Global ETD Search

1	Fuel Load and Fire Behaviour in the Southern Ontario Tallgrass Prairie Kidnie, Susan M. 12 February 2010 (has links) Prescribed burning is an important management tool for the restoration and maintenance of tallgrass prairies. To improve fire behaviour prediction in tallgrass prairies, I assessed three different aspects of fire behaviour - heat of combustion, fuel load and rate of spread. Heat of combustion was found to vary amongst certain tallgrass species but the relatively small differences in means is unlikely to contribute significantly to fire behaviour. Average fuel loads in Ontario tallgrass prairie sites were found to be higher than current default value used in fire behaviour prediction. Three rapid fuel load assessment techniques were tested. Finally, the predictions of three fire behaviour prediction systems - the FBP System, BehavePlus and an Australian grassfire spread model, were compared with actual fire behaviour observations. The FBP System was found to perform poorly while both BehavePlus and the Australian model exhibited relatively strong relationships between observed and predicted rates of spread. tallgrass prairie prescribed burning heat of combustion fuel load grass fire spread prediction 0478 0777 0329
2	Fuel Load and Fire Behaviour in the Southern Ontario Tallgrass Prairie Kidnie, Susan M. 12 February 2010 (has links) Prescribed burning is an important management tool for the restoration and maintenance of tallgrass prairies. To improve fire behaviour prediction in tallgrass prairies, I assessed three different aspects of fire behaviour - heat of combustion, fuel load and rate of spread. Heat of combustion was found to vary amongst certain tallgrass species but the relatively small differences in means is unlikely to contribute significantly to fire behaviour. Average fuel loads in Ontario tallgrass prairie sites were found to be higher than current default value used in fire behaviour prediction. Three rapid fuel load assessment techniques were tested. Finally, the predictions of three fire behaviour prediction systems - the FBP System, BehavePlus and an Australian grassfire spread model, were compared with actual fire behaviour observations. The FBP System was found to perform poorly while both BehavePlus and the Australian model exhibited relatively strong relationships between observed and predicted rates of spread. tallgrass prairie prescribed burning heat of combustion fuel load grass fire spread prediction 0478 0777 0329
3	Characterizing the Informativity of Level II Book Data for High Frequency Trading Nielsen, Logan B. 10 April 2023 (has links) (PDF) High Frequency Trading (HFT) algorithms are automated feedback systems interacting with markets to maximize returns on investments. These systems have the potential to read different resolutions of market information at any given time, where Level I information is the minimal information about an equity--essentially its price--and Level II information is the full order book at that time for that equity. This paper presents a study of using Recurrent Neural Network (RNN) models to predict the spread of the DOW Industrial 30 index traded on NASDAQ, using Level I and Level II data as inputs. The results show that Level II data does not significantly improve the prediction of spread when predicting less than 100 millisecond into the future, while it is increasingly informative for spread predictions further into the future. This suggests that HFT algorithms should not attempt to make use of Level II information, and instead reallocate that computation power for improved trading performance, while slower trading algorithms may very well benefit from processing the complete order book. High Frequency Trading Data Informativity Recurrent Neural Network Spread Prediction Limit Order Book Physical Sciences and Mathematics
4	Wildfire Spread Prediction Using Attention Mechanisms In U-Net Shah, Kamen Haresh, Shah, Kamen Haresh 01 December 2022 (has links) (PDF) An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, employing ensemble modeling reduces bias and variation, leading to more consistent and accurate predictions. When inferencing on wildfire propagation at 30-minute intervals, the architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1%. Image Segmentation Wildfire Spread Prediction Google Earth Engine Satellite Images Deep Learning U-Net Attention Ensemble Modeling Bayesian Hyperparameter Optimization Threshold Tuning TensorFlow Python Wildfires Artificial Intelligence and Robotics Computational Engineering Software Engineering
5	Developing a 2D Forest Fire Spread Simulation for Enhanced Decision-Making During Catastrophes in Sweden Gauffin Dahlin, David January 2024 (has links) This thesis presents the development and evaluation of a 2D simulation model designed to predict the spread and behavior of forest fires, with a specific focus on Swedish forest ecosystems. Despite the model's simplicity and inherent limitations due to assumptions such as homogeneity in fuel distribution and the exclusion of topographical influences, the simulations yield remarkably accurate predictions of fire spread and intensity. The model integrates basic meteorological data (wind speed and direction, temperature) and uses a discretized spatial approach to simulate the dynamics of forest fires. Initial results suggest that even with minimal input variables and broad assumptions, the model offers significant predictive capabilities, highlighting potential areas for future refinement. Key aspects such as the interaction between conduction and advection terms, the role of water vaporization in fire dynamics, and the influence of wind on fire propagation are discussed. The findings encourage further development of the model, aiming at incorporating more complex variables such as topography and more forest fuels, potentially enhancing its utility in real-time fire management and decision-making processes. Forest Fire Simulation 2D Fire Spread Model Heat Transfer in Forest Fires Cellular Automata Wildfire Behavior Meteorological Data in Fire Modeling Fire Dynamics Fire Management Decision-Making Forest Ecosystems Sweden Wildfire Study Computational Fire Models Advection and Conduction in Fires Water Vaporization in Wildfires Fire Induced Weather Patterns Fire Spread Prediction Real-time Fire Management Environmental Impact of Forest Fires Empirical Data for Fire Simulation Firefighter Interventions Stochastic Wind Modeling Mathematics Matematik

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