The purpose of this research was to assess the condition of open spaces, community perceptions, benefits, threats and challenges faced by open spaces within KwaDukuza. The research is regarded as important within the context of threats posed by uncontrolled development to urban biodiversity. Primary and secondary documentary sources on open spaces in the study area were consulted. Data was gathered through the use of a questionnaire, with a sample of 100 respondents; observation; structured interviews with key respondents and discussions with focus groups. The research revealed a consistent pattern of threatened urban biodiversity when compared with other studies. The main finding was that the open spaces were under severe strain and threat in the area of KwaDukuza due to development. There was a noted lack of knowledge about key tools meant to safeguard the environment. The study concluded by recommending community participation, education and an open space policy framework for KwaDukuza. / Geography / M.A. (Geography)

Indiana University-Purdue University Indianapolis (IUPUI) / Environmental planning is very cumbersome work for environmentalists, government agencies like USDA and NRCS, and farmers. There are a number of conflicts and issues involved in such a decision making process. This research is based on the work to provide a common platform for environmental planning called WRESTORE (Watershed Restoration using Spatio-Temporal Optimization of Resources). We have designed a system that can be used to provide the best management practices for environmental planning. A distributed system was designed to combine high performance computing power of clusters/supercomputers in running various environmental model simulations. The system is designed to be a multi-user system just like a multi-user operating system. A number of stakeholders can log-on and run environmental model simulations simultaneously, seamlessly collaborate, and make collective judgments by visualizing their landscapes. In the research, we identified challenges in running such a system and proposed various solutions. One challenge was the lack of fast optimization algorithm. In our research, several algorithms are utilized such as Genetic Algorithm (GA) and Learning Automaton (LA). However, the criticism is that LA has a slow rate of convergence and that both LA and GA have the problem of getting stuck in local optima. We tried to solve the multi-objective problems using LA in batch mode to make the learning faster and accurate. The problems where the evaluation of the fitness functions for optimization is a bottleneck, like running environmental model simulation, evaluation of a number of such models in parallel can give considerable speed-up. In the multi-objective LA, different weight pair solutions were evaluated independently. We created their parallel versions to make them practically faster in computation. Additionally, we extended the parallelism concept with the batch mode learning. Another challenge we faced was in User Modeling. There are a number of User Modeling techniques available. Selection of the best user modeling technique is a hard problem. In this research, we modeled user's preferences and search criteria using an ANN (Artificial Neural Network). Training an ANN with limited data is not always feasible. There are many situations where a simple modeling technique works better if the learning data set is small. We formulated ways to fine tune the ANN in case of limited data and also introduced the concept of Deep Learning in User Modeling for environmental planning system.