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Artificial Neural Networks for the Prediction of Channel Geomorphic Condition and Stream Sensitivity

World Environmental and Water Resources Congress 2007, , , 2007


Status: Published

Citations:

Cite: [bibtex]


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Abstract: Stream channel and watershed management requires an interdisciplinary approach by multiple stakeholders with often disparate goals and objectives. Even single management goals (e.g. mitigation of property loss due to stream bank erosion and flooding, restoring stream channels and aquatic species habitat) require solutions that span multiple spatial and temporal scales. To deal with the complexity associated with goals such as reducing fluvial erosion hazards, sediment and nutrient loading and threats to aquatic habitat associated with geomorphic instability, the Vermont Agency of Natural Resources' River Management Program has been developing and testing 1) protocols for conducting field-based and remote sensing data and 2) a geographical information system (GIS)-based tool since 1999. These protocols and tools facilitate an understanding of channel instability and assist in developing strategies at appropriate scales to restore channel equilibrium. A hierarchical system of data-driven artificial neural networks (ANNs) has been developed to enhance these existing GIS-based watershed management tools for predicting channel geomorphic condition and inherent sensitivity at the reach scale. We research and develop these ANNs as an alternative model that can incorporate large amounts of data for use in the operational management of watersheds. The ANNs have been trained and tested on remotely sensed and in-field data along with corresponding expert opinions of geomorphic condition and sensitivity. The focus is performing sensitivity analyses and addressing inadequacies associated with assimilation activities that are closely tied to large amounts of hydrological-relevant remote sensing and field data.



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Joshua Bongard - Department of Computer Science, Associate Professor

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.


  • Stacks Image 525371
    Josh Bongard, Victor Zykov, Hod Lipson. Resilient Machines Through
    Continuous Self-Modeling.
    Science 314, 1118 (2006). [Journal Page]
  • Stacks Image 525379
    Joey Anetsberger and Josh Bongard. Robots can ground crowd-proposed symbols by forming theories of group mind. Proceedings of the Artificial Life Conference 2016. [Link to Proceedings]
  • Stacks Image 525375
    Sam Kriegman, Nick Cheney, and Josh Bongard. How morphological development can guide evolution. arXiv 2017. [arXiv]


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Chris Danforth -Department of Mathematics and Statistics, Flint Professor of Mathematical, Natural, and Technical Sciences

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.

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    Peter Sheridan Dodds , Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss, Christopher M. Danforth. Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter. PLoS ONE 2011. [Journal Page].
  • Stacks Image 525314
    Lewis Mitchell , Morgan R. Frank, Kameron Decker Harris, Peter Sheridan Dodds, Christopher M. Danforth. The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place. PLoS ONE 2013. [Journal Page].
  • Stacks Image 525310
    Andrew G Reece and Christopher M Danforth. Instagram photos reveal predictive markers of depression. EPJ Data Science 2017. [Journal Page].


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Laurent Hébert-Dufresne - Assistant Professor, Computer Science

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.

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    Laurent Hébert‐Dufresne Adam F. A. Pellegrini Uttam Bhat Sidney Redner Stephen W. Pacala Andrew M. Berdahl. Edge fires drive the shape and stability of tropical forests. Ecology Letters 2018. [Journal Page]
  • Stacks Image 525335
    Samuel V. Scarpino, Antoine Allard, Laurent Hébert-Dufresne. The effect of a prudent adaptive behaviour on disease transmission. Nature Physics 2016. [Journal Page]
  • Stacks Image 525339
    Laurent Hébert-Dufresne, Joshua A. Grochow, Antoine Allard. Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition. Nature Scientific Reports 2016. [Journal Page]


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Paul Hines - School of Engineering, Associate Professor

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.

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    Paul D. H. Hines, Ian Dobson, Pooya Rezaei. Cascading Power Outages Propagate Locally in an Influence Graph That is Not the Actual Grid Topology. IEEE Transactions on Power Systems ( Volume: 32, Issue: 2, March 2017 ). [Journal Page]
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    Mert Korkali, Jason G. Veneman, Brian F. Tivnan, James P. Bagrow & Paul D. H. Hines. Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependence. Scientific Reports volume 7, Article number: 44499 (2017. [Journal Page]
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    Pooya Rezaei, Paul D. H. Hines, Margaret J. Eppstein. Estimating Cascading Failure Risk With Random Chemistry. IEEE Transactions on Power Systems ( Volume: 30, Issue: 5, Sept. 2015 ). [Journal Page]


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James Bagrow - Assistant Professor, Department of Mathematics and Statistics

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).

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    Y.-Y. Ahn, J. P. Bagrow and S. Lehmann. Link communities reveal multiscale complexity in networks. Nature, 466: 761-764 (2010). [Journal Page].
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    M. R. Frank, J. R. Williams, L. Mitchell, J. P. Bagrow, P. S. Dodds, C. M. Danforth. Constructing a taxonomy of fine-grained human movement and activity motifs through social media. In preparation. (2015). [Journal Page].
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    J. P. Bagrow and L. Mitchell. The quoter model: a paradigmatic model of the social flow of written information. To appear, Chaos (2018). [Journal Page].