Cellular automata for population growth prediction: Tripoli-Libya case

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDue 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

Cellular automata simulations of field scale flaming and smouldering wildfires in peatlands

In peatland wildfires, flaming vegetation can initiate a smouldering fire by igniting the peat underneath, thus, creating a positive feedback to climate change by releasing the carbon that cannot be reabsorbed by the ecosystem. Currently, there are very few models of peatland wildfires at the field-scale, hindering the development of effective mitigation strategies. This lack of models is mainly caused by the complexity of the phenomena, which involves 3-D spread and km-scale domains, and the very large computational resources required. This thesis aims to understand field-scale peatland wildfires, considering flaming and smouldering, via cellular automata, discrete models that use simple rules. Five multidimensional models were developed: two laboratory-scale models for smouldering, BARA and BARAPPY, and three field-scale models for flaming and smouldering, KAPAS, KAPAS II, and SUBALI. The models were validated against laboratory experiments and field data. BARA accurately simulates smouldering of peat with realistic moisture distributions and predicts the formation of unburned patches. BARAPPY brings physics into BARA and predicts the depth of burn profile, but needs 240 times more computational resources. KAPAS showed that the smouldering burnt area decreases exponentially with higher peat moisture content. KAPAS II integrates daily temporal variation of moisture content, and revealed that the omission of this temporal variation significantly underestimates the smouldering burnt area in the long term. SUBALI, the ultimate model of the thesis, integrates KAPAS II with BARA and considers the ground water table to predict the carbon emission of peatland wildfires. Applying SUBALI to Indonesia, it predicts that in El Niño years, 0.40 Gt-C in 2015 (literature said 0.23 to 0.51 Gt-C) and 0.16 Gt-C in 2019 were released, and 75% of the emission is from smouldering. This thesis provides knowledge and models to understand the spread of flaming and smouldering wildfires in peatlands, which can contribute to efforts to minimise the negative impacts of peatland wildfires on people and the environment, through faster-than-real-time simulations, to find the optimum firefighting strategy and to assess the vulnerability of peatland in the event of wildfires.Open Acces

Should big cities grow? Scenario-based cellular automata urban growth modeling and policy applications

The formation of ‘Urban Networks’ has become a wide-spread phenomenon around the world. In the study of metropolitan regions, there are competing or diverging views about management and control of environmental and land-use factors as well as about scales and arrangements of settlements. Especially in China, these matters alongside of regulatory aspects, infrastructure applications, and resource allocations, are important because of population concentrations and the overlapping of urban areas with other land resources. On the other hand, the increasing sophistication of models operating on iterative computational power and widely-available spatial information and analytical techniques make it possible to simulate and investigate the spatial distribution of urban territories at a regional scale. This research applies a scenario-based Cellular Automata model to a case study of the Changjiang Delta Region, which produces useful and predictive scenario-based projections within the region, using quantitative methods and baseline conditions that address issues of regional urban development. The contribution of the research includes the improvement of computer simulation of urban growth, the application of urban form and other indices to evaluate complex urban conditions, and a heightened understanding of the performance of an urban network in the Changjiang Delta Region composed of big, medium, and small-sized cities and towns

The use of GIS for the development of a fully embedded predictive fire model

Fire is very important for maintaining balance in the ecosystems and is used by fire management across the world to regulate growth of vegetation in natural conservation areas. However, improper management of fire may lead to hazardous behaviour. Fire modelling tools are implemented to provide fire managers with a platform to test and plan fire management activities. Fire modelling occurs in two parts: fire behaviour models and fire spread models, where fire behaviour models account for the behaviour of fires that is used in fire spread models to model the propagation of a fire event. Since fire is a worldwide phenomenon a number of fire modelling approaches have been developed across the world. Most existing fire models only model either fire behaviour or fire spread, but not both, hence full integration of fire models into GIS is not completely implemented. Full integration of environmental modelling in GIS refers to the case where an environmental model such as a fire model is implemented within a GIS environment, without requiring any transfer of data from other external environments. Most existing GIS based fire spread models account for fire propagation in the direction of prevailing winds (or defined fire channels) as opposed to full fire spread in all directions. The purpose of this study is to illustrate the role of GIS in fire management through the development of a fully integrated, predictive, wind driven, surface fire model. The fire model developed in this study models both the risk of fire occurring (fire behaviour model), and the propagation of a fire in case of an ignition incident (fire spread model), hence full integration of fire modelling in a GIS environment. The fire behaviour model is based on prevailing meteorological conditions, the type of vegetation in an area, and the topography. The spread of a fire in this model is determined by the transfer of heat energy and rate of spread of fire, and is developed based on the Cellular Automata (CA) modelling approach. This model considers the spread of fire in all directions instead of the forward wind direction only as is the case in most fire spread models. The fire behaviour model calculates fire intensity and rate of spread which are used in the fire spread model, hence demonstrating the full integration of fire modelling in GIS. No external data exchange with the model occurs except for acquisition of input data such as measured values of environmental conditions. v This cellular automata based fire spread model is developed in the ArcGIS ModelBuilder geoprocessing environment, and requires the development of a custom geoprocessing function tool to facilitate the fast and effective performance of the model. The test study area used in this research is the Kruger National Park because of frequent fire activity that occurs in the park, as a result of management activities and accidental fires, and also because these fires are recorded by park fire ecologists. Validation of the model is achieved by comparison of simulated fire areas after a certain period of time with known location of the fire at that particular time. This is achieved by the mapping of fire scars and active fire areas acquired from MODIS Terra and Aqua images, fire scars are also acquired from the Kruger National Park Scientific Services. Upon evaluation, the results of the fire model show successful simulation of fire area with respect to time. The implementation of the model within the ArcGIS environment is also performed successfully. The study thus concludes that GIS can be successfully used for the development of a fully integrated (embedded) fire model

Spatial Distribution of Urban Territories at a Regional Scale: Modeling the Changjiang Delta’s Urban Network

The formation of ‘Urban Networks’ has become a wide-spread phenomenon around the world. In the study of metropolitan regions, there are competing or diverging views about management and control of environmental and land-use factors. Especially in China, these matters, regulatory aspects, infrastructure applications, and resource allocations, are important due to population concentrations and the overlapping of urban areas with other land resources. On the other hand, the increasing sophistication of models operating on iterative computational power and widely-available spatial information and techniques make it possible to investigate the spatial distribution of urban territories at a regional scale. This thesis applies a Scenario Cellular Automata (SCA) model to the case study of the Changjiang Delta Region, which produces useful and predictive scenario-based projections within the region, using quantitative methods and baseline conditions that address issues of regional urban development. The contribution of the research includes the improvement of computer simulation of urban growth, the application of urban form and other indices to evaluate complex urban conditions, and a heightened understanding of the performance of an urban network in the Changjiang Delta Region composed of big, medium, and small-sized cities and towns

Evacuation management system for major disasters

Predicting and understanding mass evacuations are important factors in disaster management and response. Current modelling approaches are useful for planning but lack of real-time capabilities to help informed decisions as the disaster event evolves. To address this challenge, a real-time Evacuation Management System (EMS) is proposed here, following a stochastic approach and combining classical models of low complexity but high reliability. The EMS computes optimal assembly points and shelters and the related network of evacuation routes using GIS-based traffic, pedestrian and routing models including damaged assets or impassable areas. To test the proper operation performances of the EMS, we conducted a case study for the Gran Canaria wildfireThis research and APC was funded by the European Union’s H2020 research and innovation programme under grant agreement No. 832576 (ASSISTANCE project)

An Analysis of Urban Land use land cover (LULC) Changes in Lilongwe City, Central Malawi (2002–2022)

Lilongwe, Malawi’s capital city, has grown nearly tenfold in the last 40 years with a 4-5% annual population growth rate, and the city’s population is projected to double over the next decade. Rural to urban migration and natural increase are the driving factors of the city’s urban expansion. Characterised by the urbanisation of poverty, Lilongwe is experiencing uncontrolled and unplanned urban expansion that has led to the growth of informal settlements. Urbanisation leads to land use land cover (LULC) changes that negatively impact the quality of life and the environment. Lilongwe faces many challenges, including high levels of poverty, inequality, poorly built infrastructure, lack of access to safe sanitation and clean water, urban flooding, and poor waste disposal. Effective land use planning is important in mitigating future urbanisation\u27s adverse effects. To prepare and plan for the inevitable future urban growth of the city, studies of historical land use land cover changes are essential in understanding the urbanisation trajectory of the city. This study used post classification change detection and the SLEUTH urban growth model to analyse land use land cover changes in Lilongwe from 2002 to 2022. Results revealed that Lilongwe’s urban growth is characterised by the expansion of built area coverage within and on the edges of already existing urban clusters. While urban growth is apparent in all parts of the city, it is concentrated in the northwest, southwest, and southeast