Spatial variability in periglacial terrain conditions, northwestern Canada

2021 ◽  
Author(s):  
Peter Morse ◽  
Wendy Sladen ◽  
Steve Kokelj ◽  
Ryan Parker ◽  
Sharon Smith ◽  
...  
Keyword(s):  
Decision Making ◽  
Systems Approach ◽  
Mass Movement ◽  
Spatial Models ◽  
Data Sets ◽  
Mackenzie Delta ◽  
Land Area ◽  
Diverse Range ◽  
Surficial Geology ◽  
Physiographic Regions

<p>Throughout much of northern Canada there is an inadequate knowledge of permafrost and periglacial terrain conditions, which impedes development of climate-resilient northern infrastructure, identification of potential geohazards, decision making regarding resource development, and inferring past and future landscape evolution. Using a land systems approach to better understand formation of landscapes and thaw-sensitive terrains of northern Yukon and northwestern Northwest Territories, we aim to describe the permafrost-related landform-sediment assemblages that exist in the region. Permafrost is continuous in the region, but variations in geology, landscape history, climate, relief, ecology, and other natural processes have produced a diverse range of permafrost conditions and landforms. Using the 875 km-long Dempster and Inuvik-to-Tuktoyaktuk highway corridors (DH-ITH) as a regional transect, and high-resolution satellite imagery, a robust methodology was implemented to identify and digitize (at 1:5000 scale) 8793 landforms (589 km<sup>2</sup>) within a 10 km-wide corridor (8530 km<sup>2</sup>) and classify them according to main formational process (hydrological, periglacial, and mass movement). Surficial geology data were extracted from available data sets. Landform densities in all feature classes vary substantially along the transect according to physiographic region and surficial geology. The northern 39% of the corridor is characterized by generally rolling or planar relief, numerous waterbodies (19%), and the remaining land area by mostly morainal (67%), glaciofluvial (12%), lacustrine (7%), and alluvial (7%) deposits. By count, it contains 53% of mapped features and the majority of periglacial (67%) and hydrological (70%) features. In particular, the Tuktoyaktuk Coastlands, Peel Plain, and Mackenzie Delta, contain the greatest density of mapped landforms within the corridor, which cover nearly 23%, 15%, and 15% of the land area of these physiographic regions, respectively. These extents reflect the amount of ground ice and level of permafrost-thaw sensitivity of these regions. In contrast, the physiographic regions of the southern 61% of the study area are characterized by high relative relief, few waterbodies (0.2%), and the land area mainly by colluvial (63%), alluvial (18%), and morainal (14%) deposits. Most mass movement features occur here (85% by count), and are concentrated in the Ogilvie Mountains (n = 1027; 108 km<sup>2</sup>). This feature inventory provides the basis for developing spatial models of landscape-thaw susceptibility, which can inform risk assessment and improve decision making regarding public safety and environmental management.</p>

scholarly journals Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature

2021 ◽  
Vol 8 ◽  
Author(s):  
Steefan Contractor ◽  
Moninya Roughan
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Short Term Memory ◽  
Data Sets ◽  
Gap Filling ◽  
Diverse Range ◽  
Big Data Applications ◽  
Free Data ◽  
Before And After ◽  
Ocean Environment

Ocean data timeseries are vital for a diverse range of stakeholders (ranging from government, to industry, to academia) to underpin research, support decision making, and identify environmental change. However, continuous monitoring and observation of ocean variables is difficult and expensive. Moreover, since oceans are vast, observations are typically sparse in spatial and temporal resolution. In addition, the hostile ocean environment creates challenges for collecting and maintaining data sets, such as instrument malfunctions and servicing, often resulting in temporal gaps of varying lengths. Neural networks (NN) have proven effective in many diverse big data applications, but few oceanographic applications have been tested using modern frameworks and architectures. Therefore, here we demonstrate a “proof of concept” neural network application using a popular “off-the-shelf” framework called “TensorFlow” to predict subsurface ocean variables including dissolved oxygen and nutrient (nitrate, phosphate, and silicate) concentrations, and temperature timeseries and show how these models can be used successfully for gap filling data products. We achieved a final prediction accuracy of over 96% for oxygen and temperature, and mean squared errors (MSE) of 2.63, 0.0099, and 0.78, for nitrates, phosphates, and silicates, respectively. The temperature gap-filling was done with an innovative contextual Long Short-Term Memory (LSTM) NN that uses data before and after the gap as separate feature variables. We also demonstrate the application of a novel dropout based approach to approximate the Bayesian uncertainty of these temperature predictions. This Bayesian uncertainty is represented in the form of 100 monte carlo dropout estimates of the two longest gaps in the temperature timeseries from a model with 25% dropout in the input and recurrent LSTM connections. Throughout the study, we present the NN training process including the tuning of the large number of NN hyperparameters which could pose as a barrier to uptake among researchers and other oceanographic data users. Our models can be scaled up and applied operationally to provide consistent, gap-free data to all data users, thus encouraging data uptake for data-based decision making.

Considering a family systems approach to surrogate decision-making.

2017 ◽  
Vol 35 (1) ◽  
pp. 85-87
Author(s):  
Elizabeth K. Vig ◽  
Janelle S. Taylor ◽  
Ann M. O'Hare
Keyword(s):  
Decision Making ◽  
Family Systems ◽  
Systems Approach ◽  
Surrogate Decision Making ◽  
Surrogate Decision

scholarly journals A top-level model of case-based argumentation for explanation: Formalisation and experiments

2021 ◽  
pp. 1-36
Author(s):  
Henry Prakken ◽  
Rosa Ratsma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Linear Models ◽  
Evaluation Studies ◽  
Data Sets ◽  
Machine Learning Applications ◽  
Level Model ◽  
Similarities And Differences ◽  
Further Development ◽  
Case Based

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

scholarly journals An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

2020 ◽  
Vol 12 (1) ◽  
pp. 580-597
Author(s):  
Mohamad Hamzeh ◽  
Farid Karimipour
Keyword(s):  
Spatial Data ◽  
Spatial Models ◽  
Model Performance ◽  
Essential Elements ◽  
Petroleum System ◽  
Petroleum Exploration ◽  
System Level ◽  
Data Sets ◽  
Petroleum Potential ◽  
Spatial Data Sets

AbstractAn inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

scholarly journals White matter tracts characteristics in habitual decision-making circuit underlie ritual behaviors in anorexia nervosa

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Reza Tadayonnejad ◽  
Fabrizio Pizzagalli ◽  
Stuart B. Murray ◽  
Wolfgang M. Pauli ◽  
Geena Conde ◽  
...  
Keyword(s):  
Decision Making ◽  
Anorexia Nervosa ◽  
White Matter ◽  
Symptom Severity ◽  
Structural Characteristics ◽  
Data Sets ◽  
White Matter Tracts ◽  
Compulsive Behaviors ◽  
Positive Correlations ◽  
Brain Behavior

AbstractAnorexia nervosa (AN) is a difficult to treat, pernicious psychiatric disorder that has been linked to decision-making abnormalities. We examined the structural characteristics of habitual and goal-directed decision-making circuits and their connecting white matter tracts in 32 AN and 43 healthy controls across two independent data sets of adults and adolescents as an explanatory sub-study. Total bilateral premotor/supplementary motor area-putamen tracts in the habit circuit had a significantly higher volume in adults with AN, relative to controls. Positive correlations were found between both the number of tracts and white matter volume (WMV) in the habit circuit, and the severity of ritualistic/compulsive behaviors in adults and adolescents with AN. Moreover, we found a significant influence of the habit circuit WMV on AN ritualistic/compulsive symptom severity, depending on the preoccupations symptom severity levels. These findings suggest that AN is associated with white matter plasticity alterations in the habit circuit. The association between characteristics of habit circuit white matter tracts and AN behavioral symptoms provides support for a circuit based neurobiological model of AN, and identifies the habit circuit as a focus for further investigation to aid in development of novel and more effective treatments based on brain-behavior relationships.

scholarly journals Multi-instrument gravity-wave measurements over Tierra del Fuego and the Drake Passage – Part 1: Potential energies and vertical wavelengths from AIRS, COSMIC, HIRDLS, MLS-Aura, SAAMER, SABER and radiosondes

2016 ◽  
Vol 9 (3) ◽  
pp. 877-908 ◽  
Author(s):  
Corwin J. Wright ◽  
Neil P. Hindley ◽  
Andrew C. Moss ◽  
Nicholas J. Mitchell
Keyword(s):  
Gravity Wave ◽  
Wave Packets ◽  
Lower Thermosphere ◽  
Lower Troposphere ◽  
Drake Passage ◽  
Tierra Del Fuego ◽  
Data Sets ◽  
Meteor Radar ◽  
Diverse Range ◽  
Wide Range

Abstract. Gravity waves in the terrestrial atmosphere are a vital geophysical process, acting to transport energy and momentum on a wide range of scales and to couple the various atmospheric layers. Despite the importance of these waves, the many studies to date have often exhibited very dissimilar results, and it remains unclear whether these differences are primarily instrumental or methodological. Here, we address this problem by comparing observations made by a diverse range of the most widely used gravity-wave-resolving instruments in a common geographic region around the southern Andes and Drake Passage, an area known to exhibit strong wave activity. Specifically, we use data from three limb-sounding radiometers (Microwave Limb Sounder, MLS-Aura; HIgh Resolution Dynamics Limb Sounder, HIRDLS; Sounding of the Atmosphere using Broadband Emission Radiometry, SABER), the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) GPS-RO constellation, a ground-based meteor radar, the Advanced Infrared Sounder (AIRS) infrared nadir sounder and radiosondes to examine the gravity wave potential energy (GWPE) and vertical wavelengths (λz) of individual gravity-wave packets from the lower troposphere to the edge of the lower thermosphere ( ∼  100 km). Our results show important similarities and differences. Limb sounder measurements show high intercorrelation, typically  > 0.80 between any instrument pair. Meteor radar observations agree in form with the limb sounders, despite vast technical differences. AIRS and radiosonde observations tend to be uncorrelated or anticorrelated with the other data sets, suggesting very different behaviour of the wave field in the different spectral regimes accessed by each instrument. Evidence of wave dissipation is seen, and varies strongly with season. Observed GWPE for individual wave packets exhibits a log-normal distribution, with short-timescale intermittency dominating over a well-repeated monthly-median seasonal cycle. GWPE and λz exhibit strong correlations with the stratospheric winds, but not with local surface winds. Our results provide guidance for interpretation and intercomparison of such data sets in their full context.

scholarly journals Broad concepts and messy realities: optimising the application of mental capacity criteria

2021 ◽  
pp. medethics-2021-107571
Author(s):  
Scott Y H Kim ◽  
Nuala B Kane ◽  
Alexander Ruck Keene ◽  
Gareth S Owen
Keyword(s):  
Decision Making ◽  
Clinical Research ◽  
Functional Capacity ◽  
Functional Model ◽  
Relevant Information ◽  
Mental Capacity ◽  
Capacity Assessment ◽  
Diverse Range ◽  
England And Wales ◽  
Mental Capacity Act

Most jurisdictions require that a mental capacity assessment be conducted using a functional model whose definition includes several abilities. In England and Wales and in increasing number of countries, the law requires a person be able to understand, to retain, to use or weigh relevant information and to communicate one’s decision. But interpreting and applying broad and vague criteria, such as the ability ‘to use or weigh’ to a diverse range of presentations is challenging. By examining actual court judgements of capacity, we previously developed a descriptive typology of justifications (rationales) used in the application of the Mental Capacity Act (MCA) criteria. We here critically optimise this typology by showing how clear definitions—and thus boundaries—between the criteria can be achieved if the ‘understanding’ criterion is used narrowly and the multiple rationales that fall under the ability to ‘use or weigh’ are specifically enumerated in practice. Such a typology-aided practice, in theory, could make functional capacity assessments more transparent, accountable, reliable and valid. It may also help to create targeted supports for decision making by the vulnerable. We also discuss how the typology could evolve legally and scientifically, and how it lays the groundwork for clinical research on the abilities enumerated by the MCA.

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