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Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana'a metropolitan city, Yemen.ABSTRACT An effective and efficient planning of an urban growth and land use changes and its impact on the environment requires information about growth trends and patterns amongst other important information. Over the years, many urban growth models have been developed and used in the developed countries for forecasting growth patterns. In the developing countries however, there exist a very few studies showing the application of these models and their performances. In this study two models such as cellular automata (CA) and the SLEUTH models are applied in a geographical information system (GIS) to simulate and predict the urban growth and land use change for the City of Sana'a (Yemen) for the period 2004-2020. GIS based maps were generated for the urban growth pattern of the city which was further analyzed using geo-statistical techniques. During the models calibration process, a total of 35 years of time series dataset such as historical topographical maps, aerial photographs and satellite imageries was used to identify the parameters that influenced the urban growth. The validation result showed an overall accuracy of 99.6 %; with the producer's accuracy of 83.3 % and the user's accuracy 83.6 %. The SLEUTH model used the best fit growth rule parameters during the calibration to forecasting future urban growth pattern and generated various probability maps in which the individual grid cells are urbanized assuming unique "urban growth signatures". The models generated future urban growth pattern and land use changes from the period 2004-2020. Both models proved effective in forecasting growth pattern that will be useful in planning and decision making. In comparison, the CA model growth pattern showed high density development, in which growth edges were filled and clusters were merged together to form a compact built-up area wherein less agricultural lands were included. On the contrary, the SLEUTH model growth pattern showed more urban sprawl and low-density development that included substantial areas of agricultural lands.

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Urban development is a continuous and dynamic spatio-temporal phenomenon associated with economic developments and growing populations. To understand urban expansion, it is important to establish models that can simulate urbanization process and its deriving factors behaviours, monitor deriving forces interactions and predict spatio-temporally probable future urban growth patterns explicitly. In this research, therefore, we presented a hybrid model that integrates the chi-squared automatic integration detection decision tree (CHAID-DT), Markov chain (MC) and cellular automata (CA) models to analyse, simulate and predict future urban expansions in Tripoli, Libya in 2020 and 2025. First, CHAID-DT model was applied to investigate the contributions of urban factors to the expansion process, to explore their interactions and to provide future urban probability map; second, MC model was employed to estimate the future demand of urban land; third, CA model was used to allocate estimated urban land quantity on the probability map to present future projected land use map. Three satellite images of the study area were obtained from the periods of 1984, 2002 and 2010 to extract land use maps and urban expansion data. We validated the model with two methods, namely, receiver operating characteristic and the kappa statistic index of agreement. Results confirmed that the proposed hybrid model could be employed in urban expansion modelling. The applied hybrid model overcame the individual shortcomings of each model and explicitly described urban expansion dynamics, as well as the spatiotemporal patterns involved.

The urban development process is a continuous and dynamic spatio-temporal phenomenon associated with economic developments and growing populations. Understanding urban expansion processes require models capable of simulating, monitoring, and predicting both urban growth and urban sprawl. In this research, probability-based Evidential Belief Functions (EBF) and Frequency Ratio (FR) models were employed to simulate and to predict the urban expansion probability map of the metropolitan area in Tripoli, Libya. These methods have not been used before in the urban development simulations of cities. By using the geographic information system (GIS), three satellite imageries obtained from 1996, 2002, and 2010 were employed to extract seven urban-deriving factors for the study area. The urban factors are slope, distance to active economic center, distance to central business district (CBD), distance to roads, distance to built-up areas, distance to educational area, and distance to coastal areas. For model calibration, both the EBF and FR models were applied to simulate urban expansion from 1996 to 2002. Data from 2002 to 2010 were used for models validation. Consequently, future suitability maps of urban growth were produced. The validation results indicated 83 % prediction accuracy for the EBF model and 84 % for the FR model. The outcomes established that the models could be employed in the urban expansion modeling of metropolises. The applied models, however, have dynamic and temporal limitations that should be considered in urban growth analysis.

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