Predicting Future Land Use Change Using Support Vector Machine Based GIS Cellular Automata: A Case of Lagos, Nigeria

Onuwa Okwuashi, Jack McConchie, Peter Nwilo, Mfon Isong, Aniekan Eyoh, Okey Nwanekezie, Etim Eyo, Aniekan Danny Ekpo

Abstract


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; which shows the order of performance of the three kernels: 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.


Full Text: PDF DOI: 10.5539/jsd.v5n5p132

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This work is licensed under a Creative Commons Attribution 3.0 License.

Journal of Sustainable Development   ISSN 1913-9063 (Print)   ISSN 1913-9071 (Online)

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