Tremendous land use change has occurred in Lagos in recent times. Modelling urban systems now extends beyond the use of geographic information systems models. This research therefore presents a loose coupling of geographic information systems and artificial neural network for simulating land use change in Lagos. The experiment is based on three land use epochs of Lagos: 1963-1978, 1978-1984, and 1984-2000. Twelve salient land use explanatory variables (distance to water, distance to residential structures, distance to industrial and commercial centres, distance to major roads, distance to railway, distance to Lagos Island, distance to international airport, distance to international seaport, distance to University of Lagos, distance to Lagos State University, income potential, and population potential) are used for the simulation. Using the Kappa statistic, the result of the simulation in terms of the order of best-fit of the reference data is: 1978-1984, 1984-2000, and 1963-1978. An evaluation of the simulation using the receiver operating characteristics corroborates the Kappa estimates. A non black-box experiment using a one-neuron neural network to assess the performance of the spatial independent variables used for the simulation indicates that for all three epochs distance to residential structures has the highest impact in the simulation while population potential has the lowest impact.

Lagos has undergone an unprecedented urban expansion. Contemporary findings favour the integration of cellular automata and geographic information systems for modelling land use change. This research introduces the support vector machine based GIS cellular automata calibration for land use change prediction of Lagos. The support vector machine based cellular automata model is loosely coupled with the geographic information systems. Support vector machine parameters are optimised with the k-fold cross-validation technique, using the linear, polynomial, and RBF kernels functions. The land use change prediction is based on three land use epochs: 1963-1978, 1978-1984, and 1984-2000. The performance of the model was evaluated using the Kappa statistic and receiver operating characteristic. The order of performance of the three kernels is: RBF, polynomial, and linear. The results indicate substantial agreement between the actual and predicted maps. The urban forms in 2015 and 2030 are predicted based on the three land use epochs.