Ab'/> Physical habitat simulation for a fish community using the ANFIS method
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Physical habitat simulation for a fish community using the ANFIS method

机译:使用ANFIS方法的鱼群物理栖息地模拟

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AbstractThis study presents physical habitat simulations for the fish community in a reach of the Dal River, Korea. The study reach is 2.3km long, located downstream from the Goesan Dam. The reach is gravel-bed, including a bend. A riffle is present at the apex of the bend, and pools are located before and after the riffle. Field monitoring revealed that five fish species are dominant, namelyZacco platypus,Coreoleuciscus splendidus,Zacco temminckii,Pungfungia herzi, andAcheilognathus yamatsute, and account for 78% of the total fish community. These five fishes, which include both lotic and lentic fishes, were selected as target species. The Center for Computational Hydroscience and Engineering 2D (CCHE2D) model, a 2D shallow water equation solver, and the Adaptive Neuro Fuzzy Inference System (ANFIS) method were used for hydraulic and habitat simulations, respectively. The Mahalanobis distance method was used for identifying outliers in the monitoring data. It was shown that the ANFIS method combined with data quality assessment predicts the Composite Suitability Index (CSI) better than the method without it. The CSI distributions in the study reach are presented for various flows. It was found that the distributions of the combined CSI predicted by the ANFIS method are higher than those predicted by the conventional multiplicative aggregation method with Habitat Suitability Curves (HSCs). In addition, it was revealed that the discharge that yields the maximum Weighted Usable Area (WUA) for the whole fish community is more than twice the discharge yielding the max
机译:<![cdata [ 抽象 本研究提出了韩国Dal River河的鱼群的物理栖息地模拟。该研究达到2.3km长,位于船闸下游。距离是砾石床,包括弯曲。在弯曲的顶点处存在升级,并且游泳池位于浅滩之前和之后。现场监测显示,五种鱼类是显性的,即 zacco鸭嘴兽 coreoleucus splendidus zacco temminckii pungfungia herzi acheilognathus yamatsute ,占Frish社区的78%。这些包括荷花和偶然鱼类的五种鱼类被选为目标物种。用于计算方法和工程2D(CCHE2D)模型,2D浅水方程求解器和自适应神经模糊推理系统(ANFIS)方法的中心分别用于水力和栖息地模拟。 Mahalanobis距离方法用于识别监测数据中的异常值。结果表明,ANFIS方法与数据质量评估相结合预测了比没有它的方法更好的复合适用性指数(CSI)。研究覆盖范围的CSI分布是针对各种流动的。发现,ANFIS方法预测的组合CSI的分布高于具有栖息地适用性曲线(HSC)的传统乘法聚集方法预测的CSI的分布。此外,揭示了产量为整个鱼群的最大加权可用面积(WuA)的放电超过排放量的两倍以上

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