Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/10541
Title: Cellular automata for population growth prediction: Tripoli-Libya case
Authors: Zidan, Adel
Advisors: Abbod M
Keywords: MATLAB;Image processing;Fuzzy logic;Land use maps;Expand
Issue Date: 2015
Publisher: Brunel University London
Abstract: Due to obstruction in the national plan of urbanization in Tripoli (Libya) and population growth, serious problems have emerged in the form of random settlements, overcrowding and poor infrastructure. After more than two decades of inertia, the government has created a national plan in order to resolve the problems, hence it has enforced the demolition of some zones and modified other (irregularly built) ones, however the process is extremely costly. This research introduces a solution through cellular automata (CA) model to predict growth trends; size of residential, industrial and utilities areas; and to project future population. The model is implemented using digitized land use maps of Tripoli to indicate each areas as group of cells to predict their growth. The model incorporates two types of fuzzy rules bases, the first of which is based on the inputs population and area, and the second of which is based on the three inputs of population, area and density. The population prediction is performed using three scenarios, namely decreasing, fixed and increasing growth rates, such that all possibilities of growth are covered. In addition, the residential area prediction is performed based on two cases: normal density and low density. The former is introduced since new areas tend to have more open spaces and bigger houses. Furthermore, the model considers the growth of the industrial areas to be slower than that of residential areas. The model is developed and validated for the period of 1980 to 2010. The prediction is performed for thirty years from 2010 to 2040. In addition to the CA model, a regression model is developed and tested on the three growth scenarios for the same period (30 years). The prediction results are very close for 2040 in terms of population. The model incorporates the introduction of public services areas that are distributed equally on the growth areas, which occupy about 15-20% of the total area. This model can help the government to develop areas in a proper way and controls the expansion to have well layout and planned of the city, improving people's standard of living sustainably, while protecting the environment with better planning.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London
URI: http://bura.brunel.ac.uk/handle/2438/10541
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Theses

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