Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. (July 2017) - Shafizadeh-Moghadam, Hossein; Asghari, Ali; Tayyebi, Amin; Taleai, Mohammad
Abstract: This paper compares six land use change (LUC) models, including artificial neural networks (ANNs), support vector regression (SVR), random forest (RF), classification and regression trees (CART), logistic regression (LR), and multivariate adaptive regression splines (MARS). These models were used to simulate urban growth in the megacity of Tehran Metropolitan Area (TMA). These LUC models were integrated with cellular automata (CA) and validated using a variety of goodness-of-fit metrics. The results showed that the percent correct metrics (PCMs) varied between 54.6% for LR and 59.6% for MARS, while the area under curve (AUC) ranged from 67.6% for LR to 74.7% for ANNs. The results also showed a considerable difference between the spatial patterns within the error maps. The results of this comparative study will enable decision makers and scholars to better understand the performance of the models when reducing the number of misses and false alarms is a priority. Highlights: Six land use change models were coupled with CA to simulate urban growth. The integrated models were evaluated using a series of goodness-of-fit metrics. A comparison of the error maps revealed how the model mechanisms differ. MARS-CA and ANN-CA performed better than the other models. Such comparison enables decision-makers to wisely select land use change models for environmental applications.