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Aerial Spray Deposition Management Using the Genetic Algorithm
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26. Aerial Spray Deposition Management Using the Genetic Algorithm
W. D. Potter4  , W. Bi4, D. Twardus5, H. Thistle5, M. J. Twery5, J. Ghent5 and M. Teske6
| (4) |
Artificial Intelligence Center, University of Georgia, USA |
| (5) |
United States Department of Agriculture, Forest Service, USA |
| (6) |
Continuum Dynamics, Princeton, NJ |
Abstract
The AGDISP Aerial Spray Simulation Model is used to predict the deposition of spray material released from an aircraft. The
prediction is based on a well-defined set of input parameter values (e.g., release height, and droplet size) as well as constant
data (e.g., aircraft and nozzle type). But, for a given deposition, what are the optimal parameter values? We use the popular
Genetic Algorithm to heuristically search for an optimal or near-optimal set of input parameters needed to achieve a certain
aerial spray deposition.
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