Self-modifying CA model using dual ensemble Kalman filter for simulating urban land-use changes

Abstract

There are many different methods to calibrate cellular automata (CA) models for better simulation results of urban land-use changes. However, few studies have been reported on combination of parameter update and error control using local data in CA calibration procedures. This paper presents a self-modifying CA model (SM-CA) that uses the dual ensemble Kalman filter (dual EnKF), which enables the CA model to simultaneously update model parameters and simulation results by merging observation data (local data). We applied the proposed model to simulate urban land-use changes in a 13-year period (1993–2005) in Dongguan City, a rapidly urbanizing region in south China. Simulation results indicate that this model yields better simulation results than the conventional logistic-regression CA and decision-tree CA models. For example, the validation is carried out using cross-tabulation matrix. The simulation results of SM-CA have allocation disagreement of 10.18%, 19.64%, and 30.03% in 1997, 2001, and 2005, respectively, which are 2.12%, 2.47%, and 6% lower than conventional logistic-regression CA models.

Keywords:

• cellular automata
• self-modifying
• data assimilation
• state and parameter estimation
• ensemble Kalman filter
• LUCC

Additional information

Funding

This work was supported by National Basic Research Program of China (973 Program) [grant number 2011CB707103], National Natural Science Foundation of China [grant numbers 41371376 and 41301408] and Guangdong Natural Science Foundation [grant number S2013040016071].