TY - JOUR
A1 - Brill, Fabio Alexander
A1 - Passuni Pineda, Silvia
A1 - Espichan Cuya, Bruno
A1 - Kreibich, Heidi
T1 - A data-mining approach towards damage modelling for El Nino events in Peru
JF - Geomatics, natural hazards and risk
N2 - Compound natural hazards likeEl Ninoevents cause high damage to society, which to manage requires reliable risk assessments. Damage modelling is a prerequisite for quantitative risk estimations, yet many procedures still rely on expert knowledge, and empirical studies investigating damage from compound natural hazards hardly exist. A nationwide building survey in Peru after theEl Ninoevent 2017 - which caused intense rainfall, ponding water, flash floods and landslides - enables us to apply data-mining methods for statistical groundwork, using explanatory features generated from remote sensing products and open data. We separate regions of different dominant characteristics through unsupervised clustering, and investigate feature importance rankings for classifying damage via supervised machine learning. Besides the expected effect of precipitation, the classification algorithms select the topographic wetness index as most important feature, especially in low elevation areas. The slope length and steepness factor ranks high for mountains and canyons. Partial dependence plots further hint at amplified vulnerability in rural areas. An example of an empirical damage probability map, developed with a random forest model, is provided to demonstrate the technical feasibility.
KW - Natural hazard
KW - damage model
KW - residential buildings
KW - data-mining
KW - remote
KW - sensing
KW - open data
Y1 - 2020
U6 - https://doi.org/10.1080/19475705.2020.1818636
SN - 1947-5705
SN - 1947-5713
VL - 11
IS - 1
SP - 1966
EP - 1990
PB - Routledge, Taylor & Francis Group
CY - Abingdon
ER -
TY - JOUR
A1 - Samprogna Mohor, Guilherme
A1 - Hudson, Paul
A1 - Thieken, Annegret
T1 - A comparison of factors driving flood losses in households affected by different flood types
JF - Water resources research
N2 - Flood loss data collection and modeling are not standardized, and previous work has indicated that losses from different flood types (e.g., riverine and groundwater) may follow different driving forces. However, different flood types may occur within a single flood event, which is known as a compound flood event. Therefore, we aimed to identify statistical similarities between loss-driving factors across flood types and test whether the corresponding losses should be modeled separately. In this study, we used empirical data from 4,418 respondents from four survey campaigns studying households in Germany that experienced flooding. These surveys sought to investigate several features of the impact process (hazard, socioeconomic, preparedness, and building characteristics, as well as flood type). While the level of most of these features differed across flood type subsamples (e.g., degree of preparedness), they did so in a nonregular pattern. A variable selection process indicates that besides hazard and building characteristics, information on property-level preparedness was also selected as a relevant predictor of the loss ratio. These variables represent information, which is rarely adopted in loss modeling. Models shall be refined with further data collection and other statistical methods. To save costs, data collection efforts should be steered toward the most relevant predictors to enhance data availability and increase the statistical power of results. Understanding that losses from different flood types are driven by different factors is a crucial step toward targeted data collection and model development and will finally clarify conditions that allow us to transfer loss models in space and time.
Key Points
Survey data of flood-affected households show different concurrent flood types, undermining the use of a single-flood-type loss model Thirteen variables addressing flood hazard, the building, and property level preparedness are significant predictors of the building loss ratio Flood type-specific models show varying significance across the predictor variables, indicating a hindrance to model transferability
KW - Loss modeling
KW - Riverine floods
KW - Surface floods
KW - Groundwater
KW - Levee
KW - breaches
KW - Compound flood event
Y1 - 2020
U6 - https://doi.org/10.1029/2019WR025943
SN - 0043-1397
SN - 1944-7973
VL - 56
IS - 4
PB - American Geophysical Union
CY - Washington
ER -
TY - JOUR
A1 - Krmíček, Lukáš
A1 - Timmerman, Martin Jan
A1 - Ziemann, Martin Andreas
A1 - Sudo, Masafumi
A1 - Ulrych, Jaromir
T1 - 40Ar/39Ar step-heating dating of phlogopite and kaersutite megacrysts from the Železná hůrka (Eisenbühl) Pleistocene scoria cone, Czech Republic
JF - Geologica Carpathica
N2 - (40)A/Ar-39 step-heating of mica and amphibole megacrysts from hauyne-bearing olivine melilitite scoria/tephra from the Zelezna hurka yielded a 435 +/- 108 ka isotope correlation age for phlogopite and a more imprecise 1.55 Ma total gas age of the kaersutite megacryst. The amphibole megacrysts may constitute the first, and the younger phlogopite megacrysts the later phase of mafic, hydrous melilitic magma crystallization. It cannot be ruled out that the amphibole megacrysts are petrogenetically unrelated to tephra and phlogopite megacrysts and were derived from mantle xenoliths or disaggregated older, deep crustal pegmatites. This is in line both with the rarity of amphibole at Zelezna hurka and with the observed signs of magmatic resorption at the edges of amphibole crystals.
KW - Bohemian Massif
KW - Zelezna hurka
KW - Eisenbuhl
KW - argon dating
KW - mica
KW - amphibole
KW - melilitite
Y1 - 2020
U6 - https://doi.org/10.31577/GeolCarp.71.4.6
SN - 1335-0552
SN - 1336-8052
VL - 71
IS - 4
SP - 382
EP - 387
PB - Veda
CY - Bratislava
ER -
TY - JOUR
A1 - Koyan, Philipp
A1 - Tronicke, Jens
T1 - 3D modeling of ground-penetrating radar data across a realistic sedimentary model
JF - Computers & geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology
N2 - Ground-penetrating radar (GPR) is an established geophysical tool to explore a wide range of near-surface environments. Today, the use of synthetic GPR data is largely limited to 2D because 3D modeling is computationally more expensive. In fact, only recent developments of modeling tools and powerful hardware allow for a time-efficient computation of extensive 3D data sets. Thus, 3D subsurface models and resulting GPR data sets, which are of great interest to develop and evaluate novel approaches in data analysis and interpretation, have not been made publicly available up to now.
We use a published hydrofacies data set of an aquifer-analog study within fluvio-glacial deposits to infer a realistic 3D porosity model showing heterogeneities at multiple spatial scales. Assuming fresh-water saturated sediments, we generate synthetic 3D GPR data across this model using novel GPU-acceleration included in the open-source software gprMax. We present a numerical approach to examine 3D wave-propagation effects in modeled GPR data. Using the results of this examination study, we conduct a spatial model decomposition to enable a computationally efficient 3D simulation of a typical GPR reflection data set across the entire model surface. We process the resulting GPR data set using a standard 3D structural imaging sequence and compare the results to selected input data to demonstrate the feasibility and potential of the presented modeling studies. We conclude on conceivable applications of our 3D GPR reflection data set and the underlying porosity model, which are both publicly available and, thus, can support future methodological developments in GPR and other near-surface geophysical techniques.
KW - Applied geophysics
KW - Ground-penetrating radar
KW - 3D modeling
Y1 - 2020
U6 - https://doi.org/10.1016/j.cageo.2020.104422
SN - 0098-3004
SN - 1873-7803
VL - 137
PB - Elsevier
CY - Amsterdam
ER -