Satellite precipitation–based extreme event detection for flood index insurance

2021 ◽  
Vol 55 ◽  
pp. 102108
Author(s):  
Annibale Vecere ◽  
Mario Martina ◽  
Ricardo Monteiro ◽  
Carmine Galasso
Keyword(s):  
Event Detection ◽  
Extreme Event ◽  
Index Insurance ◽  
Satellite Precipitation ◽  
Flood Index

Spatio-Temporal Gaussianization Flows for Extreme Event Detection

2021 ◽  
Author(s):  
Miguel-Ángel Fernández-Torres ◽  
J. Emmanuel Johnson ◽  
María Piles ◽  
Gustau Camps-Valls
Keyword(s):  
Event Detection ◽  
Land Surface ◽  
Extreme Event ◽  
Learning Approaches ◽  
Proposed Model ◽  
Individual Contributions ◽  
Current Context ◽  
Spatio Temporal ◽  
The Individual ◽  
Anomaly Score

<p>Automatic anticipation and detection of extreme events constitute a major challenge in the current context of climate change. Machine learning approaches have excelled in detection of extremes and anomalies in Earth data cubes recently, but are typically both computationally costly and supervised, which hamper their wide adoption. We alternatively present here an unsupervised, efficient, generative approach for extreme event detection, whose performance is illustrated for drought detection in Europe during the severe Russian heat wave in 2010. The core architecture of the model is generic and could naturally be extended to the detection of other kinds of anomalies. First, it computes hierarchical appearance (spatial) and motion (temporal) representations of several informative Essential Climate Variables (ECVs), including soil moisture, land surface temperature, as well as features describing vegetation health. Then, these representations are combined using Gaussianization Flows that yield a spatio-temporal anomaly score. This allows the proposed model not only to detect droughts areas, but also to explain why they were produced, monitoring the individual contributions of each of the ECVs to the indicator at its output.</p>

scholarly journals Detecting impacts of extreme events with ecological in situ monitoring networks

2017 ◽  
Vol 14 (18) ◽  
pp. 4255-4277 ◽  
Author(s):  
Miguel D. Mahecha ◽  
Fabian Gans ◽  
Sebastian Sippel ◽  
Jonathan F. Donges ◽  
Thomas Kaminski ◽  
...  
Keyword(s):  
Extreme Events ◽  
Event Detection ◽  
Extreme Event ◽  
Decay Rates ◽  
In Situ Monitoring ◽  
Monitoring Networks ◽  
Terrestrial Biosphere ◽  
Detection Rates ◽  
The Impact

Abstract. Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with  ≈  100 randomly placed sites in Europe yield a  ≥  90 % chance of detecting the eight largest (typically very large) extreme events; but only a  ≥  50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON) reliably detect the largest extremes, but that the extreme event detection rates are not higher than would be achieved by randomly designed networks. Spatio-temporal expansions of ecological in situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor the impacts of such events in the terrestrial biosphere.

scholarly journals Detecting impacts of extreme events with ecological in-situ monitoring networks

2017 ◽  
Author(s):  
Miguel D. Mahecha ◽  
Fabian Gans ◽  
Sebastian Sippel ◽  
Jonathan F. Donges ◽  
Thomas Kaminski ◽  
...  
Keyword(s):  
Extreme Events ◽  
Event Detection ◽  
Extreme Event ◽  
Decay Rates ◽  
In Situ Monitoring ◽  
Monitoring Networks ◽  
Terrestrial Biosphere ◽  
Detection Rates ◽  
The Impact

Abstract. Extreme hydrometeorological conditions typically impact ecophysiological processes of terrestrial vegetation. Satellite based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in-situ observations. The question is if the density of existing ecological in-situ networks is sufficient for analyzing the impact of extreme events, or what are expected event detection rates of ecological in-situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small network, but also reveal a linear decay of detection probabilities towards smaller extreme events in log-log space. For instance, networks with ≈ 100 randomly placed sites in Europe yield a ≥ 90 % chance of detecting the largest 8 (typically very large) extreme events; but only a ≥ 50 % chance of capturing the largest 39 events. These finding are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation issues and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in-situ networks can capture extreme events of a given size. Consistent with our theoretic considerations, we find that today's systematic network designs (i.e. NEON) reliably detects the largest extremes. But the extreme event detection rates are not higher than they would be achieved by randomly designed networks. Spatiotemporal expansions of ecological in-situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor their impacts in the terrestrial biosphere.

scholarly journals Extreme event detection in near-wall turbulence using reflection-encoded readout of micropillar arrays

PAMM ◽  
2015 ◽  
Vol 15 (1) ◽  
pp. 475-476
Author(s):  
F. Hegner ◽  
V. Mikulich ◽  
B. Axtmann ◽  
U. Rist ◽  
Ch. Brücker
Keyword(s):  
Event Detection ◽  
Extreme Event ◽  
Wall Turbulence ◽  
Near Wall Turbulence ◽  
Near Wall ◽  
Micropillar Arrays

scholarly journals Power System Extreme Event Detection: The Vulnerability Frontier

2008 ◽  
Author(s):  
Bernard C. Lesieutre ◽  
Ali Pinar ◽  
Sandip Roy
Keyword(s):  
Power System ◽  
Event Detection ◽  
Extreme Event

scholarly journals Flood risk assessment in South Asia to prioritize flood index insurance applications in Bihar, India

2018 ◽  
Vol 10 (1) ◽  
pp. 26-48 ◽  
Author(s):  
Karthikeyan Matheswaran ◽  
Niranga Alahacoon ◽  
Rajesh Pandey ◽  
Giriraj Amarnath
Keyword(s):  
Risk Assessment ◽  
South Asia ◽  
Flood Risk ◽  
Index Insurance ◽  
Flood Risk Assessment ◽  
Flood Index

Extreme event detection and assimilation from multimedia sources

2012 ◽  
Vol 70 (1) ◽  
pp. 237-261 ◽  
Author(s):  
Ashit Talukder ◽  
Anand Panangadan
Keyword(s):  
Event Detection ◽  
Extreme Event

Military situation awareness: Facilitating critical event detection in chat

2006 ◽  
Author(s):  
Jean M. Catanzaro ◽  
Matthew R. Risser ◽  
John W. Gwynne ◽  
Daniel I. Manes
Keyword(s):  
Situation Awareness ◽  
Event Detection ◽  
Critical Event

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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