Handling the Back Calculation Problem in Aerial Spray Models Using a Genetic Algorithm

Abstract

FS) has been involved in the development of computer models to simulate deposition from aerial pesticide spraying since the early 1970s. Originally, this work was driven by the need to improve the percentage of aerially sprayed material that actually deposited on a target area. The amount of on-target deposition is a primary factor in determining the level of pest control achieved. A second focus of this modeling work that has become the objective in much of the recent work is to use modeling to determine the amount of sprayed material that does not land on the target area and is defined as “drift��?. It is assumed that drift causes unintended environmental consequences and is a form of environmental pollution. Current model operation requires that the user input a mechanical system including aircraft type and spray system, a volatile fraction, release height and forward speed, meteorological data and other parameter values. The model will then output the deposition prediction across an area including the spray block. It would be more useful from an operational standpoint to input a desired measure of deposition, constrain the model with things that are fixed (an applicator may be limited to one aircraft type for instance) and then let the model optimize on, for example, nozzle type or release height. This desired facility is called back calculation and is the focus of our research efforts using the genetic algorithm. This chapter covers our Spray Advisor Genetic Algorithm system for optimizing aerial spray parameters. 1.