Toward Optimal Calibration of the SLEUTH Land Use Change Model

Abstract

SLEUTH is a computational simulation model that uses adaptive cellular automata to simulate the way cities grow and change their surrounding land uses. It has long been known that models are of most value when calibrated, and that using back-casting (testing against known prior data) is an effective calibration method. SLEUTH's calibration uses the brute force method: every possible combination and permutation of its control parameters is tried, and the outcomes tested for their success at replicating prior data. Of the SLEUTH calibration approaches tried so far, there have been several suggested rules to follow during the brute force procedure to deal with problems of tractability, most of which leave out many of the possible parameter combinations. In this research, we instead attempt to create the complete set of possible outcomes with the goal of examining them to select the optimum from among the millions of possibilities. The self-organizing map (SOM) was used as a data reduction method to pursue the isolation of the best parameter sets, and to indicate which of the existing 13 calibration metrics used in SLEUTH are necessary to arrive at the optimum. As a result, a new metric is proposed that will be of value in future SLEUTH applications. The new measure combines seven of the current measures, eight if land use is modeled, and is recommended as a way to make SLEUTH applications more directly comparable, and to give superior modeling and forecasting results.