考虑生物多样性及人类用水之水资源优化管理模式
Sustainable Water Resource Management by Considering Both Human and Ecosystem Needs

DOI: 10.12677/JWRR.2016.54044, PP. 379-390

Keywords: 河川流量管理，台湾生态水文指针系统，人工智能技术，非支配排序基因遗传算法II，永续水资源管理
Streamflow Regime Management, Taiwan Eco-Hydrologic Indicator System (TEIS), Artificial Intelligence Techniques, Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Sustainable Water Resources Management

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Abstract:

近年来由于全球气候变迁的因应及生态环境复育意识的提升，人类开始重视与生态、环境的共存关系；河川流量管理即为一兼顾人类使用需求及河川生态系统需求之理念，将生态观念融入河川流量经营管理之中，以达到人类与河川生态系统共存的理想；台湾生态水文指针系统系永续水资源管理的重要参考指标，使环境资源之开发利用在环境所能负荷之范围内进行，以维持永续发展原则；本研究首先应用人工智能技术，架构复合式类神经网络，并透过此模式利用台湾生态水文指针推估河川生态系统中之鱼类生物多样性，结果显示此模式不但能有系统的分类归纳河川流量数据，并能快速、有效率且精确的推估鱼类之生物多样性；最后基于考虑河川流态于提升生物多样性的概念，应用非支配排序基因遗传算法II (NSGA-II)，建立考虑生物多样性为原则之多目标水资源管理策略；多目标演算模式应用对人类需求目标函数的订定乃期望能达到总缺水量最少、缺水指标最小，并避免缺水状况过于集中于某期间而造成严重缺水之枯旱情形发生。对生态系统需求目标函数的订定则为类神经网络所推估之生物多样性，期能藉由流量管理满足人类需求亦兼顾良好的河川生物多样性，具体建立提升河川生物多样性及考虑人类用水之永续水资源管理。
In response to global climate change and the raise of eco-environmental restoration concept, the equity between ecosystems, environment and human beings gains increasing attention for the past years. The concept of streamflow regime management is to incorporate ecological sustainability into flow regime management by taking the needs of both human and river ecosystems into consideration. The Taiwan Eco-hydrologic Indicator System (TEIS) is an important guiding reference for sustainable water resources management, which confines water resources development to environmental load for maintaining sus-tainable development principles. This study uses artificial intelligence techniques to build up a hybrid ANN that combines the self-organizing feature map (SOM) and the radial basis function neural networks (RBFNNs) into the self-organizing radial basis network (SORBN) for estimating fish bio-diversity based on TEIS statistics. The results show that this model not only can categorize stream flow data but also can es-timate fish bio-diversity quickly, efficiently and precisely. Then, the concept of improving riverine biodi-versity is implemented to develop sustainable water resource management by considering both human and ecosystem needs by using the non-dominated sorting genetic algorithm II (NSGA-II). For the mul-ti-objective algorithm, the objective function for human requirements expects to provide the least volume of total water deficit and the smallest water shortage index, and avoid the occurrence of serious drought periods due to over-concentrated water shortage in certain period; while the objective function, i.e., the biodiversity estimated by ANN, for ecological requirements expects to satisfy human requirements as well as riverine biodiversity based on flow regime management. This study also provides a sustainable water resources management which can satisfy human and ecosystem needs simultaneously.

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