Ant Colony Optimisation based land use suitability classification

This paper presents a new land use suitability classification (LSC) method on the basis of Ant Colony Optimisation (ACO), which is one kind of AI techniques. ACO algorithm can be used to discover suitability classification rules according to training cases. Classification rules and training cases are all organised in the form of IF-THEN, which generally incorporates practical human knowledge. To implement ACO based LSC, a tool was developed using ArcGIS Engine component in .NET framework. The tool provides some useful functions and interfaces for the integration of spatial data input, sampling of training cases, rule classification discovery and LSC mapping. A case study in the Macintyre Brook Catchment of southern Queensland in Australia is proposed. The tool was used to process land use suitability classification in the study area for irrigated agriculture. The resultant map was then compared with present irrigated land to show spatial distribution of irrigated land suitability and to reveal future potential of land use development in this area. Further analysis was conducted to demonstrate the feasibility of ACO method. The parameter values were adjusted to explore the robustness of parameter settings. We also compared the ACO method with C4.5 which is a kind of decision tree algorithm. It has been found that ACO method can produce simpler rule list with slightly reduced classification accuracy. Therefore, in our point of view, although with it limitation, the ACO method is a practicable and efficient approach, and worth more research.