Applying neural net technology for multi-objective land use planning

Neural nets have been studied for many years in an attempt to achieve human-like performance in fields where problems are unlikely to be solved efficiently by using traditional methods. Yet there have been very few practical applications of neural nets particularly in resource management. In this study, the neural net technology is adopted as a decision support system for land use planning. Computer simulation software for such a net is developed and an empirical analysis is undertaken for the Peace River Region of British Columbia in Canada to deal with problems related to multi-objective land planning. This neural net model incorporates biophysical and socio-economic data from various sources and reflects the interactions between different land use objectives. Given a set of land use options and their geographical requirements, the neural net identifies the desired land use options among the regions. The neural net model applied in this study provides an introduction to a useful computer technology for natural resource management.