information System has been applied successfully to large variety of fields, one field of particular interest is the field of disaster mitigation. The island of Taiwan is well known for the typhoons and earthquakes which in recent past has claimed lives and caused significant damage. It is well understood that hazard identification and land management play a major part in the reducing the impact of natural disasters. This is a role GIS is well suited to especially when combined with remote sensing data. This concept has been used throughout the world for purposes including identification of risk areas, real-time modelling of events and scenario modelling. Where this has the greatest potential in Taiwan is in the steep and rugged mountain areas of the Central Mountain Range which are sparsely populated and often difficult to access making conventional ground-based investigations impractical over large areas. Geographic Information System combined with remote sensing data provides the ability to effectively analyse various risk factors over large areas or inaccessible areas to identify localities which have the potential to be at higher risk from the impact of natural disasters.

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“…Even within the same watershed, different locations might have distinct terrain characteristics. The factors we considered in this study can be referred to previous works (Lu et al, 2007;Wan et al, 2008), that is, creek length (km), size of subbasin area (km 2 ), slope (degree) and form factor. The creek length is an important geomorphologic factor for predicting debris flows.…”

Section: Analysis Of Geomorphologic Factorsmentioning

confidence: 99%

Investigating the impact of the Chi‐Chi earthquake on the occurrence of debris flows using artificial neural networks

Chang

Chiang

Lee

2009

Hydrological Processes

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Abstract:Debris flows have caused enormous losses of property and human life in Taiwan during the last two decades. An efficient and reliable method for predicting the occurrence of debris flows is required. The major goal of this study is to explore the impact of the Chi-Chi earthquake on the occurrence of debris flows by applying the artificial neural network (ANN) that takes both hydrological and geomorphologic influences into account. The Chen-Yu-Lan River watershed, which is located in central Taiwan, is chosen for evaluating the critical rainfall triggering debris flows. A total of 1151 data sets were collected for calibrating model parameters with two training strategies. Significant differences before and after the earthquake have been found: (1) The size of landslide area is proportioned to the occurrence of debris flows; (2) the amount of critical rainfall required for triggering debris flows has reduced significantly, about half of the original critical rainfall in the study case; and (3) the frequency of the occurrence of debris flows is largely increased. The overall accuracy of model prediction in testing phase has reached 96Ð5%; moreover, the accuracy of occurrence prediction is largely increased from 24 to 80% as the network trained with data from before the Chi-Chi earthquake sets and with data from the lumped before and after the earthquake sets. The results demonstrated that the ANN is capable of learning the complex mechanism of debris flows and producing satisfactory predictions.

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Vulnerability assessment of rainfall-induced debris flows in Taiwan

Cited by 31 publications

References 49 publications

A systematic review of landslide probability mapping using logistic regression

A systematic review of landslide probability mapping using logistic regression

Applications of geographical information systems and remote sensing in natural disaster hazard assessment and mitigation in Taiwan

Investigating the impact of the Chi‐Chi earthquake on the occurrence of debris flows using artificial neural networks

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