| Lands provide basic materials for human life. The demand for land is constantly increasing with population growth and economic development. It is a major issue for us that how to rational allocate lands for human production and living with limited land resourses. It is also essential to ensure the sustainable use of land resources and harmonize the relationship between human and land. Land-use suitability assessment (LSA) aims to rational use lands according to specific type of lands, identifies land properties and describes land suitability extent. LSA is an important basis for land-use planning and decision-making. It is of great significance for sustainable development and usage of lands. But it remains problems in traditional methods of solving LSA, such as subjective assessment, inefficient evaluation and difficult to do long-term evaluation. To cope with these problems, some artificial intelligence (AI) methods have been applied to this research field. However, current usages of AI methods are relatively homogeneous. Many new advanced AI methods have not been used. It is necessary to make further study and discussion.The discussion in this paper starts from Analytical Hierarchy Procedure (AHP), which is a common method in LSA. The shortcomings of AHP have been pointed out and we use sensitivity analysis to examine the uncertainties in it. In this study, it is also attempt to use the suitability rule classification method to replace AHP to reduce the subjective effects of criteria weights to the assessment results. In order to conduct the potential suitability assessment, the paper also tries to make use of geographical simulation system to reveal the land-use suitability conversion rules under certain development pattern, which provides a better basis for sustainable land-use planning.The main scientific work and findings of this paper include:(1) Propose the conception of land-use suitability simulation.In this paper, the land-use suitability simulation is defined as the method of implementing potential LSA using geographical simulation system. It utilises the characteristic of geographical simulation system that it could simulate complex systems to support potential LSA. The method can reveal implicated content in suitability distribution form under specified land-use type, discover prospective rules.(2) Simulation of potential land-use suitability using the mechanism of cellular automata (CA).According to the proposed concept of land-use suitability simulation, a CA based suitability simulation method has been designed. This is the first use of CA theory in the field of land-use suitability study to achieve the evaluation. The work is on the basis of three assumptions:(a) neighbour effect of land-use suitability (b) land-use development mode (c) restricted land-use suitability factors. CA simulation of potential land use suitability makes land evaluation more standardized and accurate, so as to make the work better meet the actual requirements of planners and decision-makers, and provide better solutions and technical support for sustainable use of lands.(3) Land-use suitability classification rules discovery for LSA using Ant colony optimization (ACO).This paper introduces new artificial intelligence theory - ACO, to obtain classification rules for LSA. The method avoids the subjective factors of weighting in AHP, reduces the interference of weight uncertainty to the evaluation process.The paper uses the definition of rules in rule-based system as reference, expresses suitability rules as a conditional which is in IF-THEN form. Meanwhile, the knowledge information obtained from the samples is also converted to this form and imported to the training set, which supply ACO discover classification rules. The data structure of classification rule discovery based on training set is abstracted with the inspiration of optimal path mechanism of ACO. It is utilized to discover rules for land-use suitability classification and generate the evaluation result map.(4) Spatial weight sensitivity analysis.The spatial weight sensitivity analysis makes use of improved OAT (one-at-a-time) methods. It explores the stability of the evaluation results, the relative weight sensitivity of criteria, and the problem that how to reduce the uncertainty in multi-criteria decision-making methods, etc. The results are presented in different forms including tables, charts and thematic maps, which make it easy to identify the geographical locations of high sensitivity. (5) Development of LSA Tool (LSA-GIS).Based on Microsoft C #. NET development platform, a model tool named LSA-GIS has been developed using ESRI ArcGIS Engine components, Mathworks MATLAB embedded development components. Critical flow charts and sample codes are also presented in this paper.The implementation of the tool focuses on the interaction design, which specially pays attention to user experience, to improve the efficiency of using the tool. Interaction design carries out unified modeling (UML) approach, which makes the design process more standardized and builds a good basis for function extension of the modelling tool in the future.(6) Case study of irrigated agricultural land-use suitability.This paper selected the Macintyre Brook catchment, Australia, as the study area. Three methods:AHP, land-use suitability classification rule discovery and CA based land-use suitability simulation have been conducted to assess and analyse irrigated agricultural land-use suitability. These three evaluation results were separately compared in spatial context according to certain regulation. It proved that the methods are reasonable, feasible. But there still existed limitation. Experiments showed that LSA-GIS modelling tool has generated satisfied results in the evaluation of study area. The methods and the tool are able to be popularised to complete LSA work in other study areas.
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