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

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