MONITOR: Hazard monitoring for risk assessment and risk communication

Mayer, R. Samuel; Plank, Claudia; Böhner, Alexander; Kollarits, Stefan; Corsini, Alessandro; Ronchetti, Francesco; Siegel, H.; Noessing, L.; Mair, Volkmar; Sulzenbacher, U.; Tosoni, D.; Cimarosto, S.; Zanco, A.; Todorov, S.; Krastev, L.; Wergles, Nathalie; Gasperl, Wolfgang; Mayerl, Markus; Toli, T.; Haradalia, H.; Koutsias, Nikos; Kreuzer, Simon; Liehr, C.; Rachoy, Christian; Papez, J.; Jindra, P.

doi:10.1080/17499510802506139

Cited by 7 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This quantification can facilitate risk management (Ho and Ko 2009), in the identification of regions prone to severe debris and the making of policies for mitigation (Carmela and Mario Parise 2022). For example the decision of planting more vegetation that fits the conditions of the region to strengthen the soil or deciding an appropriate activity to be done in a given region to improve stability ,safety and efficient land use (Mayer et al 2008) .…”

Section: Discussion and Model Suitability Assessment For Landslide-in...mentioning

confidence: 99%

Prediction of landslide induced debris’ severity using machine learning algorithms: a case of South Korea

Jérémie,

Yune,

Kim

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. Rainfall-induced landslides frequently occur in the mountainous region of the Korean peninsula. The resulting landslide-induced debris causes extreme property damage, huge financial losses, and human deaths. To mitigate their effect different landslide susceptibility mapping is frequently used. However, these methods do identify regions with potential landslides but they do not quantify their severity. In this paper, multi-category ordered machine models, namely, proportional odd logistic regression (POLR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (EGB) methods, are proposed to fill the specified gap. Moreover, the exploratory data analysis on the landslide-induced debris dataset has been conducted to examine patterns and relationships between landslide-induced debris severity(size), causal factors(rainfall), and influencing factors. Findings revealed that cumulative three days’ rainfall and slope length were most responsible for the severity of landslide-originated debris severity and slopes between 20° to 40° were identified as the most vulnerable region. Furthermore, the predictive accuracy statistics were compared to assess the suitable model for debris severity for the Korean case. The RF and EGB ranked higher with an overall accuracy of 90.07 % and 86.09 % and kappa of 0.72 and 0.61 on the validation set, respectively. The findings of this research may be useful in the identification of high-risk zones for extreme rainfall-induced debris to elaborate mitigation and resilience policies, post-disaster rehabilitation planning, and land use management.

show abstract

Section: Discussion and Model Suitability Assessment For Landslide-in...mentioning

confidence: 99%

Prediction of landslide induced debris’ severity using machine learning algorithms: a case of South Korea

Jérémie,

Yune,

Kim

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For rapid mitigation against seismic hazards, automated alerting technology based on seismic networks EEWs is used, with automated event estimation employed to facilitate the issuing of timeous public alerts (Allen and Melgar, 2019;Velazquez et al, 2020). Such systems operate at national or local government levels, as their fundamental technology requires 24 h monitoring (Mayer et al, 2008). Several countries have implemented EEW systems as public services, including Japan (Hoshiba and Ozaki, 2014), Republic of Korea (Lee et al, 2022), Taiwan (Hsiao et al, 2009), and the United States (Kohler et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Assessing network-based earthquake early warning systems in low-seismicity areas

Ahn,

Cho,

Hwang

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

Earthquake early warning (EEW) technology, designed to alert the public of earthquake risks after initial P-wave detection but before the onset of strong tremors, has developed rapidly. Methodologies from various fields are combined in EEW systems to estimate earthquake locations, magnitudes, and expected intensities based on the initial P-wave data. These systems operate automatically because prompt responses are required. However, as no common evaluation framework for EEW system verification exists, potentially divergent evaluations from reviewers or countries could ensue. Moreover, evaluating EEW systems is more complicated when the target area does not experience frequent earthquakes. We aimed to establish a guidance review process for low-seismicity areas to ensure reliable and stable integrated EEW system operation. We incorporated management aspects through actual system operator surveillance and designed an EEW assessment process based on feedback from our surveys. Using this approach, we created a comprehensive and well-informed evaluation process that considers the diverse perspectives of experts involved in EEWs. Our proposed assessment method allows for a uniform and consistent evaluation process, regardless of changes in the methods or technologies used by EEW systems. The method aims to guide EEW system assessments in low-seismicity areas.

show abstract

“…This situation may be the main cause of flooding in the Siret and Prut river basins in NE Romania (Petrisor, Petre, & Meita, 2016;Romanescu & Nistor, 2011). To minimize the negative consequences of these catastrophic events, decision makers and authorities need accurate information regarding the geographic extent of the areas at risk so that they can apply comprehensive and innovative risk management measures (Gitas, Polychronaki, Katagis, & Mallinis, 2008;Mayer et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Historic flood events in NE Romania (post-1990)

et al. 2017

View full text Add to dashboard Cite

Using open-source satellite imagery like Landsat TM, ETM+ and Sentinel 2 can lead to accurate cartographic products. We mapped flood events from Siret and Prut river basins in the last 30 years based on the availability of Landsat data archive. In this area were recorded historical values in flow rates for the entire Romanian territory: 4650 m³/s on the Siret River in 2005 -the maximum value ever recorded for Romania; 4240 m³/s on the Prut in 2008 -second maximum value recorded for Romania. The most powerful floods that took place in Romania in the last years were in

show abstract

MONITOR: Hazard monitoring for risk assessment and risk communication

Cited by 7 publications

References 2 publications

Prediction of landslide induced debris’ severity using machine learning algorithms: a case of South Korea

Prediction of landslide induced debris’ severity using machine learning algorithms: a case of South Korea

Assessing network-based earthquake early warning systems in low-seismicity areas

Historic flood events in NE Romania (post-1990)

Contact Info

Product

Resources

About