CHAPTER 4. MODELLING URBAN GROWTH USING CELLULAR AUTOMATA
4.4. Urban modelling software
4.4.2. Stella
The field of system dynamics originated in the end of 1950 with the work of Jay W.
Forrester and his colleagues at the Massachusetts Institute of Technology. Forrester expanded the ideas by applying conceptions from feedback control theory of the industrial system. One of the best known appliance of Forrester is Urban Dynamics.
Afterwards Urban Dynamics is expanded and became approach which known as system dynamics. System dynamics is a methodology for researching and managing complexity of the systems which change over time. This method uses computer modelling to focus our attention on information feedback loops that give rise to the dynamic behavior.
The theoretical and practical evolution of system dynamics in water research areas over the 50 years. Problems in regional planning and river basin management, urban water management, flooding and irrigation exhibit important short-term and long-term effects. System dynamic application in water research management have branched off in many directions. For example, regional analysis and river basin planning, urban water, flooding and irrigation. In the early 1970s system approach is used in water resource management and 1971- Anderson, Biswas, Grigg, Helmeg, Camara. Participatory modelling-1988- Vennix, Winch, Andersen and Richardson, Van den Belt.
Practical developments of system dynamics transmitted into regional analysis and river basin planning by the leading researchers Hamilton, Camara, Ford, Costanza and Ruth Simonovic , Xu Carthwright and Connor; Den Exter and Specht et al. in 1968.
Urban water resource management study using system approach is started since 1974 pioneering researchers were Wallace, Palmer, Grigg, Cloud, Stave Bagheri and Hjorth.
The flooding and irrigation related studies which were used system dynamics are intensively increased since 2000. Ahmad and Simonovic, Li and Simonovic, Saysel Diaz-Ibarra, Fernandez and Selma, Elmahdi were made a several research work in that field.
As a scales of research area in system dynamics can be divided three levels. First is regional level study is focused on river basin modelling, integrating complex hydrologic data with other information (e.g., policy, regulatory, and management criteria), analyse a factors and constraints in the basin area and which enables them to
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better understand the system via the simulation of multiple scenarios. (Sehlke G1, Jacobson J. 2005).
Second level is national one of well-known study is made by Simonovic and Rajasekaram in 2004, integrated water resources management model for Canada, CanadaWater, has been developed using the system dynamics simulation approach.
In the global level, study using system dynamics is include number of socio-economic factors, and emphasize improved computer capabilities as well as changing problem such as global water crisis and social impacts. Simonovic and Rajasekaram (2004) note a recent trend in the reduction of spatial scales to basin and watersheds with the aim of identifying regional and local solutions.
Application in regional analysis have often had a strong economic focus examining feedback relationships between industry and available water resources.
River basin and watershed management application is focus more narrowly on water resources and their interaction with population growth, as with regional analysis tools, temporal scales of these models are typically long-term (50-100 years).
Urban water resource management may be seen as a special case of watershed management where concerns are more immediate and more contentious.
One of broadly used software systematic research is Stella. Using Stella software and simple conceptual diagram population growth is calculated in case birth rate is 23.6, 21.1 and 18.9 (Figure 4.17). Also number of population is showed in Table 4.2.
Furthermore, urban growth is calculated in the software using death rate (Figure 4.18).
Table 4.3 shows that highest number of population in case which death rate 4.13 in the lowest number. Next step is sensitive analysis (Figure 4.19) which selecting the highest death rate and lowest growth rate. It is one of the attempt to predict the lowest number (Table 4.4 ) of population of Darkhan city.
Water usage is calculated based on the lowest number of population (Figure 4.20).
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䢳䢸䢼䢳䢹䢢䢢䢢䢢䢴䢲䢳䢶ᖺ䢹᭶䢴᪥
䢳䢱䢢䢴䢵䢰䢸䢢䢴䢱䢴䢳䢰䢳䢢䢵䢱䢳䢺䢰䢻 䣒䣣䣩䣧䢢䢳
䢲䢰䢲䢲 䢳䢲䢰䢲䢲 䢴䢲䢰䢲䢲 䢵䢲䢰䢲䢲 䢶䢲䢰䢲䢲
䣛䣧䣣䣴䣵 䢳䢼
䢳䢼 䢳䢼
䢹䢷䢲䢲䢲 䢳䢳䢷䢲䢲䢲 䢳䢷䢷䢲䢲䢲 䣒䣱䣲䣷䣮䣣䣶䣫䣱䣰䢼䢢䢳䢢䢯䢢䢴䢢䢯䢢䢵䢢䢯䢢
䢳
䢳
䢳
䢳
䢴
䢴
䢴
䢴
䢵
䢵
䢵
䢵
Figure 4. 17 Exponential growth of population, birth rate is (23.6-18.9) Table 4. 1 Population growth (exponential)
Year Number of population
2013 75644
2020 82251
2025 89436
2030 97248
2035 105743
2040 114979
2045 125023
2050 135943
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Table 4. 2 Growth population (death rate)
Year Number of population
2013 75644
2020 83007
2025 91088
2030 99956
2035 109687
2040 120365
2045 132082
2050 144940
䢳䢸䢼䢴䢶䢢䢢䢢䢢䢴䢲䢳䢶ᖺ䢹᭶䢴᪥
䢳䢱䢸䢰䢳䢵䢮䢢䢴䢱䢷䢰䢳䢵䢮䢢䢵䢱䢶䢰䢳䢵 䣒䣣䣩䣧䢢䢳
䢲䢰䢲䢲 䢳䢲䢰䢲䢲 䢴䢲䢰䢲䢲 䢵䢲䢰䢲䢲 䢶䢲䢰䢲䢲
䣛䣧䣣䣴䣵 䢳䢼
䢳䢼 䢳䢼
䢹䢷䢲䢲䢲 䢳䢴䢲䢲䢲䢲 䢳䢸䢷䢲䢲䢲 䣒䣱䣲䣷䣮䣣䣶䣫䣱䣰䢼䢢䢳䢢䢯䢢䢴䢢䢯䢢䢵䢢䢯䢢
䢳
䢳
䢳
䢳
䢴
䢴
䢴
䢴
䢵
䢵
䢵
䢵
Figure 4. 18 Exponential growth of population, death rate is (6.13-4.13)
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Table 4. 3 Growth population (Sensitive analysis)
Year Number of population
2013 75644
2020 81116
2025 86985
2030 93279
2035 100027
2040 107264
2045 115025
2050 123347
䢳䢸䢼䢵䢲䢢䢢䢢䢢䢴䢲䢳䢶ᖺ䢹᭶䢴᪥
䣦䣧䣣䣶䣪䢢䣴䣣䣶䣧䢢䢸䢰䢳䢵䢯䢶䢰䢳䢵䢢䢱䢢䢢䣤䣫䣴䣶䣪䢢䣴䣣䣶䣧䢢䢴䢵䢰䢸䢯䢳䢺䢰䢸 䣒䣣䣩䣧䢢䢳
䢲䢰䢲䢲 䢳䢲䢰䢲䢲 䢴䢲䢰䢲䢲 䢵䢲䢰䢲䢲 䢶䢲䢰䢲䢲
䣛䣧䣣䣴䣵 䢳䢼
䢳䢼 䢳䢼
䢹䢷䢲䢲䢲 䢳䢳䢷䢲䢲䢲 䢳䢷䢷䢲䢲䢲 䣒䣱䣲䣷䣮䣣䣶䣫䣱䣰䢼䢢䢳䢢䢯䢢䢴䢢䢯䢢䢵䢢䢯䢢
䢳
䢳
䢳
䢳
䢴
䢴
䢴
䢴
䢵
䢵
䢵
䢵
Figure 4.19 Sensitive analyze Birth rate 23.6-18.6
Death rate 6.13-4.13
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䢳䢷䢼䢲䢲䢢䢢䢢䢢䢴䢲䢳䢶ᖺ䢹᭶䢵᪥
䣗䣰䣶䣫䣶䣮䣧䣦 䣒䣣䣩䣧䢢䢳
䢲䢰䢲䢲 䢳䢲䢰䢲䢲 䢴䢲䢰䢲䢲 䢵䢲䢰䢲䢲 䢶䢲䢰䢲䢲
䣛䣧䣣䣴䣵 䢳䢼
䢳䢼 䢳䢼
䢳䢲䢲䢲䢲䢲䢲 䢴䢷䢲䢲䢲䢲䢲 䢶䢲䢲䢲䢲䢲䢲
䣶䣱䣶䣣䣮䢢䣹䣣䣶䣧䣴䢢䣷䣵䣣䣩䣧䢼䢢䢳䢢䢯䢢䢴䢢䢯䢢䢵䢢䢯䢢䢶䢢䢯䢢䢷䢢䢯䢢
䢳
䢳
䢳
䢳
䢴
䢴
䢴
䢴
䢵
䢵
䢵
䢵
䢶
䢶
䢶
䢶
䢷
䢷
䢷
䢷
Figure 4. 20 Comparative analyze of domestic water usage
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