Comparing three transition potential models: A case study of built-up transitions in North-East India

Abstract Change allocation is an important step in the Land Use Land Cover (LULC) change modelling. Many established LULC models use transition potential maps for the allocation of the estimated land demand. This study compares three commonly used techniques for transition potential modelling: (1) Multi-Layer Perceptron Neural Network (MLP), (2) Logistic Regression (LogReg), and (3) Similarity Weighted Instance-based Learning (SimWeight); and evaluates their applicability for built-up transitions. A case study has been taken from Guwahati city, in North-East India which experiences heterogeneous built-up growth in a limited area within the large topographic variations. With the same set of input and tested driving factors, all three models were simulated for the period 1989–2001 to produce the transition potential maps for 2011 and same amount of land demands, as in 2011 were allocated on the potential maps. The validation was done by (1) a multi-resolution validation method and (2) a region based method using the wards of the city. For this particular study, with the specific landscape environment and scale, MLP produced the most accurate change and predicted areas. The LogReg simulated the no change areas the most accurately, while the SimWeight could generate the edge extensions satisfactorily. We presented a detailed comparison of the change potentials and simulated maps and discuss the importance of evaluating the ability of the transition potential model used for LULC model. The results from this study can assist the LULC modelers to validate their transition potential models for generating accurate prediction maps. It can be also useful for planners and decision makers of Guwahati city and similar landscape, environment, scale in producing accurate transition potential zones for precise built-up growth modelling.