Abstract: Use of traditional statistical techniques is often limited by shortage of observation samples and difference in data measurement scales. Neural network techniques have been extensively explored in many fields for prediction and classification as an alternative to statistical methods. In this paper, a feed forward neural network algorithm for predicting hardwood yield is introduced and evaluated. In addition, we report a data transformation method developed for converting qualitative variable data to quantitative data for use in multiple regression when relatively few samples are available for building prediction models. The method that converts qualitative data into quantitative data is helpful to improve hardwood yield prediction accuracy by multiple linear regression models. In this study, the best prediction results using the neural network technique are obtained.

Key words: Neural network, Multiple regression, Forest yield prediction, Data tranformation