Modeling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools

Abstract

Over half of the earth's terrestrial surface has been modified by humans. This modification is called land use change and its pattern is known to occur in a non-linear way. The land use change modeling community can advance its models using data mining tools. Here, we present three data mining land use change models, one based on Artificial Neural Network (ANN), another on Classification And Regression Trees (CART) and another Multivariate Adaptive Regression Splines (MARS). We reconfigured the three data mining models to concurrently simulate multiple land use classes (e.g. agriculture, forest and urban) in South-Eastern Wisconsin (SEWI), USA (time interval 1990–2000) and in Muskegon River Watershed (MRW), Michigan, USA (time interval 1978–1998). We compared the results of the three data mining tools using relative operating characteristic (ROC) and percent correct match (PCM). We found that ANN provided the best accuracy in both areas for three land use classes (e.g. urban, agriculture and forest). In addition, in both regions, CART and MARS both showed that forest gain occurred in areas close to current forests, agriculture patches and away from roads. Urban increased in areas of high urban density, close to roads and in areas with few forests and wetlands. We also found that agriculture gain is more likely for the areas closer to the agriculture and forest patches. Elevation strongly influenced urbanization and forest gain in MRW while it has no effect in SEWI.