A New Procedure for Selecting and Ranking Ground-Motion Prediction Equations (GMPEs): The Euclidean Distance-Based Ranking (EDR) Method

Kale, Özkan; Akkar, Sinan

doi:10.1785/0120120134

Cited by 126 publications

(75 citation statements)

References 44 publications

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“…Models 2012AWW and 2004AD remain the highest ranks under both wMAE and IGN, implying robustness of their good performance. Other scores proposed for GMPE validations (e.g., Scherbaum et al, 2004;Kale and Akkar, 2013;Akkar and Kale, 2014;Mak et al, 2014) are not implemented in this study because they are not as popular as the IGN.…”

Section: Validating Intensity Prediction Equations For Italy By Obsermentioning

confidence: 99%

Validating Intensity Prediction Equations for Italy by Observations

Mak¹,

Clements²,

Schorlemmer³

2015

Bulletin of the Seismological Society of America

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The engineering seismology community has recently recognized the importance of validating the performance of predictive models for seismic hazard by independent observations, yielding a number of studies on the relative performance of ground-motion prediction equations. The validation of intensity prediction equations (IPEs) has attracted less attention. We fill this gap by validating eight Italian IPEs plus one global IPE using five sets of Italian macroseismic intensity data, of which three are prospective and two retrospective to the models. We implemented multiple scoring methods to validate the models and found that the simple score of mean absolute error is sufficient to measure the general model performance. Good models consistently perform well under multiple methods and datasets, showing robustness. Models with physical functional forms are found to perform better. The global IPE performed well for Italian data, implying insignificant regional differences for IPEs. This result encourages grouping intensity data collected from multiple geographic regions, both from the Internet and traditional surveys, into a larger dataset for the use of future model development and validation.

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Section: Validating Intensity Prediction Equations For Italy By Obsermentioning

confidence: 99%

Validating Intensity Prediction Equations for Italy by Observations

Mak¹,

Clements²,

Schorlemmer³

2015

Bulletin of the Seismological Society of America

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“…The concept of a logic tree is to capture the epistemic uncertainty in a model, and thus the selected IPEs should best describe the ground motion observed in the region, while at the same time avoiding redundancy. Which models are chosen for implementation is often based on the visual analysis of data fits or on the application of ranking methods (e.g., as proposed by Scherbaum et al, 2009, andKale andAkkar, 2013). The problem with this approach is that simply choosing the best ranking or best-fitting models might lead to the selection of very similar and therefore dependent models (Scherbaum et al, 2010).…”

Section: Macroseismic Intensity Predictionmentioning

confidence: 99%

“…In the first step, we clustered available attenuation relations by similarity using a k-means cluster method (Steinhaus, 1956) on IPE estimates over a discrete parameter space. Then, in the second step, we applied ranking methods (Scherbaum et al, 2009;Kale and Akkar, 2013) to our collected intensity data to select a single representative model from each of the resulting clusters. We Determined from the 100 events closest to the points (longitude, latitude) given in the table.…”

Section: Macroseismic Intensity Predictionmentioning

confidence: 99%

“…First, we cluster a set of 10 candidate IPEs to identify groups of models that can be considered similar with respect to the distribution of the predicted values for a selection of the explanatory variables (i.e., magnitude and distance), using a k-mean approach (Steinhaus, 1956). Then, we apply different ranking techniques (Scherbaum et al, 2009;Kale and Akkar, 2013) to identify within each cluster the most suitable model to be included in the logic tree. The resulting hazard models are consistent with existing probabilistic seismic-hazard models for the region.…”

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confidence: 99%

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Data‐Driven Seismic‐Hazard Models Prepared for a Seismic Risk Assessment in the Dead Sea Region

Haas

Agnon

Bindi

et al. 2016

Bulletin of the Seismological Society of America

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We derive a probabilistic seismic-hazard model for the Dead Sea region to allow for seismic risk estimation, which will be part of a subsequent study. This hazard model relies as much as possible on data-driven approaches, utilizing a seismic catalog compiled for the region by integrating data from different sources. We derive seismicity models using two different smoothing approaches and estimate a hypocentral depth distribution from historical observations. We do not include paleoseismological evidence apart from the observed seismicity. Because ground-motion records are sparse in the region, we formulate the model in the European Macroseismic intensity scale from 1998. However, the collected macroseismic intensity data are still too few to derive a local intensity prediction equation (IPE). Thus, we choose among existing equations derived for different regions and combine them in a logic tree. Here, motivated by Scherbaum et al. (2010), we propose a two-step approach to select the IPEs for the logic tree. First, we cluster a set of 10 candidate IPEs to identify groups of models that can be considered similar with respect to the distribution of the predicted values for a selection of the explanatory variables (i.e., magnitude and distance), using a k-mean approach (Steinhaus, 1956). Then, we apply different ranking techniques (Scherbaum et al., 2009;Kale and Akkar, 2013) to identify within each cluster the most suitable model to be included in the logic tree. The resulting hazard models are consistent with existing probabilistic seismic-hazard models for the region. We estimate a moderate-to-high hazard of intensity grade VII-VIII with 10% probability of exceedance within 50 years in close vicinity to the Dead Sea transform fault, the dominant seismogenic structure in this region.

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“…"CU= OQm}wQ u}=Q@=v@ "CU= QwmPt VwQ |=yha[ R= VQ}PB |xv=DU; u}}aD , qFt x}rw= [7] "CU= xOt; O}OB xOWQmP |=yha[ Q@ x@re |= Q@ LLH |D=aq]=-|Q_v R= uDiQo s=yr= =@ w LLH w LH |}=tvCUQO Q@ |vD@t VwQ wO 'u}= Q@ xwqa |UO}rk= |xrY=i Q@ |vD@t |WwQ u= QoWywSB '=yxOv=t}k=@ p}rLD |=yVwQ Q}=U h= QLv= |Q}oxR=Ov= w u}tR |=yCmQL C}a]k sOa x@ xHwD =@ |Ov@x@DQ |= Q@ = Q |@=} RQ= CyH Q=@ u}rw= |= Q@ xm Ov=xOQm |iQat |v}tND w |D=Oy=Wt |=yxO=O u}@ R= |}=yCW=ov?=DW T=U= Q@ xv=}tQw=N w =B wQ= x@ \w@Qt Q}N= |oOvy=m \@=wQ Q}N= |=yp=U QO u}vJty [8] "CU= xOW xO=iDU= u=DUm=B w uOQ= 'R=kik 'u= Q}= 'x}mQD |wQ Q@ |oOvy=m \@=wQ |Ov@x@DQ w ?=NDv= |xv}tR QO |at=H Cq=kt u= QoWywSB ?NDvt |v=yH w |rLt |oOvy=m \@=wQ T=U= Q@ XNWt |xO=O xawtHt l} xawtHt =yu; |xty QO xm Ov=xOQm x = Q= uQOt w l}Uqm |=yVwQ |Q}oQ=mx@ =@ 'p=Lu}= =@ …”

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confidence: 99%