摘要：Greenery is one of the important ingredients for urban planning with a sustainable environment. Increasing parks and open spaces (POS) to offer a greater diversity of green spaces have substantial impact on its environment in many mega cities around the world. However, any place cannot be a potential site for POS due to multi-objective modeling nature of POS planning. In this paper, an intelligent multi-objective continuous optimization model is thus developed for locating POS with particular emphasis on greeneries that will potentially benefit and facilitate the planning of a sustainable city. Three environmentally incommensurable factors analyzed with the help of geographic information systems (GIS) namely air-quality, noise-level, and population-distribution have been considered in the model and a genetic algorithm (GA) is used to solve the continuous optimization problem heuristically. The model has been applied to Dhaka city as a case study to find the optimal locations of additional POS to make it a sustainable city by ameliorating its degraded environment. The multiple objectives are combined into a single one by employing a dynamic weighting scheme and a set of non-dominated Pareto optimal solutions is derived. The obtained alternative non-dominated solutions from the robust modelling approach can serve as a candidate pool for the city planners in decision making for POS planning by selecting an alternative solution which is best suited for the prevailing land-use pattern in a city. The model has successfully demonstrated to provide optimal locations of new POS. In addition, we found that locations of POS can be optimized even by integrating it with land cover and uses like lakes, streams, trails (for simplicity which were considered as a barrier constraint in the model) to rejuvenate added beauties in a city. The obtained results thus indicate that the developed multi-objective POS location model can serve as an effective tool for urban POS planning maintaining sustainable environment.
关键词：Urban Greening; Parks and Open Space; Sustainable Environment; GA and GIS; Intelligent Location Modelling
