Time and Date: 14:10 - 15:50 on 13th June 2017
Room: HG D 7.2
Chair: Craig Douglas
|214|| Data resolution effects on a coupled data driven system for forest fire propagation prediction [abstract]
Abstract: Every year, millions of forest worldwide hectares are burned causing important consequences on the atmosphere, biodiversity and economy. A correct prediction of the fire evolution allows to manage the fire fighting equipment properly. Therefore, it is crucial to use reliable and speed simulations in order to predict the evolution of the fire. WRF-SFIRE is a wildland fire simulator, which couples a meteorological model called Weather Research and Forecasting Model (WRF) and a forest fire simulator, SFIRE. The aforementioned coupling strategy reproduces the interaction between the propagation of the fire and the atmosphere surrounding it. The mesh resolution used to solve the atmosphere evolution has a deep impact in the prediction of small scale meteorological effects. At the same time, the ability of introducing these small scale meteorological events into the forest fire simulation implies enhancements in the quality of the data that drives the simulation, therefore, better fire propagation predictions. However, this improvement can be affected by the instability of the problem to solve. So, this paper states the convergence problem due to the mesh resolution when using WRF-SFIRE and a proposal to overcome it is described. The proposed scheme has been tested using a real case that took place in Catalonia (northeast of Spain) in 2005.
|Àngel Farguell, Ana Cortés, Tomàs Margalef, Josep Ramón Miró and Jordi Mercader|
|442|| Data Assimilation of Wildfires with Fuel Adjustment Factors in FARSITE using Ensemble Kalman Filtering [abstract]
Abstract: This paper show the extension of the wildfire simulation tool FARSITE to allow for data assimilation capabilities on both fire perimeters and fuel adjustment factors to improve the accuracy of wildfire spread predictions. While fire perimeters characterize the overall burn scar of a wildfire, fuel adjustment factors are fuel model specific calibration numbers that adjust the rate of spread for each fuel type independently. Data assimilation updates of both fire perimeters and fuel adjustment factors are calculated from an Ensemble Kalman Filter (EnKF) that exploits the uncertainty information on the simulated fire perimeter, fuel adjustment factors and a measured fire perimeter. The effectiveness of the proposed data assimilation is illustrated on a wildfire simulation representing the 2014 Cocos fire, tracking time varying fuel adjustment factors based on noisy and limited spatial resolution observations of the fire perimeter.
|Thayjes Srivas, Raymond de Callafon, Daniel Crawl and Ilkay Altintas|
|187|| Optimization strategy exploration in a wildfire propagation data driven system [abstract]
Abstract: The increasing capacity to gather data of an on-going wildfire operation has triggered the methods and strategies to incorporate these data to a flexible model to improve forecasting accuracy and validity. In the present paper we discuss the optimization strategy included in an inverse model algorithm based on semi-empirical fire spread model fed with infra-red airborne acquired images. The algorithm calibrates 7 parameters and incorporates a topographic diagnosis wind model. The optimization problem is shown to be a non-smooth problem and thus, its best resolving strategy is critical regarding efficiency and times constraints. Three optimization strategies are evaluated in a synthetic real-scale scenario to select the more efficient one. Preliminary results are discussed and compared.
|Oriol Rios, M. Miguel Valero, Elsa Pastor and Eulalia Planas|
|127|| Feature Based Grid Event Classication from Synchrophasor Data [abstract]
Abstract: This paper presents a method for automatic classification of power disturbance events in an electric grid by means of distributed parameter estimation and clustering techniques of synchro- phasor data produced by phasor measurement units (PMUs). Disturbance events detected in the PMU data are subjected to a parameter estimation routine to extract features that include oscillation frequency, participation factor, damping factor and post and pre-event frequency offset. The parameters are used to classify events and classification rules are deduced on the basis of a training set of known events using nonlinear programming. Once the classification rules are set, the approach can be used to automatically classify events not seen in the training set. The proposed event classification is illustrated on a microPMU system data developed by Power Standards Lab for which disturbance events were measured over several months.
|Sai Akhil Reddy Konakalla and Raymond de Callafon|
|586|| A Framework for Provenance Analysis and Visualization [abstract]
Abstract: Data provenance is a fundamental concept in scientific experimentation. However, for their understanding and use, efficient and user-friendly mechanisms are needed. Research in software visualization, ontologies and complex networks can help in this process. This paper presents a framework to assist the understanding and use of data provenance through visualization techniques, ontologies and complex networks. The framework generates new information using ontologies and provenance graph analysis and highlights results through new visualization techniques. The framework was used in the E-SECO scientific ecosystem platform.
|Weiner Oliveira, Lenita M. Ambrósio, Regina Braga, Victor Ströele, José Maria N. David and Fernanda Campos|