Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model

Abstract

Sustainable urban planning and management require reliable land change models, which can be used to improve decision making. The objective of this study was to test a random forest-cellular automata (RF-CA) model, which combines random forest (RF) and cellular automata (CA) models. The Kappa simulation (KSimulation), figure of merit, and components of agreement and disagreement statistics were used to validate the RF-CA model. Furthermore, the RF-CA model was compared with support vector machine cellular automata (SVM-CA) and logistic regression cellular automata (LR-CA) models. Results show that the RF-CA model outperformed the SVM-CA and LR-CA models. The RF-CA model had a Kappa simulation (KSimulation) accuracy of 0.51 (with a figure of merit statistic of 47%), while SVM-CA and LR-CA models had a KSimulation accuracy of 0.39 and −0.22 (with figure of merit statistics of 39% and 6%), respectively. Generally, the RF-CA model was relatively accurate at allocating "non-built-up to built-up" changes as reflected by the correct "non-built-up to built-up" components of agreement of 15%. The performance of the RF-CA model was attributed to the relatively accurate RF transition potential maps. Therefore, this study highlights the potential of the RF-CA model for simulating urban growth.

Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

“…This approach demonstrated an improvement when compared to the logistic-CA model in terms of urban simulation accuracy. A random forest based CA model was used to simulate urban growth in Harare Metropolitan Province, Zimbabwe from 1984 to 2013 [24]. This model outperformed CA models based on support vector machine (SVM) and logistic regression in the study area.…”

“…One important issue in CA modeling is the quantification of the impacts of the factors that drive urban growth and land use change at both global and local scales. Many approaches have been developed to define CA transition rules and each is aimed at improving the overall accuracy and reducing errors of simulation [21][22][23][24]. These approaches vary widely in theoretical assumptions, underlying methodologies, and spatio-temporal resolutions and extents [25].…”

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

“…Historically, the most common method to assess simulation results was visual inspection by experts [18,24,25], however this is highly subjective and irreproducible. Several methods have been proposed over the last decade, all of which compare simulated maps with maps of assumed truth [12][13][14][15][16][17]26]. There is continued debate over accuracy assessment methodologies among land change and remote sensing research foci, with the sole emphasis on quantity and allocation disagreement [13,14].…”

Modeling Urban Dynamics Using Random Forest: Implementing Roc and Toc for Model Evaluation

“…Relaxation of normal distribution assumption, robustness to over-fitting, less required training time and providing information regarding variable importance are the main characteristics of this method. Kamusoko and Gamba (2015) compared the random forest-cellular automata (RF-CA) with support vector machine cellular automata (SVM-CA) and logistic regression cellular automata (LR-CA) models for modelling urban change. They found that RF-CA model outperformed SVM-CA and LR-CA models.…”