An Intelligent Decision-making Method For Landscape Plant Selection Based On Their Habits Of Sunshine Requirements

Landscape plant is a green, safe, and effective way by playing ecological functions for the alleviating urban problems due to the urbanization. However, the survival and healthy growth of plants is a prerequisite for its ecological functions. Our investigation and analysis shows that the sunshine is a key factor, which plays a very important role in the growth and development of landscape plant, especially in the high-density and built-up areas. In the same geographical area, building height, building layout and terrain diversity which led to the diversity of solar radiation. Due to the limitations of human perception, it has been difficult to achieve an exact match between the solar radiation provided by the environment and the sunshine requirements of landscape plants, according to the designer's traditional method with experience-oriented for plant selection and the construction of plant communities, and cannot ensure the sustainability of the plant.To solve this problem, this research used the instrument testing, digital simulation, data fitting, plant health judgment, landscape ecology, and the theory of evolution of plant communities, taking geographical information system (GISs) and Microsoft Excel as the technology platform combined with MATLAB computer programming language, designed the intelligent decision-making support system for urban plant selection and construction of plant community based on their sunshine requirements (UP-DSS), realized the goal of plant selection and configuration in the digital, systematic and intelligent manner?It produced the following major innovative achievements in the research process:Firstly, this study presents a forecast system for the plant habits of sunshine needs by black-box thinking, plant health judgment, and digital simulation technology, which changed the working manner through the instrument to determine the plant light compensation point (LCP) and light saturation point (LSP). According to our experience, determined the plants LCP and LSP by photosynthetic instrument most are performed in a controlled experimental environment, which is susceptible to environmental conditions, instrument accuracy, operating habits and other factors, and have a long measurement period as well as low efficiency. Our prediction system can analyze the range value of solar radiation needs and sensitivity for different landscape plant by digital simulation of solar radiation combined with the plant health response. Simultaneously, our testing method has many advantages, such as short cycle, low cost, and high efficiency.Secondly, we proposed a model for the construction of plant communities under the condition of solar radiation restrictions, according to this model, we designed 100 community types and to meet the different types of urban regions in greening practice (Central China). The solar radiation has become a crucial factor for limiting plant species selection and community build based on our survey results, especially in the modern residential area that equipped better conservation facilities, and the sunshine condition cannot be improved through artificial measures. According to this hypothesis, this study adopted the evolution theory of xeric plant community, and proposed the model of construction of plant communities, which used to guide the plant selection and community designing. Based on this model combined with plant species of this study, we preset 100 different plant communities and stored those in UP-DSS database that used for the popularization of this system.Thirdly, using GIS technology and MATLAB computer programming language to construct the intelligent decision-making system of urban plant selection and configuration based on their habits of sunshine requirements, to achieve the method for plant selection and configuration in digital, systematic, and intelligent manner. Adopted GIS and MS Excel tools for the storage, analysis and management of building data, terrain data and landscape plant data combined with solar radiation model, Boolean information retrieval models, it implements the intelligent decision-making work for urban plant selection and plant community configuration based on solar radiation. Taking study area as an example for testing, completed the adaptation planning and layout of landscape plants, and the detailed design of plant selection and community structure for waterfront recreation area, biodiversity maintenance area, and road pollution area.Finally, this study used the MATLAB computer programming language and finished the designing of the succinct graphical user interface (GUI) of UP-DSS and making our method easy application. We adopted a case presented in order to test the performance of UP-DSS, it was found that UP-DSS has good practical performance, and can be enough to meet the actual needs of a technology platform for landscape architects.