Exploring helical dynamos with machine learning

We use ensemble machine learning algorithms to study the evolution of magnetic fields in magnetohydrodynamic (MHD) turbulence that is helically forced. We perform direct numerical simulations of helically forced turbulence using mean field formalism, with electromotive force (EMF) modeled both as a linear and non-linear function of the mean magnetic field and current density. The form of the EMF is determined using regularized linear regression and random forests. We also compare various analytical models to the data using Bayesian inference with Markov Chain Monte Carlo (MCMC) sampling. Our results demonstrate that linear regression is largely successful at predicting the EMF and the use of more sophisticated algorithms (random forests, MCMC) do not lead to significant improvement in the fits. We conclude that the data we are looking at is effectively low dimensional and essentially linear. Finally, to encourage further exploration by the community, we provide all of our simulation data and analysis scripts as open source IPython notebooks.Comment: accepted by A&A, 11 pages, 6 figures, 3 tables, data + IPython notebooks: https://github.com/fnauman/ML_alpha

MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities

Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is built based on the former liveability classes. The score is predicted using Random Forest for regression and achieved a recall of 0.83 with 10-fold cross-validation. Afterwards, Exploratory Factor Analysis is applied to select the actions present in the model. The resulting indicators are divided into 5 groups, and 12 actions are generated. The performance of four optimisation algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three established criteria of quality: cardinality, the spread of the solutions, spacing, and the resulting score and number of turns. Although all four algorithms show different strengths, eps-MOEA is selected to be the most suitable for this problem. Ultimately, the simulation incorporates the model and the selected AI module in a GUI written in the Kivy framework for Python. Tests performed on users show positive responses and encourage further initiatives towards joining technology and public applications.Comment: 16 page

Modelling Associations between Public Understanding, Engagement and Forest Conditions in the Inland Northwest, USA.

Abstract Opinions about public lands and the actions of private non-industrial forest owners in the western United States play important roles in forested landscape management as both public and private forests face increasing risks from large wildfires, pests and disease. This work presents the responses from two surveys, a random-sample telephone survey of more than 1500 residents and a mail survey targeting owners of parcels with 10 or more acres of forest. These surveys were conducted in three counties (Wallowa, Union, and Baker) in northeast Oregon, USA. We analyze these survey data using structural equation models in order to assess how individual characteristics and understanding of forest management issues affect perceptions about forest conditions and risks associated with declining forest health on public lands. We test whether forest understanding is informed by background, beliefs, and experiences, and whether as an intervening variable it is associated with views about forest conditions on publicly managed forests. Individual background characteristics such as age, gender and county of residence have significant direct or indirect effects on our measurement of understanding. Controlling for background factors, we found that forest owners with higher self-assessed understanding, and more education about forest management, tend to hold more pessimistic views about forest conditions. Based on our results we argue that self-assessed understanding, interest in learning, and willingness to engage in extension activities together have leverage to affect perceptions about the risks posed by declining forest conditions on public lands, influence land owner actions, and affect support for public policies. These results also have broader implications for management of forested landscapes on public and private lands amidst changing demographics in rural communities across the Inland Northwest where migration may significantly alter the composition of forest owner goals, understanding, and support for various management actions

Examining Connections between Gendered Dimensions of Inequality and Deforestation in Nepal

The United Nations recognizes empowering women as a key component of achieving numerous development-related goals. Qualitative studies suggest that communities where men and women have equal levels of agency over resource allocation and land tenure sometimes experience decreases in forest degradation and deforestation, all else being equal. However, these patterns are spatially heterogeneous, as are patterns of gender inequality in terms of land tenure and agency. This paper uses data from the Demographic and Health Surveys (DHS) to quantify the relationship between gender inequality and ecosystem degradation using three linear regression models, Empirical Bayesian Kriging, and mapping the intersections between gender inequality and deforestation. Results from LASSO, Ordinary Least Squares, and Stepwise regression models show that there is no linear relationship between gender inequality and deforestation. Additionally, the distributions of gender inequality as it pertains to land tenure and deforestation are highly heterogeneous over space, indicating potential sociocultural and sociodemographic factors not captured in my data. Further work should focus on identifying ways to incorporate complex gender dynamics into environmental planning at multiple levels of forest governance

Landscape-scale establishment and population spread of yellow-cedar (Callitropsis nootkatensis) at a leading northern range edge

Thesis (M.S.) University of Alaska Fairbanks, 2016Yellow-cedar is a long-lived conifer of the North Pacific Coastal Temperate Rainforest region that is thought to be undergoing a continued natural range expansion in southeast Alaska. Yellow-cedar is locally rare in northeastern portions of the Alexander Archipelago, and the fairly homogenous climate and forest conditions across the region suggest that yellow-cedar's rarity could be due to its local migrational history rather than constraints on its growth. Yellow-cedar trees in northern range edge locations appear to be healthy, with few dead trees; additionally, yellow-cedar tend to be younger than co-dominant mountain and western hemlock trees, indicating recent establishment in existing forests. To explore yellow-cedar's migration in the region, and determine if the range is expanding into unoccupied habitat, I located 11 leading edge yellow-cedar populations near Juneau, Alaska. I used the geographic context of these populations to determine the topographic, climatic, and disturbance factors associated with range edge population establishment. I used those same landscape variables to model suitable habitat for the species at the range edge. Based on habitat modeling, yellow-cedar is currently only occupying 0.8 percent of its potential landscape niche in the Juneau study area. Tree ages indicate that populations are relatively young for the species, indicating recent migration, and that most populations established during the Little Ice Age climate period (1100 -- 1850). To determine if yellow-cedar is continuing to colonize unoccupied habitat in the region, I located 29 plots at the edges of yellow-cedar stands to measure regeneration and expansion into existing forest communities. Despite abundant suitable habitat, yellow-cedar stand expansion appears stagnant in recent decades. On average, seedlings only dispersed 4.65 m beyond stand boundaries and few seedlings reached mature heights both inside and outside of existing yellow-cedar stands. Mature, 100 --200-year-old trees were often observed abruptly at stand boundaries, indicating that most standboundaries have not moved in the past ~150 years. When observed, seedlings were most common in high light understory plant communities and moderately wet portions of the soil drainage gradient, consistent with the species' autecology in the region. Despite an overall lack of regeneration via seed, yellow-cedar is reproducing via asexual layering in high densities across stands. Layering may be one strategy this species employs to slowly infill habitat and/or persist on the landscape until conditions are more favorable for sexual reproduction. This study leads to a picture of yellow-cedar migration as punctuated, and relatively slow, in southeast Alaska. Yellow-cedar's migration history and currently limited spread at the northeastern range edge should be considered when planning for the conservation and management of this high value tree under future climate scenarios