Contribution of the International Consortium on Landslides to the implementation of the Sendai Framework for Disaster Risk Reduction: engraining to the Science and Technology Roadmap

Ayala, Irasema Alcántara; Sassa, Kyoji

doi:10.1007/s10346-020-01539-8

Cited by 14 publications

(3 citation statements)

References 23 publications

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“…During the period from 2002 to 2014, a total of 246,768 landslides occurred in China, which led to the injury or death of 17,296 people and direct economic losses of CNY 58.75 billion [5]. It is evident that landslide disasters have imposed constraints on sustainable development [6,7]. The International Consortium on Landslides (ICL) has advocated for harmonizing regulations of the Sendai Landslide Partnerships 2015-2025 and the 2030 Agenda Sustainable Development Goals, among others [7].…”

Section: Introductionmentioning

confidence: 99%

“…It is evident that landslide disasters have imposed constraints on sustainable development [6,7]. The International Consortium on Landslides (ICL) has advocated for harmonizing regulations of the Sendai Landslide Partnerships 2015-2025 and the 2030 Agenda Sustainable Development Goals, among others [7]. Understanding where landslides can occur is, therefore, of great importance for reducing casualties, addressing ecological and economic losses, and promoting sustainable development [8].…”

Section: Introductionmentioning

confidence: 99%

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Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Huang

Zheng³

et al. 2023

Sustainability

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Landslides pose a serious threat to human lives and property. Accurate landslide susceptibility mapping (LSM) is crucial for sustainable development. Machine learning has recently become an important means of LSM. However, the accuracy of machine learning models is limited by the heterogeneity of environmental factors and the imbalance of samples, especially for large-scale LSM. To address these problems, we created an improved random forest (RF)-based LSM model and applied it to Guangdong Province, China. First, the RF-based LSM model was constructed using rainfall-induced landslide samples and 13 environmental factors and by exploring the optimal positive-to-negative and training-to-test sample ratios. Second, the performance of the RF-based LSM model was evaluated and compared with three other machine learning models. The results indicate that: (1) the proposed RF-based model has the best performance with the highest area under curve (AUC) of 0.9145, based on optimal positive-to-negative and training-to-test sample ratios of 1:1 and 8:2, respectively; (2) the introduction of rainfall and global human modification (GHM) can increase the AUC from 0.8808 to 0.9145; and (3) rainfall and topography are two dominant factors in Guangdong landslides. These findings can facilitate landslide risk prevention and serve as a technical reference for large-scale accurate LSM.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Huang

Zheng³

et al. 2023

Sustainability

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“…In the Sendai document, the “understanding of disaster risk” is given highest priority. To achieve this aim, it is necessary “to promote comprehensive surveys on multi‐hazard disaster risks and the development of regional disaster risk assessments and maps, including climate change scenarios.” These efforts resulted in the development of a suite of assessment methods for catastrophic hazard processes (e.g., Alcántara‐Ayala & Sassa, 2021; Sassa et al., 2017), whereupon the focus was more on the individual processes and less on the interactions between different processes. Specifically for the mountain cryosphere, guidelines for the assessment of glacial and permafrost hazards have been produced by a Scientific Standing Group of the International Association of Cryospheric Sciences IACS and the International Permafrost Association IPA (GAPHAZ, 2017).…”

Section: Introductionmentioning

confidence: 99%

Geomorphic Process Chains in High‐Mountain Regions—A Review and Classification Approach for Natural Hazards Assessment

Mani,

Allen,

Evans

et al. 2023

Reviews of Geophysics

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Populations and infrastructure in high mountain regions are exposed to a wide range of natural hazards, the frequency, magnitude, and location of which are extremely sensitive to climate change. In cases where several hazards can occur simultaneously or where the occurrence of one event will change the disposition of another, assessments need to account for complex process chains. While process chains are widely recognized as a major threat, no systematic analysis has hitherto been undertaken. We therefore establish new understanding on the factors that directly trigger or alter the disposition for subsequent events in the chain and derive a novel classification scheme and parameters to aid natural hazard assessment. Process chains in high mountains are commonly associated with glacier retreat or permafrost degradation. Regional differences exist in the nature and rate of sequencing—some process chains are almost instantaneous, while other linkages are delayed. Process chains involving rapid sequences are difficult to predict, and impacts are often devastating. We demonstrate that process chains are triggered most frequently by progressive failures, being the result of gradual landscape weakening and not due to the occurrence of a distinct process. If fluvial processes are part of the process chain the reach (or mobility) of process chains is increased. Increased mobility can also occur if sediment deposition areas along river channels are activated. As climate changes causes glacial environments to transform into sediment‐rich paraglacial and fluvial landscapes, it is expected that the mobility of process chains will increase in the future.

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Landslide Warning Systems in Low-And Lower-Middle-Income Countries: Future Challenges and Societal Impact

Alcántara-Ayala

Garnica-Peña

2023

Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022

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There is a growing body of literature that recognises the importance of warning systems to reduce landslide disaster risk and avoid the occurrence of disasters. Recent developments in landslide disasters around the world have heightened the need for the implementation of Landslide Early Warning Systems (LEWSs) particularly in low-and lower-middle-income countries (LICs and MICs), where levels of vulnerability and exposure are very high. However, no previous study has systematically evaluated the use of LEWSs in LICs and MICs. By means of a systematic review on the scientific literature this chapter explores the ways in which LEWSs have been implemented in LICs and MICs. This research seeks to address the spatial distribution of LEWSs in the world, specifically in LICs and MICs. Special attention is given to reviewing the development of LEWSs in terms of their inclusion in integrated disaster risk reduction (DRR) strategies or as standalone initiatives, and the type of approaches followed, either as top-down or bottom-up. The chapter has three key components: (1) to prepare a search and inclusion criteria strategy for systematic literature review to collect a set of articles on LEWSs using the ISI Web of Science database; (2) to organize the literature review set to extract and analyse quantitative and qualitative data and information on LEWSs in LICs and MICs; and (3) to provide insights on a future LEWSs research agenda concerning critical issues and gaps in the literature and identifying main challenges with high societal impact. A noteworthy remark about this review is that only 12.4% of the total publications that met the specified criteria are from LICs and MICs. These papers address diverse dimensions of LEWSs in different degrees, but despite that, the actual use or implementation of LEWSs was addressed only by five papers. This suggests a potential disadvantage in the development and successful systematic implementation of LEWSs in these countries.

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Contribution of the International Consortium on Landslides to the implementation of the Sendai Framework for Disaster Risk Reduction: engraining to the Science and Technology Roadmap

Cited by 14 publications

References 23 publications

Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Geomorphic Process Chains in High‐Mountain Regions—A Review and Classification Approach for Natural Hazards Assessment

Landslide Warning Systems in Low-And Lower-Middle-Income Countries: Future Challenges and Societal Impact

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