Add to ORKGAbstract. We present an overview of an ongoing project to build DDDAS to use all available data for a short term wildfire prediction. The project involves new data assimilation methods to inject data into a running simulation, a physics based model coupled with weather pre-diction, on-site data acquisition using sensors that can survive a passing fire, and on-line visualization using Google Earth.
Wildfire information has long been collected in Europe, with particular focus on forest fires. The E...
AbstractForest fire devastate every year thousand of hectares of forest around the world. Fire behav...
Modeling fire spread is critical in fire risk management. Creating data-driven models to forecast sp...
We present an overview of an ongoing project to build DDDAS to use all available data for a short te...
Abstract. A proposed system for real-time modeling of wildfires is described. The system involves nu...
Abstract. We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) f...
AbstractWe have applied the Dynamic Data Driven Application System (DDDAS) methodology to predict wi...
We have applied the Dynamic Data Driven Application System (DDDAS) methodology to predict wildfire p...
Abstract. This work presents a novel idea for forest fire prediction, based on Dynamic Data Driven A...
This thesis describes an observation function for a dynamic data driven application system designed ...
AbstractWildfires are critical for ecosystems in many geographical regions. However, our current urb...
Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ec...
Abstract. This work represents the first step toward a DDDAS for Wildland Fire Prediction where our ...
Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ec...
Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ec...
Wildfire information has long been collected in Europe, with particular focus on forest fires. The E...
AbstractForest fire devastate every year thousand of hectares of forest around the world. Fire behav...
Modeling fire spread is critical in fire risk management. Creating data-driven models to forecast sp...
We present an overview of an ongoing project to build DDDAS to use all available data for a short te...
Abstract. A proposed system for real-time modeling of wildfires is described. The system involves nu...
Abstract. We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) f...
AbstractWe have applied the Dynamic Data Driven Application System (DDDAS) methodology to predict wi...
We have applied the Dynamic Data Driven Application System (DDDAS) methodology to predict wildfire p...
Abstract. This work presents a novel idea for forest fire prediction, based on Dynamic Data Driven A...
This thesis describes an observation function for a dynamic data driven application system designed ...
AbstractWildfires are critical for ecosystems in many geographical regions. However, our current urb...
Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ec...
Abstract. This work represents the first step toward a DDDAS for Wildland Fire Prediction where our ...
Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ec...
Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ec...
Wildfire information has long been collected in Europe, with particular focus on forest fires. The E...
AbstractForest fire devastate every year thousand of hectares of forest around the world. Fire behav...
Modeling fire spread is critical in fire risk management. Creating data-driven models to forecast sp...