Calculations of risk from natural disasters may require ensembles of hundreds of thousands of simulations to accurately quantify the complex relationships between the outcome of a disaster and its contributing factors...Calculations of risk from natural disasters may require ensembles of hundreds of thousands of simulations to accurately quantify the complex relationships between the outcome of a disaster and its contributing factors.Such large ensembles cannot typically be run on a single computer due to the limited computational resources available.Cloud Computing offers an attractive alternative,with an almost unlimited capacity for computation,storage,and network bandwidth.However,there are no clear mechanisms that define how to implement these complex natural disaster ensembles on the Cloud with minimal time and resources.As such,this paper proposes a system framework with two phases of cost optimization to run the ensembles as a service over Cloud.The cost is minimized through efficient distribution of the simulations among the cost-efficient instances and intelligent choice of the instances based on pricing models.We validate the proposed framework using real Cloud environment with real wildfire ensemble scenarios under different user requirements.The experimental results give an edge to the proposed system over the bag-of-task type execution on the Clouds with less cost and better flexibility.展开更多
At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from a...At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.展开更多
Taking hundreds of pieces of hazardous geological maps （1 ： 10 000） of Three Gorges res-ervoir area （3GR） as background, we establish regional three-dimensional （3D） geo-hazard modelusing DEM （digital elevatio...Taking hundreds of pieces of hazardous geological maps （1 ： 10 000） of Three Gorges res-ervoir area （3GR） as background, we establish regional three-dimensional （3D） geo-hazard modelusing DEM （digital elevation model） superposed surface images and geo-hazards elements. Based on landslides and other geo-hazard survey data,using improved B-REP（boundary representa-tion）entity data structure （two-body 3D data structure）, we set up 3D solid models for each hazardous bodies in each hazardous geological maps. Then we integrate the two types of 3D models with different scales from area to point, which are the regional geo-hazard 3D model and the solid models of each disaster body, in order to provide a visual processing and analysis plat-form for danger partition, stability evaluation, disaster prevention and control, early warning and command.展开更多
Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Marko...Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.展开更多
In this study,a broad range of supervised machine learning and parametric statistical,geospatial,and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derive...In this study,a broad range of supervised machine learning and parametric statistical,geospatial,and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derived geolocated building damage data for earthquakes,via regression-and classification-based models,respectively.For the aggregated observational data,models were ranked via predictive performance of mortality,population displacement,building damage,and building destruction for 375 observations across 161 earthquakes in 61 countries.For the satellite image-derived data,models were ranked via classification performance(damaged/unaff ected)of 369,813 geolocated buildings for 26 earthquakes in 15 countries.Grouped k-fold,3-repeat cross validation was used to ensure out-of-sample predictive performance.Feature importance of several variables used as proxies for vulnerability to disasters indicates covariate utility.The 2023 Türkiye-Syria earthquake event was used to explore model limitations for extreme events.However,applying the AdaBoost model on the 27,032 held-out buildings of the 2023 Türkiye-Syria earthquake event,predictions had an AUC of 0.93.Therefore,without any geospatial,building-specific,or direct satellite image information,this model accurately classified building damage,with significantly improved performance over satellite image trained models found in the literature.展开更多
Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep uncertainty.Due to deep uncertainty,decision-makers have severe i...Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep uncertainty.Due to deep uncertainty,decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future,thus making it difficult to specify appropriate models and parameters to describe the system of interest.In this paper,we propose a dynamic exploratory hybrid modeling framework that fits data,models,and computational ex-periments together to simulate complex systems with deep uncertainty.In the framework,one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experi-ments to explore the diversity of future scenarios.Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system.This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated.Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions.The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.展开更多
基金supported by Data61,Commonwealth Scientific and Industrial Research Organization(CSIRO)University of Tasmania(Tasmania Graduate Research Scholarship 2018)。
文摘Calculations of risk from natural disasters may require ensembles of hundreds of thousands of simulations to accurately quantify the complex relationships between the outcome of a disaster and its contributing factors.Such large ensembles cannot typically be run on a single computer due to the limited computational resources available.Cloud Computing offers an attractive alternative,with an almost unlimited capacity for computation,storage,and network bandwidth.However,there are no clear mechanisms that define how to implement these complex natural disaster ensembles on the Cloud with minimal time and resources.As such,this paper proposes a system framework with two phases of cost optimization to run the ensembles as a service over Cloud.The cost is minimized through efficient distribution of the simulations among the cost-efficient instances and intelligent choice of the instances based on pricing models.We validate the proposed framework using real Cloud environment with real wildfire ensemble scenarios under different user requirements.The experimental results give an edge to the proposed system over the bag-of-task type execution on the Clouds with less cost and better flexibility.
基金funded by the National Key Technologies R&D Program of China (Grants No. 2017YFC0505104)the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation of China (Grants No. DM2016SC09)
文摘At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.
基金supported by the 3D Model Library of Geo-hazards in the 3GR (No. SXJC-3ZH1A7)the software development of 3D area disaster geology map in the 3GR (No. SXJC-3ZH1A6)+1 种基金survey data acquisition and geologic map CAD system in the 3GR (No. SXKY4-02)985 Platform Projects,3D modeling and space analysis system of geo-hazards and the National Natural Science Foundation of China (No. 41172300)
文摘Taking hundreds of pieces of hazardous geological maps （1 ： 10 000） of Three Gorges res-ervoir area （3GR） as background, we establish regional three-dimensional （3D） geo-hazard modelusing DEM （digital elevation model） superposed surface images and geo-hazards elements. Based on landslides and other geo-hazard survey data,using improved B-REP（boundary representa-tion）entity data structure （two-body 3D data structure）, we set up 3D solid models for each hazardous bodies in each hazardous geological maps. Then we integrate the two types of 3D models with different scales from area to point, which are the regional geo-hazard 3D model and the solid models of each disaster body, in order to provide a visual processing and analysis plat-form for danger partition, stability evaluation, disaster prevention and control, early warning and command.
基金supported by the National Natural Science Foundation of China (50879085)the Program for New Century Excellent Talents in University(NCET-07-0778)the Key Technology Research Project of Dynamic Environmental Flume for Ocean Monitoring Facilities (201005027-4)
文摘Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.
基金funded by the Engineering&Physical Sciences Research Council(EPSRC)Impact Acceleration Account Award EP/R511742/1。
文摘In this study,a broad range of supervised machine learning and parametric statistical,geospatial,and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derived geolocated building damage data for earthquakes,via regression-and classification-based models,respectively.For the aggregated observational data,models were ranked via predictive performance of mortality,population displacement,building damage,and building destruction for 375 observations across 161 earthquakes in 61 countries.For the satellite image-derived data,models were ranked via classification performance(damaged/unaff ected)of 369,813 geolocated buildings for 26 earthquakes in 15 countries.Grouped k-fold,3-repeat cross validation was used to ensure out-of-sample predictive performance.Feature importance of several variables used as proxies for vulnerability to disasters indicates covariate utility.The 2023 Türkiye-Syria earthquake event was used to explore model limitations for extreme events.However,applying the AdaBoost model on the 27,032 held-out buildings of the 2023 Türkiye-Syria earthquake event,predictions had an AUC of 0.93.Therefore,without any geospatial,building-specific,or direct satellite image information,this model accurately classified building damage,with significantly improved performance over satellite image trained models found in the literature.
基金This research was supported by the National Natural Science Foundation of China[72004141,72174102,72334003]the Guangdong Office of Philosophy and Social Science[GD23XGL115].
文摘Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep uncertainty.Due to deep uncertainty,decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future,thus making it difficult to specify appropriate models and parameters to describe the system of interest.In this paper,we propose a dynamic exploratory hybrid modeling framework that fits data,models,and computational ex-periments together to simulate complex systems with deep uncertainty.In the framework,one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experi-ments to explore the diversity of future scenarios.Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system.This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated.Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions.The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.
