MODELING PESTICIDE LEACHING FROM GOLF-COURSES USING ARTIFICIAL NEURALNETWORKS

The objective of this work was to develop a computer model that accura tely predicted pesticide leaching of pesticides applied to turfgrass a reas. After much investigation, the number of inputs used to train the Artificial Neural Networks (ANN) was reduced to pesticide solubility, pesticide soil:water partitioning coefficient (Koc), time after appli cation, and the irrigation application practice. For comparison reason s, Ist and 2nd order polynomial regression models were developed. An a rtificial neural network is a form of artificial intelligence enabling the program to learn relationships instead of the relationships being defined by the programmer. The ANN proved to be a feasible modeling t echnique for pesticide leaching. The ANN predictions for the test case s had much less error than the Ist or 2nd order regression equations ( sum of the squared error between measured and predicted values were 17 .4, 528.4, and 522.3, respectively). An interactive World Wide Web (ww w) site has been developed where this artificial neural network can be accessed (http//www.eece.ksu.edu/-starret/KTURF/). The www site is ca lled KTURF and is accessible through the Internet. Used as an assessme nt tool, KTURF can help to reduce pesticide leaching by allowing users to experiment with different pesticide/irrigation schemes. They can t hus optimize their practices to reduce the likelihood of pesticide lea ching beyond the rootzone.