A Study of Artificial Neural Networks for Estimating Riverine Biodiversity

• Main Authors: Wen-Ping Tsai, 蔡文柄
• Other Authors: Fi-John Chang
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/58171663789047743500

碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 95 === As the population and demand for land use rapidly increased, the use of environmental resources has exceeded the rate of naturalization that might result in the degeneracy of ecological structure and the decrease of the diversities of species which could reduce the resources provided by environment. Due to the raise of eco-environmental restoration concept in the past several years, people gradually pay attention to the coexistence relationship between human being and eco-environment and the impacts of human activities on eco-environment. Stream flow management is the idea that combines the concept of ecology and provides the demand for both human and river ecosystem. Base on the limited understanding of nature, it’s hard to get acquainted with actual demands of river ecosystem and represent it by numerical methods or formula. Therefore, this study combines Self-Organizing Feature Map (SOM) and Radial Basis Function Neural Networks (RBFNN) into Self-Organizing Radial Basis Neural Networks (SORBNN). By this model, it can be estimated “river bio-diversity” by using the index of Taiwan Ecohydrology Index System and the diversities of fish families are to be the index of bio-diversity. In this research, the stream flow data which are only collected with records more than 20 years and without anthropogenic control would be tested. The result shows that this model not only can categorize the stream flow data but also can estimate the bio-diversity quickly, efficiently and precisely.