Modeling and implementation of classification rule discovery by ant colony optimisation for spatial land-use suitability assessment

This paper presents an integrated modeling method for multi-criteria land-use suitability assessment (LSA) using classification rule discovery (CRD) by ant colony optimisation (ACO) in ArcGIS. This new attempt applies artificial intelligent algorithms to intelligentise LSA by discovering suitability classification rules. The methodology is implemented as a tool called ACO–LSA. The tool can generate rules which are straightforward and comprehensible for users with high classification accuracy and simple rule list in solving CRD problems. A case study in the Macintyre Brook Catchment of southern Queensland in Australia is proposed to demonstrate the feasibility of this new modeling technique. The results have addressed the major advantages of this novel approach.

Research highlights
► Land-use suitability classification based on ant colony optimisation.
► Intelligentising land suitability assessment by discovering suitability classification rules.
► A GIS-based ACO–LSA tool which can discover appropriate classification rules on the basis of expert knowledge and field survey in a spatial context.