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|Title:||URBAN LAND GROWTH ANALYSIS AND MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORKS INTEGRATED WITH GIS|
|Authors:||Demissie, Mulugeta Feyissa|
|Keywords:||CIVIL ENGINEERING;URBAN LAND GROWTH ANALYSIS AND MODELING;CELLULAR AUTOMATA;ARTIFICIAL NEURAL NETWORK|
|Abstract:||The goal of this study is to simulate and predict urban dynamics changes and quantify their impact on different land uses. For this purpose, a multi-spatiotemporal urban growth model is , designed based on artificial intelligence techniques including, cellular automata, fuzzy logic and neural networks. Cellular automata define a set of transition rules as a function of spatial neighborhood structure and input data. It serves as a modeling engine to simulate changes in spatiotemporal urban dynamics. Calibration of such rules is performed spatially and temporally as a function of time. The use of fuzzy logic provides good initials for the cellular automata model and allows for including semantic knowledge into urban growth modeling. Fuzzy logic preserves the continuity of urban dynamics spatially by choosing fuzzy membership functions, fuzzy rules, and the fuzzification-defuzzification process. Neural Networks are used to simplify model structure and facilitate the determination of parameter values. Unlike traditional CA models, the_proposed_model does not require users- to provide transition rules which. may vary from different applications. An attempt is made, to develop a neuro-fuzzy model, to be integrated into cellular automata model for predicting the effects of urban growth as a function of road distance, population density, slope, and distance from the center of. the city. The developed model and approach are tested for the historical development of Addis Ababa, Ethiopia for a period of 30 years. Land cover maps derived from satellite imagery, road networks, population data, digital elevation model and the city center are used as input data for the study. The integrated model results show satisfactory fitness with close urban match patterns between the real and simulated data. It also shows the capability of predicting the grey level of development of the urban area during any year's, growth in the simulation. The developed methodology can be used to support urbanization related studies, such as regional planning, sustainable development, environmental monitoring, ecosystem protection, and global warming.|
|Research Supervisor/ Guide:||Jain, K.|
|Appears in Collections:||MASTERS' THESES (Civil Engg)|
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