Using Artificial Neural Networks to Predict Local Disease Risk Indicators with Multi-Scale Weather, Land and Crop Data
World Water and Environmental Resources Congress 2003, , , 2003
Abstract: The risk of fungal and bacterial diseases in a crop canopy can be predicted using disease risk models with specific environmental conditions (microclimate data such as temperature, relative humidity, solar radiation, wind speed, and surface wetness duration (SWD)). Unfortunately, the inconvenience and uncertainty associated with monitoring key variables such as SWD at the local crop scale prevent existing disease risk models from being used with reliability. A suite of recurrent Artificial Neural Networks (ANNs) has been developed to estimate key environmental variables (specifically SWD) at local crop scales from local and regional weather station data and site specific sensing data. The selected ANN combines two statistical methods to accomplish this spatial mapping (a K-nearest means classifier and a Bayesian classifier), while the recurrent nature of the ANNs provide a means of forecasting in time. The overall goal is the development of web-based Geographical Information System (GIS) software incorporating ANN models that ultimately can be combined with existing disease models to improve maps of disease risk at fine spatial and temporal scales.
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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).