Integrating expert opinion with modelling for quantitative multi-hazard risk assessment in the Eastern Italian Alps

Chen, Lixia; Westen, C.J. van; Hussin, Haydar; Ciurean, Roxana; Turkington, Thea; Chavarro-Rincon, D.C.; Shrestha, Dhruba Pikha

doi:10.1016/j.geomorph.2016.07.041

Cited by 56 publications

(36 citation statements)

References 40 publications

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“…Extreme hydro-meteorological conditions, triggered by climate change thereby, increase the susceptibility of mountainous areas toward landslide hazards (Chen et al, 2016), which has become more devastating as the frequency and severity of the hazard had increased than before (Kanungo, Arora, Sarkar, & Gupta, 2009). Anthropogenic activities such as rapid unplanned urbanization and deforestation also combine with geological and geomorphological aspects that contribute to the occurrence of the landslide (Horelli, 2005;Van Westen, 2000).…”

Section: Introductionmentioning

confidence: 99%

Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: a case study of Rangamati district in Bangladesh

Sifa

Mahmud

Tarin

et al. 2019

Geology, Ecology, and Landscapes

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Landslide hazard of 2017 in Rangamati district had devastating impacts on development, thereby making landslide susceptibility mapping a prerequisite for disaster risk management. This study aims to map the future landslide susceptible areas by overlaying the landslide inventory of 2017 with causative factor maps using WoE and MFR and compare their results to determine that statistical model describes the susceptibility of the landslide occurrence better than the other. The analysis shows that although both models define the spatial relationship of past landslides with the triggering factors in a same way but in case of mapping, MFR had overestimated the high and low susceptible areas and underestimated the moderate susceptible areas than WoE. When validated from success rate curve by plotting the percentage of landslide susceptibility index rank against the percentage of cumulative landslide occurrence, it shows that the WoE model describes the landslides better than the MFR model. About 20% of the high susceptible areas include 85% of the total landslide area in case of the WoE model but the MFR model includes only 20%. On the other hand, the WoE model describes that 30% highly susceptible area covers more than 99% of the total landslide area while MFR defines only 78%.

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Section: Introductionmentioning

confidence: 99%

Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: a case study of Rangamati district in Bangladesh

Sifa

Mahmud

Tarin

et al. 2019

Geology, Ecology, and Landscapes

View full text Add to dashboard Cite

show abstract

“…Meanwhile, the risk results cannot be tested because of the lack of historical damage data in the area, which is a common difficulty in China and other researcher's areas (Ghosh et al, 2011;Chen et al, 2016b). Damage data is also crucial for vulnerability analysis.…”

Section: Other Limitations In Risk Resultsmentioning

confidence: 99%

“…Theoretically, the definition of hazard scenarios as described by Chen et al (2016b).should be based on the analysis of landslide occurrences and triggering events. due to the incomplete information on landslide date in the community, it is challenging to build the relationship between landslide return period and triggering factor (rainfall in this case study).…”

Section: Discussion On Landslide Hazard Assessmentmentioning

confidence: 99%

Community-based landslide hazard probability and risk assessment: A case in west Hubei, China

Chen

Woldai

et al. 2019

Preprint

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Abstract. Small communities living in high mountainous terrains, in Hubei Province are often impacted by landslide hazard. Past work by China Geology Survey focused only on hazard assessment at 1 : 100 000 scales. In this study, we conducted a more-detailed semiquantitative landslide and risk assessment at a community level and scale of 1 : 10 000. We applied the probabilistic method to assess the landslide spatial, temporal and size probabilities while the landslide hazard and risk assessment were considered for four return periods (5, 10, 20 and 50 years) and two size scenarios (landslide volume). The spatial probability by susceptibility mapping with an accuracy of 84 % indicates that Quaternary deposits and weathered eluvium from Ordovician limestone are the two major controlling factors. Most building areas in hazard maps are located at the foot of major slopes where hazard probabilities are very high. We computed the loss of lives and properties for each slope. The result shows that 1530 people and 126 million RMB economics were at risk of being affected by landslides with a 50-year return period and a landslide volume of fifty thousand cubic meters. Meanwhile, the longer the return period, the higher the hazard probability is. Compared with the function by ordinary least square method, classic inverse gamma and power law distribution of landslide magnitude and frequency are not suitable for landslide size probability analysis in the study area. The proposed procedure is proved to be more useful to complement risk assessment on the small scale of 100 000 in west Hubei, China.

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“…In all these studies, scores and weights provided by the experts had great importance, and it was stated by the researchers that they affected the accuracy of the results. Chen et al [28] considered debris flows and river and flash flooding to be common in one area. These hazard types were examined in four scenarios (major, moderate, minor, and frequent events).…”

Section: Background On Multi-hazard Assessmentmentioning

confidence: 99%

Use of Mamdani Fuzzy Algorithm for Multi-Hazard Susceptibility Assessment in a Developing Urban Settlement (Mamak, Ankara, Turkey)

Yanar

Kocaman

Gökçeoğlu

2020

IJGI

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Urban areas may be affected by multiple hazards, and integrated hazard susceptibility maps are needed for suitable site selection and planning. Furthermore, geological–geotechnical parameters, construction costs, and the spatial distribution of existing infrastructure should be taken into account for this purpose. Up-to-date land-use and land-cover (LULC) maps, as well as natural hazard susceptibility maps, can be frequently obtained from high-resolution satellite sensors. In this study, an integrated hazard susceptibility assessment was performed for a developing urban settlement (Mamak District of Ankara City, Turkey) considering landslide and flood potential. The flood susceptibility map of Ankara City was produced in a previous study using modified analytical hierarchical process (M-AHP) approach. The landslide susceptibility map was produced using the logistic regression technique in this study. Sentinel-2 images were employed for generating LULC data with the random forest classification method. Topographical derivatives obtained from a high-resolution digital elevation model and lithological parameters were employed for the production of landslide susceptibility maps. For the integrated hazard susceptibility assessment, the Mamdani fuzzy algorithm was considered, and the results are discussed in the present study. The results demonstrate that multi-hazard susceptibility assessment maps for urban planning can be obtained by combining a set of expert-based and ensemble learning methods.

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Integrating expert opinion with modelling for quantitative multi-hazard risk assessment in the Eastern Italian Alps

Cited by 56 publications

References 40 publications

Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: a case study of Rangamati district in Bangladesh

Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: a case study of Rangamati district in Bangladesh

Community-based landslide hazard probability and risk assessment: A case in west Hubei, China

Use of Mamdani Fuzzy Algorithm for Multi-Hazard Susceptibility Assessment in a Developing Urban Settlement (Mamak, Ankara, Turkey)

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