scholarly journals Integrating empirical models and satellite radar can improve landslide detection for emergency response

2021 ◽  
Vol 21 (10) ◽  
pp. 2993-3014
Author(s):  
Katy Burrows ◽  
David Milledge ◽  
Richard J. Walters ◽  
Dino Bellugi
Keyword(s):  
Model Performance ◽  
Radar Data ◽  
Empirical Models ◽  
Alternative Methods ◽  
Similar Data ◽  
Characteristic Analysis ◽  
Emergency Responders ◽  
Improve Model ◽  
Satellite Radar ◽  
Using Data

Abstract. Information on the spatial distribution of triggered landslides following an earthquake is invaluable to emergency responders. Manual mapping using optical satellite imagery, which is currently the most common method of generating this landslide information, is extremely time consuming and can be disrupted by cloud cover. Empirical models of landslide probability and landslide detection with satellite radar data are two alternative methods of generating information on triggered landslides that overcome these limitations. Here we assess the potential of a combined approach, in which we generate an empirical model of the landslides using data available immediately following the earthquake using the random forest technique and then progressively add landslide indicators derived from Sentinel-1 and ALOS-2 satellite radar data to this model in the order they were acquired following the earthquake. We use three large case study earthquakes and test two model types: first, a model that is trained on a small part of the study area and used to predict the remainder of the landslides and, second, a preliminary global model that is trained on the landslide data from two earthquakes and used to predict the third. We assess model performance using receiver operating characteristic analysis and r2, and we find that the addition of the radar data can considerably improve model performance and robustness within 2 weeks of the earthquake. In particular, we observed a large improvement in model performance when the first ALOS-2 image was added and recommend that these data or similar data from other L-band radar satellites be routinely incorporated in future empirical models.

scholarly journals Improved rapid landslide detection from integration of empirical models and satellite radar

2021 ◽  
Author(s):  
Katy Burrows ◽  
David Milledge ◽  
Richard J. Walters ◽  
Dino Bellugi
Keyword(s):  
Model Performance ◽  
Radar Data ◽  
Empirical Models ◽  
Alternative Methods ◽  
Similar Data ◽  
Characteristic Analysis ◽  
Emergency Responders ◽  
Improve Model ◽  
Satellite Radar ◽  
Using Data

Abstract. Information on the spatial distribution of triggered landslides following an earthquake is invaluable to emergency responders. Manual mapping using optical satellite imagery, which is currently the most common method of generating this landslide information, is extremely time consuming and can be disrupted by cloud-cover. Empirical models of landslide probability and landslide detection with satellite radar data are two alternative methods of generating information on triggered landslides that overcome these limitations. Here we assess the potential of a combined approach, in which we generate an empirical model of the landslides using data available immediately following the earthquake using the Random Forests technique, and then progressively add landslide indicators derived from Sentinel-1 and ALOS-2 satellite radar data to this model in the order they were acquired following the earthquake. We use three large case study earthquakes and test two model types: first, a model that is trained on a small part of the study area and used to predict the remainder of the landslides, and second a preliminary "global" model that is trained on the landslide data from two earthquakes and used to predict the third. We assess model performance using receiver operating characteristic analysis and r2, and find that the addition of the radar data can considerably improve model performance and robustness within two weeks of the earthquake. In particular, we observed a large improvement in model performance when the first ALOS-2 image was added and recommend that these data or similar data from other L-band radar satellites be routinely incorporated in future empirical models.

scholarly journals Population Pharmacokinetics of Doxycycline in Children

2019 ◽  
Vol 63 (12) ◽  
Author(s):  
Elizabeth J. Thompson ◽  
Huali Wu ◽  
Chiara Melloni ◽  
Stephen Balevic ◽  
Janice E. Sullivan ◽  
...  
Keyword(s):  
Model Performance ◽  
Standard Of Care ◽  
Volume Of Distribution ◽  
Clinical Value ◽  
Final Model ◽  
Childhood Infections ◽  
Obesity Status ◽  
Improve Model ◽  
Using Data ◽  
Improve Model Performance

ABSTRACT Doxycycline is a tetracycline-class antimicrobial labeled by the U.S. Food and Drug Administration for children >8 years of age for many common childhood infections. Doxycycline is not labeled for children ≤8 years of age, due to the association between tetracycline-class antibiotics and tooth staining, although doxycycline may be used off-label under severe conditions. Accordingly, there is a paucity of pharmacokinetic (PK) data to guide dosing in children 8 years and younger. We leveraged opportunistically collected plasma samples after intravenous (i.v.) and oral doxycycline doses received per standard of care to characterize the PK of doxycycline in children of different ages and evaluated the effect of obesity and fasting status on PK parameters. We developed a population PK model of doxycycline using data collected from 47 patients 0 to 18 years of age, including 14 participants ≤8 years. We developed a 1-compartment PK model and found doxycycline clearance to be 3.32 liters/h/70 kg of body weight and volume to be 96.8 liters/70 kg for all patients, comparable to values reported in adults. We estimated a bioavailability of 89.6%, also consistent with adult data. Allometrically scaled clearance and volume of distribution did not differ between children 2 to ≤8 years of age and children >8 to ≤18 years of age, suggesting that younger children may be given the same per-kilogram dosing. Obesity status and fasting status were not selected for inclusion in the final model. Additional doxycycline PK samples collected in future studies may be used to improve model performance and maximize its clinical value.

scholarly journals A systematic exploration of satellite radar coherence methods for rapid landslide detection

2020 ◽  
Vol 20 (11) ◽  
pp. 3197-3214
Author(s):  
Katy Burrows ◽  
Richard J. Walters ◽  
David Milledge ◽  
Alexander L. Densmore
Keyword(s):  
Emergency Response ◽  
Radar Data ◽  
Detection Methods ◽  
Emergency Responders ◽  
Systematic Exploration ◽  
Radar Imagery ◽  
Satellite Radar ◽  
Satellite Radar Imagery ◽  
Event Image ◽  
Satellite Sensor

Abstract. Emergency responders require information on the distribution of triggered landslides within 2 weeks of an earthquake or storm. Useable satellite radar imagery is acquired within days of any such event worldwide. Recently, several landslide detection methods that use these data have been developed, but testing of these methods has been limited in each case to a single event and satellite sensor. Here we systematically test five methods using ALOS-2 and Sentinel-1 data across four triggering earthquakes. The best-performing method was dependent on the satellite sensor. For three of our four case study events, an initial ALOS-2 image was acquired within 2 weeks, and with these data, co-event coherence loss (CECL) is the best-performing method. Using a single post-event Sentinel-1 image, the best-performing method was the boxcar–sibling (Bx–S) method. We also present three new methods which incorporate a second post-event image. While the waiting time for this second post-event image is disadvantageous for emergency response, these methods perform more consistently and on average 10 % better across event and sensor type than the boxcar–sibling and CECL methods. Thus, our results demonstrate that useful landslide density information can be generated on the timescale of emergency response and allow us to make recommendations on the best method based on the availability and latency of post-event radar data.

scholarly journals A New Method for Large-Scale Landslide Classification from Satellite Radar

2019 ◽  
Vol 11 (3) ◽  
pp. 237 ◽  
Author(s):  
Katy Burrows ◽  
Richard J. Walters ◽  
David Milledge ◽  
Karsten Spaans ◽  
Alexander L. Densmore
Keyword(s):  
Emergency Response ◽  
Large Scale ◽  
Operating Characteristic ◽  
Receiver Operating Characteristic Analysis ◽  
Weather Conditions ◽  
New Method ◽  
Characteristic Analysis ◽  
Emergency Responders ◽  
Density Maps ◽  
Satellite Radar

Following a large continental earthquake, information on the spatial distribution of triggered landslides is required as quickly as possible for use in emergency response coordination. Synthetic Aperture Radar (SAR) methods have the potential to overcome variability in weather conditions, which often causes delays of days or weeks when mapping landslides using optical satellite imagery. Here we test landslide classifiers based on SAR coherence, which is estimated from the similarity in phase change in time between small ensembles of pixels. We test two existing SAR-coherence-based landslide classifiers against an independent inventory of landslides triggered following the Mw 7.8 Gorkha, Nepal earthquake, and present and test a new method, which uses a classifier based on coherence calculated from ensembles of neighbouring pixels and coherence calculated from a more dispersed ensemble of ‘sibling’ pixels. Using Receiver Operating Characteristic analysis, we show that none of these three SAR-coherence-based landslide classification methods are suitable for mapping individual landslides on a pixel-by-pixel basis. However, they show potential in generating lower-resolution density maps, which are used by emergency responders following an earthquake to coordinate large-scale operations and identify priority areas. The new method we present outperforms existing methods when tested at these lower resolutions, suggesting that it may be able to provide useful and rapid information on landslide distributions following major continental earthquakes.

scholarly journals A Systematic Exploration of Satellite Radar Coherence Methods for Rapid Landslide Detection

2020 ◽  
Author(s):  
Katy Burrows ◽  
Richard J. Walters ◽  
David Milledge ◽  
Alexander L. Densmore
Keyword(s):  
Emergency Response ◽  
Radar Data ◽  
Detection Methods ◽  
Emergency Responders ◽  
Systematic Exploration ◽  
Radar Imagery ◽  
Satellite Radar ◽  
Satellite Radar Imagery ◽  
Event Image ◽  
Satellite Sensor

Abstract. Emergency responders require information on the distribution of triggered landslides within two weeks of an earthquake or storm. Useable satellite radar imagery is acquired within days of any such event worldwide. Recently, several landslide detection methods that use these data have been developed, but testing of these methods has been limited in each case to a single event and satellite sensor. Here we systematically test five methods using ALOS-2 and Sentinel-1 data across four triggering events. The best performing method was dependent on the satellite sensor. For three of our four case study events, an initial ALOS-2 image was acquired within 2 weeks, and with these data the ARIA method performs best. Using a single post-event Sentinel-1 image, the best-performing method was the boxcar-sibling method. We also present three new methods which incorporate a second post-event image. While the waiting time for this second post-event image is disadvantageous for emergency response, these methods perform more consistently and on average 10 % better across event and sensor type than the boxcar-sibling and ARIA methods. Thus, our results demonstrate that useful landslide density information can be generated on the timescale of emergency response, and allow us to make recommendations on the best method based on the availability and latency of post-event radar data.

The Multiple-Vortex Structure of the El Reno, Oklahoma, Tornado on 31 May 2013

2018 ◽  
Vol 146 (8) ◽  
pp. 2483-2502 ◽  
Author(s):  
Howard B. Bluestein ◽  
Kyle J. Thiem ◽  
Jeffrey C. Snyder ◽  
Jana B. Houser
Keyword(s):  
Doppler Radar ◽  
Radar Data ◽  
Rapid Scan ◽  
X Band ◽  
Close Range ◽  
Formation And Evolution ◽  
Using Data ◽  
Secondary Vortices ◽  
The Right ◽  
Gust Front

Abstract This study documents the formation and evolution of secondary vortices associated within a large, violent tornado in Oklahoma based on data from a close-range, mobile, polarimetric, rapid-scan, X-band Doppler radar. Secondary vortices were tracked relative to the parent circulation using data collected every 2 s. It was found that most long-lived vortices (those that could be tracked for ≥15 s) formed within the radius of maximum wind (RMW), mainly in the left-rear quadrant (with respect to parent tornado motion), passing around the center of the parent tornado and dissipating closer to the center in the right-forward and left-forward quadrants. Some secondary vortices persisted for at least 1 min. When a Burgers–Rott vortex is fit to the Doppler radar data, and the vortex is assumed to be axisymmetric, the secondary vortices propagated slowly against the mean azimuthal flow; if the vortex is not assumed to be axisymmetric as a result of a strong rear-flank gust front on one side of it, then the secondary vortices moved along approximately with the wind.

scholarly journals Classification of Cattle Behaviours Using Neck-Mounted Accelerometer-Equipped Collars and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4050
Author(s):  
Dejan Pavlovic ◽  
Christopher Davison ◽  
Andrew Hamilton ◽  
Oskar Marko ◽  
Robert Atkinson ◽  
...  
Keyword(s):  
Neural Network ◽  
Model Performance ◽  
Ground Truth ◽  
Practical Implementation ◽  
Ground Truth Data ◽  
Battery Lifetime ◽  
Implementation Challenges ◽  
Memory Footprint ◽  
Commercial Farms ◽  
Using Data

Monitoring cattle behaviour is core to the early detection of health and welfare issues and to optimise the fertility of large herds. Accelerometer-based sensor systems that provide activity profiles are now used extensively on commercial farms and have evolved to identify behaviours such as the time spent ruminating and eating at an individual animal level. Acquiring this information at scale is central to informing on-farm management decisions. The paper presents the development of a Convolutional Neural Network (CNN) that classifies cattle behavioural states (`rumination’, `eating’ and `other’) using data generated from neck-mounted accelerometer collars. During three farm trials in the United Kingdom (Easter Howgate Farm, Edinburgh, UK), 18 steers were monitored to provide raw acceleration measurements, with ground truth data provided by muzzle-mounted pressure sensor halters. A range of neural network architectures are explored and rigorous hyper-parameter searches are performed to optimise the network. The computational complexity and memory footprint of CNN models are not readily compatible with deployment on low-power processors which are both memory and energy constrained. Thus, progressive reductions of the CNN were executed with minimal loss of performance in order to address the practical implementation challenges, defining the trade-off between model performance versus computation complexity and memory footprint to permit deployment on micro-controller architectures. The proposed methodology achieves a compression of 14.30 compared to the unpruned architecture but is nevertheless able to accurately classify cattle behaviours with an overall F1 score of 0.82 for both FP32 and FP16 precision while achieving a reasonable battery lifetime in excess of 5.7 years.

scholarly journals Towards Robust Representations of Spatial Networks Using Graph Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6918
Author(s):  
Chidubem Iddianozie ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Network Models ◽  
Data Representation ◽  
Spatial Networks ◽  
Neural Network Models ◽  
Improve Model ◽  
Graph Neural Networks ◽  
Spatial Entities ◽  
Improve Model Performance

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

scholarly journals Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption

2017 ◽  
Vol 17 (2) ◽  
pp. 1187-1205 ◽  
Author(s):  
Guangliang Fu ◽  
Fred Prata ◽  
Hai Xiang Lin ◽  
Arnold Heemink ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Volcanic Ash ◽  
Three Dimensional ◽  
Forecast Accuracy ◽  
Improve Model ◽  
Powerful Approach ◽  
Using Data ◽  
D Model

Abstract. Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations.In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space.Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h.

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