Although traditional cellular automata (CA)-based models can effectively simulate urban land-use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell-based simulation strategies. This research proposes a new patch-based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a patterncalibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000-2006-2012.The results from this model were compared with two common models: cell-based CA and logistic-patch CA. The proposed model yields a degree of patch-level agreement that is 23.4 and 7.5% higher than those of these pre-existing models, respectively. This reveals that the patch-based CA model simulates actual development patterns much better than the two other models.

Mineral Potential Mapping (MPM) is usually regarded as a complex procedure. In modelling such a procedure, aspects such as the multiplicity of factors, fuzziness in the qualitative and quantitative data, and diverse experts' viewpoints need to be considered. To respond to these requirements, in this research, a methodology is proposed on the basis of the Fuzzy Inference System (FIS) and relations. The method is tested on the data of the Chah Firoozeh prospect, Kerman, Iran, to map the porphyry copper prospectivity. In the first stage, level-one factor maps are weighted using asymmetric fuzzy relation analysis and are combined using multilevel fuzzy comprehensive evaluation. The results are the level-two factor maps. In the next stage, these factor maps are integrated using a FIS to generate mineral potential maps. The final mineral potential map was evaluated using exploratory boreholes. The results indicate that the most prospective areas for porphyry copper mineralisation in Chah Firoozeh are located in the centre of the study area with a northsouth trend. A high compliance rate (83.33%) indicates the usefulness of combining FIS, as a knowledge-driven method, and asymmetric fuzzy relation analysis and multilevel fuzzy comprehensive evaluation, as a data-driven method, for generating porphyry copper mineral potential maps.

The megapolis areas are new types of urban settlements created in recent decades along with rapid urbanization. These areas constructed by clusters of small and large urban patches with various growth patterns. Spatial characteristics of urban patches are affected by some driving forces such as closeness to cities Central Business Center CBD and transportation network. The Cellular automata as a most common model for simulating urban growth, is unable in modeling spatial configuration of urban patches because of bottom up procedure and despite of high simulation power at cell level, CA has weaker performance in patch level. So in this study a method is presented for simulation of urban patches growth that is integrated with Logistic CA to modeling urban growth. In this method, on the one hand the growth potential map derived using logistic regression and on the other hand size and growth type of patch in each location is derived using integration of driving forces of growth patterns of urban patches. Finally according to proposed framework, a patch is constructed around selected cell and urban growth map will be prepared. The proposed model is implemented in the Tehran's megalopolis area in 1379-1385-1391-1397 periods. The overall accuracy and FOM of results is equal to 91/01 and 37/96, respectively that are better than logistic CA model. Also the results of validation of produces urban growth map by using spatial metrics reviled high precision of methodology in simulation of spatial configuration of urban pattern.

Interactions between cities play a significant role in the development of metropolitan regions. Although these interactions and their role in the urban growth modelling have already been investigated, there is still room for more studies. In this research, in addition to conventional urban growth factors, spatial interactions between the cities (SIBC) are incorporated into urban growth modelling. This causes directional trends in urban growth (DTUG). Therefore, first the DTUG of each city was measured using a developed indicator based on the history of urban growth that was extracted from satellite images and spatial statistics. The SIBC was then estimated by integrating the DTUG of the cities. Finally, the SIBC and other driving forces, including the physical suitability, accessibility and neighbourhood effects, were integrated using a cellular automata-based model. The accuracy of the model in the Tehran metropolitan region was increased by 6.44% after considering the SIBC. The analysis of the DTUG and SIBC in the Tehran metropolitan region during 1991–2000–2007–2014 revealed specific patterns as the spatial interactions intensified over time and usually peaked in the periphery of the central business districts and intense interactions existed between the metropolises and other major cities. These findings could help urban managers with strategic decision-making in the metropolitan regions and adjust the science and practice relation in this field.