Recognizing Saltwater Recreational Angers’ Motivations Using Multilayer Perceptron Neural Network

Abstract

The purpose of this study was to examine saltwater recreational anglers’ answers to the fifteen statements regarding the importance of fishing trips, and to classify groups exhibiting common patterns of responses from individuals’ recreational fishing motivations using the data extracted from the database collected from the 2013 National Saltwater Angler Survey. Using the factor analysis, the fifteen statements were reduced into five dimensions, named catch, information, site preferences, social, and management. Empirical results based on the k-means clustering analysis identified three different saltwater recreational angler groups, named catch and social, site choice, and fishing related groups. Results of the discriminant analysis indicated that cluster means were significantly different. The multilayer perceptron neural network model was utilized as a predictive model in deciding the classification of saltwater anglers based on recreational fishing motivations. From an architectural perspective, it showed a 15-9-3 neural network construction. This study may provide insight into the information about what types of saltwater recreational anger groups exist and identifying unknown groups in the data set for saltwater recreational fishing planning and management purposes.