Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth

There are two main issues of concern for land change scientists to consider. First, selecting appropriate and independent land cover change (LCC) drivers is a substantial challenge because these drivers usually correlate with each other. For this reason, we used a well-known machine learning tool called genetic algorithm (GA) to select the optimum LCC drivers. In addition, using the best or most appropriate LCC model is critical since some of them are limited to a specific function, to discover non-linear patterns within land use data. In this study, a support vector regression (SVR) was implemented to model LCC as SVRs use various linear and non-linear kernels to better identify non-linear patterns within land use data. With such an approach, choosing the appropriate kernels to model LCC is critical because SVR kernels have a direct impact on the accuracy of the model. Therefore, various linear and non-linear kernels, including radial basis function (RBF), sigmoid (SIG), polynomial (PL) and linear (LN) kernels, were used across two phases: 1) in combination with GA, and 2) without GA present. The simulated maps resulting from each combination were evaluated using a recently modified version of the receiver operating characteristics (ROC) tool called the total operating characteristic (TOC) tool. The proposed approach was applied to simulate urban growth in Rasht County, which is located in the north of Iran. As a result, an SVR-GA-RBF model achieved the highest area under curve (AUC) value at 94% while the lowest AUC was achieved when using the SVR-LN model at 71%. The results show that the synergy between GA and SVR can effectively optimize the variables selection process used when developing an LCC model, and can enhance the predictive accuracy of SVR.