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Author: Lengyi Han
Publisher:
ISBN:
Category :
Languages : en
Pages : 314
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Book Description
This thesis develops statistical methods and models and applies them to problems related to forest fires. The unifying goal of the work is to provide a data analytic basis for quantifying the uncertainty surrounding fire ignition and fire growth which builds on existing theory where possible. The main body of the thesis is comprised of three research papers. The Fire Weather Index (FWI) plays an important role in fire management and is central to the first two papers. In the first instance, the block bootstrap confidence interval method is used to deal nonparametrically with the dependence in the FWI data. Because the actual and nominal confidence levels differ substantially, a double bootstrap is applied and used to calibrate the confidence intervals. The calibration technique focuses on interval length-adjustment instead of level-adjustment. The second paper systematically develops a sequence of parametric time series models for the FWI, starting from some basic physical observations. The final model developed is a seasonal random effect mixture tailed minification model. Numerical approximations using the model allows for calculation of the survival function for the minimum FWI in one or more consecutive days. Tentative results presented here suggest that fire danger prediction may be more effectively accomplished using information on runs of moderately large FWI values, instead of simply using a single-day cut-off value. The third paper studies the fire growth model, Prometheus and re-analyzes the data underlying the associated rate of spread formulas. The main goal of the paper is to incorporate randomness into the deterministic Prometheus model giving rise to a new simulator called Dionysus. The essential idea is a parametric bootstrap. Burn probability contours can be created quickly by Dionysus. These can help fire managers make suppression resource allocation decisions for fires which are currently burning.
Author: Lengyi Han
Publisher:
ISBN:
Category :
Languages : en
Pages : 314
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Book Description
This thesis develops statistical methods and models and applies them to problems related to forest fires. The unifying goal of the work is to provide a data analytic basis for quantifying the uncertainty surrounding fire ignition and fire growth which builds on existing theory where possible. The main body of the thesis is comprised of three research papers. The Fire Weather Index (FWI) plays an important role in fire management and is central to the first two papers. In the first instance, the block bootstrap confidence interval method is used to deal nonparametrically with the dependence in the FWI data. Because the actual and nominal confidence levels differ substantially, a double bootstrap is applied and used to calibrate the confidence intervals. The calibration technique focuses on interval length-adjustment instead of level-adjustment. The second paper systematically develops a sequence of parametric time series models for the FWI, starting from some basic physical observations. The final model developed is a seasonal random effect mixture tailed minification model. Numerical approximations using the model allows for calculation of the survival function for the minimum FWI in one or more consecutive days. Tentative results presented here suggest that fire danger prediction may be more effectively accomplished using information on runs of moderately large FWI values, instead of simply using a single-day cut-off value. The third paper studies the fire growth model, Prometheus and re-analyzes the data underlying the associated rate of spread formulas. The main goal of the paper is to incorporate randomness into the deterministic Prometheus model giving rise to a new simulator called Dionysus. The essential idea is a parametric bootstrap. Burn probability contours can be created quickly by Dionysus. These can help fire managers make suppression resource allocation decisions for fires which are currently burning.
Author: Kevin P. Murphy
Publisher: MIT Press
ISBN: 0262018020
Category : Computers
Languages : en
Pages : 1102
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Book Description
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Author: Harry Podschwit
Publisher:
ISBN:
Category :
Languages : en
Pages : 117
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Book Description
Understanding and quantifying wildfire behavior is of interest to the scientific community, as well as public health and fire management professionals. To achieve this end, there is a demand for statistical descriptions of wildfire behavior and its relationship to the environment. However, wildfire behavior can be complex, described by multiple characteristics such as final size, duration and growth rates, and influenced by processes that can be regionally dependent. Further challenges arise due to the poor quality and availability of cumulative burn area time series data, which often contain missing and erroneous measurements. To address these issues, a variety of methods are presented. Multiple wildfire behaviors are represented using a simple decomposition of cumulative burn area time series that measures four meaningful quantities from the growth curve. The relationship between wildfire activity and the environment are approximated using regionally specific generalized linear models. Weather and landscape data are used to predict various measures of wildfire behavior. Validation results suggested that most of the models generalized well to independent data, and have potentially useful applications in climatological research. Data quality issues common to cumulative burn area time series are addressed using Bayesian state-space models, which reconstruct growth curves from multiple corrupted burn area time series. Two state space models are presented, a stationary version that assumes idealized fire growth, and a non-stationary version that produces reconstructions with time-varying growth rates. The relative computational costs and goodness-of-fit is illustrated by reconstructing the growth curves of 13 wildfires from 2014 wildfire season using growth data coming from two sources, fire perimeters from the Geospatial Multi-Agency Coordination (GeoMAC) and cumulative hotspot detects from the Hazard Mapping System (HMS). The stationary model had minimal computational costs, but rarely produced adequate descriptions of the burn area observations. The non-stationary model had much higher computational costs, but produced realistic estimates of the time series. An informal sensitivity analysis suggested that the reconstructed curves would be robust to changes in the priors. The main application of the state-space models is to reconstruct burn area time series, which can in turn be used for statistical analysis or validation of currently existing growth models. The framework can be modified for other purposes as well including forecasting burn area, and predicting the extinguishment date of a fire.
Author: Harry Podschwit
Publisher:
ISBN:
Category :
Languages : en
Pages : 148
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Book Description
Gauging the magnitude of model uncertainty and incorporating model uncertainty into predictions is of critical importance when models are used to inform wildfire-related decisions, where ignoring potential risks threaten human health, property, and the environment. Although techniques exist for addressing model uncertainty, these uncertainties are commonly ignored in most analyses. In this dissertation, I will evaluate the effects of model uncertainty on statistical predictions of wildfire activity in multiple contexts and propose techniques to incorporate these uncertainties into predictions. I will determine how uncertainty in the choice of predictive model and climate model influence forecasts of very-large fire activity in the second half of the 21st century, and integrate this uncertainty using a novel Bayesian model averaging approach to produce robust predictions. I find that when these model uncertainties are accounted for, that one may conclude, across the suite of model choices, that the frequency of very-large wildfires should be expected to increase in most regions of the United States if climate changes are not mitigated. The effects of model uncertainty will also be explored in the context of predicting final wildfire size for individual fires that have no yet finished growing. Specifically, I will gauge how the choice of utility function and the inclusion of growth information that is unavailable early in the wildfire's life alters the predictive ability of statistical models of final fire size and the stability of the model structure. I find that predictions of fire size can drastically change when new utility functions are considered, particularly in models that use growth information. I also find that the covariates used in the best model are sensitive to the choice of utility function, and that no single model is likely to optimally address the preferences of all wildfire-related decisionmakers they are intended to inform. The results of this analysis that (1) the preferred model will often change when new performance measures are considered, and (2) that the preferred model may change over time. I also present a method of integrating the model uncertainties associated with time-varying covariates and ill-defined utility functions into a single predictive distribution using Bayesian model averaging. I find that this novel model averaging approach generally improves predictive performance across a number of performance measures compared to the individual models contained within it. I discuss how the novel methods developed can be applicable to other forecasting applications and how they might allow wildfire professionals make better decisions.
Author: Gina M. Gerlich
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Drought and wildfire occurrences are predicted to compound due to global climate change, especially in Mediterranean climates. Therefore, researching potential wildfire determinants is imperative in preparing for and managing future wildfires. The primary goal of this research was to determine if specific environmental, spatial, and human-based variables can explain large wildfire occurrences in Southern California during four designated wildfire regimes, which are drought and post-drought years within the two fire seasons (i.e., dry and Santa Ana (SA) wind fire seasons), between 2012 and 2019 utilizing binary logistic regression models. The secondary goal was to map the predictive patterns of large wildfire occurrences in Southern California. This research used remotely sensed land surface temperature (LST), normalized difference vegetation index (NDVI), and evapotranspiration (ET) datasets. This research also used other raster datasets, such as precipitation, wind, aspect, slope, and digital elevation model (DEM). Various vector derived raster datasets were also used, such as distance to roads, powerlines, cities, and campgrounds, ecoregions, and the wildland-urban interface (WUI). Wildfire occurrences are influenced by anthropogenic, environmental, and spatial factors; however, once ignition occurs and wildfires begin to spread, the environmental factors become more significant in fueling large wildfires. The results indicated that lower NDVI values were the strongest predictor when wildfires were smaller in terms of area burned and when less wildfires occurred. Higher wind speeds were the strongest predictor when wildfires were larger. However, higher LST values were the strongest predictor when wind was not a significant contributor to the model. These conclusions determine that large wildfires are mostly explained by wind, and when wind is not a significant contributor, then LST takes on that role, as these two variables have the ability to dry vegetation and to spread wildfires. This research further establishes the potential for early detections of large wildfires based on wildfire prediction patterns, provides useful information for resource issuance and wildfire management, and enhances general knowledge of the predicted extreme wildfire events in Southern California.
Author: Yi Xiong
Publisher:
ISBN:
Category :
Languages : en
Pages : 166
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Book Description
Motivated partly by analysis of lightning-caused wildfire data from Alberta, this dissertation develops statistical methodology for analyzing event times with missing origins aided by auxiliary information such as associated longitudinal measures and other relevant information prior to the time origin. We begin an analysis of the motivating data to estimate distribution of time to initial attack since a wildfire starts burning with flames, i.e. duration between the start time and initial attack time of a fire, with two conventional approaches: one neglects the missing origin and performs inference on the observed portion of duration and the other views the observation on the event time of interest subject to interval censoring with a pre-determined interval. The counterintuitive/non-informative results of the preliminary analysis lead us to propose new approaches to tackling the issue of missing origin. To facilitate methodological development, we first consider estimation of the duration distribution with independently and identically distributed (iid) observations. We link the unobserved time origin to the available longitudinal measures of burnt areas via the first-hitting-time model. This yields an intuitive and easy-to-implement adaption of the empirical distribution function with the event time data. We establish consistency and weak convergence of the proposed estimator and present its variance estimation. We then extend the proposed approach to studying the association of the duration time with a list of potential risk factors. A semi-parametric accelerated failure time (AFT) regression model is considered together with a Wiener process model using random drift for longitudinal measures. Further, we accommodate the potential spatial correlation of the wildfires by specifying the drift of the Wiener process as a function of covariates and spatially correlated random effects. Moreover, we propose a method to aid the duration distribution estimation with lightning data. It leads to an alternative approach to estimating the distribution of the duration by adapting the Turnbull estimator with interval-censored observations. A prominent byproduct of this approach is an estimation procedure for the distribution of ignition time using all the lightning data and the sub-sampled data. The finite-sample performance of proposed approaches is examined via simulation studies. We use the motivating Alberta wildfire data to illustrate the proposed approaches throughout the thesis. The data analyses and simulation studies show that the two conventional approaches with current data structure could give rise to misleading inference. The proposed approaches provide intuitive, easy-to-implement alternatives to analysis of event times with missing origins. We anticipate the methodology has many applications in practice, such as infectious diseases research.
Author: C. A. Brebbia
Publisher: WIT Press
ISBN: 1845645847
Category : Nature
Languages : en
Pages : 259
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Book Description
Forest fires analysis and mitigation requires the development of computer codes that can take into consideration a large number of different parameters. The papers in this book, presented at the third in a successful series on the topic, cover the latest research and applications of available computational tools to analyse and predict the spread of forest fires in an attempt to prevent or reduce major loss of life and property as well as damage to the environment. Featured topics include: Risk and Vulnerability Assessment; Computational Methods and Experiments; Environmental Impact Models; Air Pollution and Health Risk Models; Eco-Remediation Models; Decision Support Systems;Monitoring Systems; Emergency Response Systems; Economic Impact; Human Behaviour and Education, Rural-Urban Interface; Case Studies.
Author: Germán Bianchini
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: National Aeronautics and Space Administration (NASA)
Publisher: Createspace Independent Publishing Platform
ISBN: 9781721801749
Category :
Languages : en
Pages : 24
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Book Description
Forest and grass fires cause economic losses in the billions of dollars in the U.S. alone. In addition, boreal forests constitute a large carbon store; it has been estimated that, were no burning to occur, an additional 7 gigatons of carbon would be sequestered in boreal soils each century. Effective wildfire suppression requires anticipation of locales and times for which wildfire is most probable, preferably with a two to four week forecast, so that limited resources can be efficiently deployed. The United States Forest Service (USFS), and other experts and agencies have developed several measures of fire risk combining physical principles and expert judgment, and have used them in automated procedures for forecasting fire risk. Forecasting accuracies for some fire risk indices in combination with climate and other variables have been estimated for specific locations, with the value of fire risk index variables assessed by their statistical significance in regressions. In other cases, the MAPSS forecasts [23, 241 for example, forecasting accuracy has been estimated only by simulated data. We describe alternative forecasting methods that predict fire probability by locale and time using statistical or machine learning procedures trained on historical data, and we give comparative assessments of their forecasting accuracy for one fire season year, April- October, 2003, for all U.S. Forest Service lands. Aside from providing an accuracy baseline for other forecasting methods, the results illustrate the interdependence between the statistical significance of prediction variables and the forecasting method used. Glymour, Clark Ames Research Center
Author:
Publisher:
ISBN:
Category : Forest fires
Languages : en
Pages : 14
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