Abstract: We present a clustering methodology that distinguishes management zones in a landfill leachate contaminated groundwater aquifer using only microbiological data for input rather than traditional physiochemical information. The self-organizing map (SOM), an artificial neural network (ANN), is commonly used as a K-means clustering method. The method outperforms many traditional clustering methods on noisy datasets (e.g. high dispersion, outliers, non-uniform cluster densities); and is appropriate when combining the multiple correlated and auto-correlated data associated with most hydrochemical research. We applied an SOM to a set of genome-based microbial community profiles created using terminal restriction fragment length polymorphism (T-RFLP) of the 16S rRNA gene sampled from groundwater monitoring wells in an aquifer contaminated with landfill leachate. We modified the existing algorithm to allow weighting of the input variables according to their relative importance, and added a post-processing radial basis function to estimate group membership between measurement locations auto-correlated in space. We statistically tested the SOM output clusters using a nonparametric MANOVA to identify an optimal number of clusters. The SOM methodology distinguished between tiers of contamination in this multi-contaminant environment using expert knowledge to guide data preprocessing and to weight the input variables. Results showed a composite delineation representative of overall groundwater contamination at the landfill based only on microbiological information. Using a small number of clusters, the SOM distinguished between background and leachate-contaminated sampling locations, whereas with a larger number of clusters it groups across a gradient of groundwater contamination. The landfill leachate application demonstrates that microbial community data can compliment standard analytical analyses for the purpose of delineating spatial zones of groundwater contamination. The success of this research is attributed to communication between the computational and biological scientists. This ensured that the essential nature of the dataset was preserved throughout the computational transformations and that the methodology was optimized for the application.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).