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Mr. Fabio Brill
Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam

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0 Natural Hazarads
0 flood risk modelling
0 Remote Sensing & Gis
0 Data Mining and Machine Learning
0 Climate change adaptation and community resilience

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Journal article
Published: 22 May 2021 in Remote Sensing
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Flood masks are among the most common remote sensing products, used for rapid crisis information and as input for hydraulic and impact models. Despite the high relevance of such products, vegetated and urban areas are still unreliably mapped and are sometimes even excluded from analysis. The information content of synthetic aperture radar (SAR) images is limited in these areas due to the side-looking imaging geometry of radar sensors and complex interactions of the microwave signal with trees and urban structures. Classification from SAR data can only be optimized to reduce false positives, but cannot avoid false negatives in areas that are essentially unobservable to the sensor, for example, due to radar shadows, layover, speckle and other effects. We therefore propose to treat satellite-based flood masks as intermediate products with true positives, and unlabeled cells instead of negatives. This corresponds to the input of a positive-unlabeled (PU) learning one-class classifier (OCC). Assuming that flood extent is at least partially explainable by topography, we present a novel procedure to estimate the true extent of the flood, given the initial mask, by using the satellite-based products as input to a PU OCC algorithm learned on topographic features. Additional rainfall data and distance to buildings had only minor effect on the models in our experiments. All three of the tested initial flood masks were considerably improved by the presented procedure, with obtainable increases in the overall κ score ranging from 0.2 for a high quality initial mask to 0.7 in the best case for a standard emergency response product. An assessment of κ for vegetated and urban areas separately shows that the performance in urban areas is still better when learning from a high quality initial mask.

ACS Style

Fabio Brill; Stefan Schlaffer; Sandro Martinis; Kai Schröter; Heidi Kreibich. Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston. Remote Sensing 2021, 13, 2042 .

AMA Style

Fabio Brill, Stefan Schlaffer, Sandro Martinis, Kai Schröter, Heidi Kreibich. Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston. Remote Sensing. 2021; 13 (11):2042.

Chicago/Turabian Style

Fabio Brill; Stefan Schlaffer; Sandro Martinis; Kai Schröter; Heidi Kreibich. 2021. "Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston." Remote Sensing 13, no. 11: 2042.

Journal article
Published: 05 February 2021 in Sustainability
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The inhabitants of Latacunga living in the surrounding of the Cotopaxi volcano (Ecuador) are exposed to several hazards and related disasters. After the last 2015 volcanic eruption, it became evident once again how important it is for the exposed population to understand their own social, physical, and systemic vulnerability. Effective risk communication is essential before the occurrence of a volcanic crisis. This study integrates quantitative risk and semi-quantitative social risk perceptions, aiming for risk-informed communities. We present the use of the RIESGOS demonstrator for interactive exploration and visualisation of risk scenarios. The development of this demonstrator through an iterative process with the local experts and potential end-users increases both the quality of the technical tool as well as its practical applicability. Moreover, the community risk perception in a focused area was investigated through online and field surveys. Geo-located interviews are used to map the social perception of volcanic risk factors. Scenario-based outcomes from quantitative risk assessment obtained by the RIESGOS demonstrator are compared with the semi-quantitative risk perceptions. We have found that further efforts are required to provide the exposed communities with a better understanding of the concepts of hazard scenario and intensity.

ACS Style

Juan Gomez-Zapata; Cristhian Parrado; Theresa Frimberger; Fernando Barragán-Ochoa; Fabio Brill; Kerstin Büche; Michael Krautblatter; Michael Langbein; Massimiliano Pittore; Hugo Rosero-Velásquez; Elisabeth Schoepfer; Harald Spahn; Camilo Zapata-Tapia. Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador. Sustainability 2021, 13, 1714 .

AMA Style

Juan Gomez-Zapata, Cristhian Parrado, Theresa Frimberger, Fernando Barragán-Ochoa, Fabio Brill, Kerstin Büche, Michael Krautblatter, Michael Langbein, Massimiliano Pittore, Hugo Rosero-Velásquez, Elisabeth Schoepfer, Harald Spahn, Camilo Zapata-Tapia. Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador. Sustainability. 2021; 13 (4):1714.

Chicago/Turabian Style

Juan Gomez-Zapata; Cristhian Parrado; Theresa Frimberger; Fernando Barragán-Ochoa; Fabio Brill; Kerstin Büche; Michael Krautblatter; Michael Langbein; Massimiliano Pittore; Hugo Rosero-Velásquez; Elisabeth Schoepfer; Harald Spahn; Camilo Zapata-Tapia. 2021. "Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador." Sustainability 13, no. 4: 1714.

Journal article
Published: 18 July 2020 in Environmental Modelling & Software
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Flood loss estimation models are developed using synthetic or empirical approaches. The synthetic approach consists of what-if scenarios developed by experts. The empirical models are based on statistical analysis of empirical loss data. In this study, we propose a novel Bayesian Data-Driven approach to enhance established synthetic models using available empirical data from recorded events. For five case studies in Western Europe, the resulting Bayesian Data-Driven Synthetic (BDDS) model enhances synthetic model predictions by reducing the prediction errors and quantifying the uncertainty and reliability of loss predictions for post-event scenarios and future events. The performance of the BDDS model for a potential future event is improved by integration of empirical data once a new flood event affects the region. The BDDS model, therefore, has high potential for combining established synthetic models with local empirical loss data to provide accurate and reliable flood loss predictions for quantifying future risk.

ACS Style

Nivedita Sairam; Kai Schröter; Francesca Carisi; Dennis Wagenaar; Alessio Domeneghetti; Daniela Molinari; Fabio Brill; Sally Priest; Christophe Viavattene; Bruno Merz; Heidi Kreibich. Bayesian Data-Driven approach enhances synthetic flood loss models. Environmental Modelling & Software 2020, 132, 104798 .

AMA Style

Nivedita Sairam, Kai Schröter, Francesca Carisi, Dennis Wagenaar, Alessio Domeneghetti, Daniela Molinari, Fabio Brill, Sally Priest, Christophe Viavattene, Bruno Merz, Heidi Kreibich. Bayesian Data-Driven approach enhances synthetic flood loss models. Environmental Modelling & Software. 2020; 132 ():104798.

Chicago/Turabian Style

Nivedita Sairam; Kai Schröter; Francesca Carisi; Dennis Wagenaar; Alessio Domeneghetti; Daniela Molinari; Fabio Brill; Sally Priest; Christophe Viavattene; Bruno Merz; Heidi Kreibich. 2020. "Bayesian Data-Driven approach enhances synthetic flood loss models." Environmental Modelling & Software 132, no. : 104798.

Preprint content
Published: 23 March 2020
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Compound natural hazards, like El Niño events, which trigger torrential rain, mudslides, riverine and flash floods, cause high damage to society. An improved risk management based on reliable risk assessments are urgently needed. However, knowledge about the complex processes leading to El Niño damage is lacking, and so are loss models.  We explore a large dataset of building damage from the coastal El Niño event 2017 in Peru. We use data-mining techniques to analyse data of damage grades of about 180.000 affected houses together with satellite observations and open geo-information. In a first step, we use unsupervised clustering (t-SNE + OPTICS) to separate regions of different dominant processes. Secondly, we train various supervised classification algorithms and create feature importance rankings per cluster, to identify drivers of observed damage for each of these regions. A comparison of different algorithms provides further insights about the potential and limitations of these methods and datasets. Results indicate that topographic wetness is the most important indicator, as selected by the algorithms, when using the entire dataset. Rainfall sum and maximum from TRMM satellite measurements are identified as damage driver despite the coarse spatial resolution. Also urbanity, based on a focal window around the global urban footprint, appears to play a role for the amount of damage. At least a coarse separation of processes is possible: the slope length and steepness, bare soil index, stream power index, and maximum rainfall are dominating the damage processes in lower mountain ranges and canyons, indicating rapid processes. Damage in upper mountain areas seem more influenced by the rainfall sum, local topographic position, and vegetation cover. In the lowlands, topographic wetness is very dominant, indicating ponding water or riverine floods. As opposed to previous work, this study constructs importance rankings based entirely on real observed damage to buildings. It is therefore a step towards data-driven damage assessments for El Niño events.

ACS Style

Fabio Brill; Heidi Kreibich. A data-mining approach to investigate El Niño damage in Peru. 2020, 1 .

AMA Style

Fabio Brill, Heidi Kreibich. A data-mining approach to investigate El Niño damage in Peru. . 2020; ():1.

Chicago/Turabian Style

Fabio Brill; Heidi Kreibich. 2020. "A data-mining approach to investigate El Niño damage in Peru." , no. : 1.

Journal article
Published: 01 January 2020 in Geomatics, Natural Hazards and Risk
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ACS Style

Fabio Brill; Silvia Passuni Pineda; Bruno Espichán Cuya; Heidi Kreibich. A data-mining approach towards damage modelling for El Niño events in Peru. Geomatics, Natural Hazards and Risk 2020, 11, 1966 -1990.

AMA Style

Fabio Brill, Silvia Passuni Pineda, Bruno Espichán Cuya, Heidi Kreibich. A data-mining approach towards damage modelling for El Niño events in Peru. Geomatics, Natural Hazards and Risk. 2020; 11 (1):1966-1990.

Chicago/Turabian Style

Fabio Brill; Silvia Passuni Pineda; Bruno Espichán Cuya; Heidi Kreibich. 2020. "A data-mining approach towards damage modelling for El Niño events in Peru." Geomatics, Natural Hazards and Risk 11, no. 1: 1966-1990.