Agent-Based Model Validation Using Bayesian Networks and Vector Spatial Data

Validation of agent-based models (ABMs) of land-use change is a significant challenge in current spatial-modelling research and application. During the validation process, model performance and accuracy assessment depend mostly on pixel-by-pixel comparisons. However, in urban land-use planning problems the use of vector spatial data to develop ABMs is becoming more necessary. Hence, improved and robust validation approaches are required for vector-based ABMs. This study presents a novel validation approach for an ABM using vector-based geographic information system and Bayesian networks. The approach creates a unique-polygons map and an object-oriented database. Three indicator variables are calculated to assess the probability of agreement. The indicator variables are nodes in a Bayesian network that is used to evaluate the final agreement of each unique polygon. Further, an index of overall agreement is calculated. The approach was applied to a simulation outcome map generated by an existing Bayesian network-based agent-system (BNAS) model. The BNAS model simulation of land-use change for the year 2001 was compared with the actual land-use change for the same year using the proposed validation approach. The results obtained indicate that significant agreement between the maps was achieved. The approach is well suited for validating vector-based ABMs and can be used as an aid in model designs for improved model performance.