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首页> 外文期刊>Ecological Modelling >Monitoring and estimating the flow conditions and fish presence probability under various flow conditions at reach scale using genetic algorithms and kriging methods
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Monitoring and estimating the flow conditions and fish presence probability under various flow conditions at reach scale using genetic algorithms and kriging methods

机译:使用遗传算法和克里金法,监测和估计各种水流条件下的水流状况和鱼类存在概率

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摘要

The combination of current velocity and water depth influences stream flow conditions, and fish activities prefer particular flow conditions. This study develops a novel optimal flow classification method for identifying types of stream flow based on the current velocity and the water depth using a genetic algorithm. It is applied to the Datuan stream in northern Taiwan. Fish were sampled and their habitat investigated at the study site during the spring, summer, fall and winter of 2008-2009. The current velocity, water depth and maps of the presence probability of fish were estimated by ordinary and indicator kriging. The optimal classification results were compared with the classification results obtained using the Froude number and empirical methods. The flow classification results demonstrate that the proposed optimal flow classification method that considers depth-velocity and optimally identified criteria for classifying flow types, yields a current velocity and water depth of 0.32 (m/s) and 0.29 (m), respectively, and classifies the flow conditions in the study area as pool, run, riffle and slack. The variography results of the current velocity and the water depth data reveal that seasonal flows are not spatially stationary among seasons in the study area. Kriging methods and a two-dimensional hydrodynamic model (River 2D) with empirical and optimal flow classification methods are more effective than the Froude number method in classifying flow conditions in the study area. The flow condition classifications and probability maps were generated by River 2D, ordinary kriging and indicator kriging, to quantify the flow conditions preferred by Sicyopterus japonicus in the study area. However, the proposed optimal classification method with kriging and River 2D is an effective alternative method for mapping flow conditions and determining the relationship between flow and the presence probability of target fish in support of stream restoration.
机译:流速和水深的组合会影响溪流状况,而鱼类活动则偏爱特定的溪流状况。这项研究开发了一种新颖的最佳流分类方法,该方法可以使用遗传算法基于当前速度和水深来识别水流类型。它适用于台湾北部的大端河。在研究地点的2008-2009年春季,夏季,秋季和冬季对鱼类进行了采样并调查了它们的栖息地。通过普通克里金法和指示器克里金法估计了当前速度,水深和鱼类存在概率图。将最佳分类结果与使用Froude数和经验方法获得的分类结果进行比较。流量分类结果表明,所提出的最优流量分类方法考虑了深度速度和最优地确定了用于对流量类型进行分类的标准,得出的当前速度和水深分别为0.32(m / s)和0.29(m),并进行了分类研究区域的流动条件为水池,奔跑,浅滩和松弛。当前速度和水深数据的方差分析结果表明,研究区域中各个季节之间的季节性流量在空间上不是固定的。在研究区域的流动条件分类中,使用克里格方法和带有经验和最佳流分类方法的二维流体力学模型(River 2D)比弗洛德数法更有效。通过River 2D,普通克里金法和指示克瑞金法生成流动条件分类和概率图,以量化研究区内日本鳞翅目(Sicyopterus japonicus)偏爱的流动条件。然而,提出的克里金法和River 2D最优分类方法是一种有效的替代方法,可用于绘制水流状况并确定水流与目标鱼的存在概率之间的关系,以支持水流恢复。

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