The Fire and Smoke Model Evaluation Experiment—A Plan for Integrated, Large Fire–Atmosphere Field Campaigns

The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models

An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field

This paper investigates the task assignment problem for a team of autonomous aerial/marine vehicles driven by constant thrust and maneuvering in a planar lateral drift field. The aim is to minimize the total traveling time in order to guide the vehicles to deliver a number of customized sensors to a set of target points with different sensor demands in the drift field. To solve the problem, we consider together navigation strategies and target assignment algorithms; the former minimizes the traveling time between two given locations in the drift field and the latter allocates a sequence of target locations to each vehicle. We first consider the effect of the weight of the carried sensors on the speed of each vehicle, and construct a sufficient condition to guarantee that the whole operation environment is reachable for the vehicles. Then from optimal control principles, time-optimal path planning is carried out to navigate each vehicle from an initial position to its given target location. Most importantly, to assign the targets to the vehicles, we combine the virtual coding strategy, multiple offspring method, intermarriage crossover strategy, and the tabu search mechanism to obtain a co-evolutionary multi-population genetic algorithm, short-named CMGA. Simulations on sensor delivery scenarios in both fixed and time-varying drift fields are shown to highlight the satisfying performances of the proposed approach against popular greedy algorithms

Online estimation of ocean current from sparse GPS data for underwater vehicles

© 2019 IEEE. Underwater robots are subject to position drift due to the effect of ocean currents and the lack of accurate localisation while submerged. We are interested in exploiting such position drift to estimate the ocean current in the surrounding area, thereby assisting navigation and planning. We present a Gaussian process (GP)-based expectation-maximisation (EM) algorithm that estimates the underlying ocean current using sparse GPS data obtained on the surface and dead-reckoned position estimates. We first develop a specialised GP regression scheme that exploits the incompressibility of ocean currents to counteract the underdetermined nature of the problem. We then use the proposed regression scheme in an EM algorithm that estimates the best-fitting ocean current in between each GPS fix. The proposed algorithm is validated in simulation and on a real dataset, and is shown to be capable of reconstructing the underlying ocean current field. We expect to use this algorithm to close the loop between planning and estimation for underwater navigation in unknown ocean currents

Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models

As sensors become increasingly compact and dependable in natural environments, spatially-distributed heterogeneous sensor network systems steadily become more pervasive. However, any environmental monitoring system must account for potential data loss due to a variety of natural and technological causes. Modeling a natural spatial region can be problematic due to spatial nonstationarities in environmental variables, and as particular regions may be subject to specific influences at different spatial scales. Relationships between processes within these regions are often ephemeral, so models designed to represent them cannot remain static. Integrating temporal factors into this model engenders further complexity. This dissertation evaluates the use of multilayer perceptron neural network models in the context of sensor networks as a possible solution to many of these problems given their data-driven nature, their representational flexibility and straightforward fitting process. The relative importance of parameters is determined via an adaptive backpropagation training process, which converges to a best-fit model for sensing platforms to validate collected data or approximate missing readings. As conditions evolve over time such that the model can no longer adapt to changes, new models are trained to replace the old. We demonstrate accuracy results for the MLP generally on par with those of spatial kriging, but able to integrate additional physical and temporal parameters, enabling its application to any region with a collection of available data streams. Potential uses of this model might be not only to approximate missing data in the sensor field, but also to flag potentially incorrect, unusual or atypical data returned by the sensor network. Given the potential for spatial heterogeneity in a monitored phenomenon, this dissertation further explores the benefits of partitioning a space and applying individual MLP models to these partitions. A system of neural models using both spatial and temporal parameters can be envisioned such that a spatiotemporal space partitioned by k-means is modeled by k neural models with internal weightings varying individually according to the dominant processes within the assigned region of each. Evaluated on simulated and real data on surface currents of theGulf ofMaine, partitioned models show significant improved results over single global models

Spatiotemporal Modeling of Shorebird Habitat Availability at Rankin Wildlife Management Area, Tennessee

This study examines spatiotemporal patterns of shorebird stopover habitat availability at Rankin Wildlife Management Area (Rankin Bottoms) on the Douglas Reservoir, Tennessee, USA. Rankin Bottoms is a key stopover site for fall migrating shorebirds traveling through the Tennessee River Valley (TRV). In the TRV, the majority of shorebird habitats consist of mudflats created along reservoirs in the fall as the Tennessee Valley Authority (TVA) lowers reservoir levels to prepare for winter and spring rains. Occasional changes to the annual reservoir management cycle enacted by TVA have affected the timing of mudflat exposure and thus the timing of availability of stopover habitats for migrating shorebirds in the TRV. I used high-resolution LiDAR elevation data of the lake bottom along with recorded reservoir stage values from 1972 to the present in a Geographic Information System (GIS) to model mudflat exposure at Rankin Bottoms. I defined model parameters that allow me to report values for shorebird habitat availability as it changes through the migration period, and modeled these values for three reservoir management scenarios including the current management scenario. I used average reservoir stage data for the 1972-1990 and 1991-2003 reservoir management scenarios and predictive reservoir stage data for the current ROS management regime as input into this model. My results suggest that changes made in 1991, and more so in 2004, delay the creation of habitat at Rankin Bottoms to the beginning of August, but extend habitat availability further into the winter. Under the most recent management scenario implemented by TVA in 2004, the 15 species of shorebirds known to potentially arrive in the TRV in July will find their habitat at Rankin Bottoms inundated upon their arrival. Based on these models, shorebird-optimal reservoir management guidelines have been prepared for TVA to consider as part of their adaptive management plan. The findings of this study are presented in the Rankin Wildlife Management Area Shorebird Habitat Viewer, a visualization tool, which offers 3-Dimensional animations of habitat availability at Rankin Bottoms. Using this tool, interested parties can compare and contrast the amount of available habitat for any day of the migration period under the historic and current management regimes. The models generated for this study can help TVA’s reservoir managers to assess the habitat impacts of proposed reservoir management activities now and in the future. The methods developed in this study are not specific to the phenomenon of shorebird migration or to the TVA river system. They may be used by reservoir and wildlife managers elsewhere to assess the habitat consequences of different management strategies and ultimately determine the optimal management strategy for species of concern

Dynamiques hydrologiques d’un petit bassin versant arctique, rivière Niaqunguk, Iqaluit, Nunavut

Même si des avancées importantes ont été réalisées au cours des dernières années quant à la compréhension des dynamiques hydrologiques des environnements de pergélisol, un manque de connaissances limite toujours notre capacité à prédire précisément les impacts dynamiques hydrologiques des bassins versants Arctique. Ce mémoire se divise selon deux principales études effectuées respectivement à l’échelle d’une pente et à l’échelle du bassin versant de la rivière Niaqunguk, près d’Iqaluit, Nunavut, ont été réalisées. La première étude démontre que les dynamiques de drainage d’une pente sont intrinsèquement liées aux patrons de dégel de la couche active du pergélisol. Les connaissances développées à l’échelle de la pente ont été essentielles à la compréhension des dynamiques émergentes à l’échelle du bassin versant. Quatre périodes hydrologiques distinctes correspondant au début de la fonte des neiges, à la récession de la fonte, au débit de base et aux crues estivales ont été délimitées selon les caractéristiques physicochimiques de l’eau échantillonnée dans le bassin versant de la rivière Niaqunguk. Les principaux processus associés à ces périodes sont respectivement la contribution de la fonte des neiges, la distinction entre la contribution des neiges résiduelles et des lacs, les écoulements à travers la portion minérale de la couche active et les écoulements en surface à travers la microtopographie de la couche organique. Globalement, cette recherche propose des connaissances fondamentales qui sont essentielles à la compréhension des dynamiques hydrologiques en milieu de pergélisol. De plus, elle fournit des connaissances significatives pour une exploitation durable de la rivière comme source d’eau potable.Despite recent advances in the hydrological knowledge of permafrost regions, precise predictions or modelling of hydrological dynamics in Arctic watersheds remains difficult. In this master thesis a combination of field measurements, chemistry and statistical analysis were conducted at the hillslope scale and at watershed scale within the Niaqunguk River catchment, to improve our understanding of the processes controlling the water delivery to rivers in permafrost landscape. The first study show that hillslope drainage dynamics were intrinsically linked to permafrost active layer thawing patterns. The microtopography of the surficial organic layer has a great influence on the distribution across the hillslope, by first enhancing the accumulation of water in surficial depressions, which in return enhance localized thaw and the accumulation of water in subsurface depressions. The second study conducted at the watershed scale allowed to delineated, based on the particularities of the water chemistry along the river network, four distinct periods, corresponding respectively to early snowmelt, snowmelt recession, baseflow and summer rainfall events. Since dominant hydrological processes within the watershed changed from one period to another, this period division is relevant the understand the behaviour of the river system. The primary processes associated with each of the four periods were: snowmelt contributions, the distinction between late lying snowpack melt and lake contributions, subsurface flows through mineral deposits and surface flows through the organic layer. Fundamental hydrological knowledge generated in this research will greatly help to improve our ability to predict the response of Arctic hydrological systems to climate changes, but is also indispensable for a sustainable management of a future exploitation of the Niaqunguk River as a potable water for the City of Iqaluit

Optimal steering for kinematic vehicles with applications to spatially distributed agents

The recent technological advances in the field of autonomous vehicles have resulted in a growing impetus for researchers to improve the current framework of mission planning and execution within both the military and civilian contexts. Many recent efforts towards this direction emphasize the importance of replacing the so-called monolithic paradigm, where a mission is planned, monitored, and controlled by a unique global decision maker, with a network centric paradigm, where the same mission related tasks are performed by networks of interacting decision makers (autonomous vehicles). The interest in applications involving teams of autonomous vehicles is expected to significantly grow in the near future as new paradigms for their use are constantly being proposed for a diverse spectrum of real world applications. One promising approach to extend available techniques for addressing problems involving a single autonomous vehicle to those involving teams of autonomous vehicles is to use the concept of Voronoi diagram as a means for reducing the complexity of the multi-vehicle problem. In particular, the Voronoi diagram provides a spatial partition of the environment the team of vehicles operate in, where each element of this partition is associated with a unique vehicle from the team. The partition induces, in turn, a graph abstraction of the operating space that is in a one-to-one correspondence with the network abstraction of the team of autonomous vehicles; a fact that can provide both conceptual and analytical advantages during mission planning and execution. In this dissertation, we propose the use of a new class of Voronoi-like partitioning schemes with respect to state-dependent proximity (pseudo-) metrics rather than the Euclidean distance or other generalized distance functions, which are typically used in the literature. An important nuance here is that, in contrast to the Euclidean distance, state-dependent metrics can succinctly capture system theoretic features of each vehicle from the team (e.g., vehicle kinematics), as well as the environment-vehicle interactions, which are induced, for example, by local winds/currents. We subsequently illustrate how the proposed concept of state-dependent Voronoi-like partition can induce local control schemes for problems involving networks of spatially distributed autonomous vehicles by examining different application scenarios.PhDCommittee Chair: Tsiotras Panagiotis; Committee Member: Egerstedt Magnus; Committee Member: Feron Eric; Committee Member: Haddad Wassim; Committee Member: Shamma Jef

Time Series Analysis of fMRI Data: Spatial Modelling and Bayesian Computation

Time series analysis of fMRI data is an important area of medical statistics for neuroimaging data. The neuroimaging community has embraced mean-field variational Bayes (VB) approximations, which are implemented in Statistical Parametric Mapping (SPM) software. While computationally efficient, the quality of VB approximations remains unclear even though they are commonly used in the analysis of neuroimaging data. For reliable statistical inference, it is important that these approximations be accurate and that users understand the scenarios under which they may not be accurate. We consider this issue for a particular model that includes spatially-varying coefficients. To examine the accuracy of the VB approximation we derive Hamiltonian Monte Carlo (HMC) for this model and conduct simulation studies to compare its performance with VB. As expected we find that the computation time required for VB is considerably less than that for HMC. In settings involving a high or moderate signal-to-noise ratio (SNR) we find that the two approaches produce very similar results suggesting that the VB approximation is useful in this setting. On the other hand, when one considers a low SNR, substantial differences are found, suggesting that the approximation may not be accurate in such cases and we demonstrate that VB produces Bayes estimators with larger mean squared error (MSE). A real application related to face perception is also carried out. Overall, our work clarifies the usefulness of VB for the spatiotemporal analysis of fMRI data, while also pointing out the limitation of VB when the SNR is low and the utility of HMC in this case