An endemic carp species, Alburnus tarichi, inhabits Van Lake Basin in Turkey, where approximately 10,000 tons of this economical and anadromous species are caught each year. Until now, the A. tarichi population has been statistically analyzed based only on caught fish, which provides insufficient information. When these fish reach maturity, do they go to the water sources where their parents spawn, as do salmon? If Alburnus tarichi go to the same locations as their parents to spawn and hatch, then new strains of this species will start forming over time. This study applies two machine learning classification algorithms, k-nearest neighbor (k-NN) and support vector machine (SVM), to an original dataset for A. tarichi population analysis. Fish from nine areas were caught to prepare the original dataset. Five strains were found with machine learning classification algorithms in the Van Lake Basin, and results show that the accuracy levels of the k-NN algorithm were superior to those of the SVM algorithm in the population analysis of Alburnus tarichi.

一种特有的鲤鱼物种，Alburnus tarichi，栖息在土耳其的范湖盆地，每年大约捕获 10,000 吨这种经济和溯河种的鱼类。到目前为止，仅根据捕获的鱼对A. tarichi种群进行了统计分析，这提供的信息不足。当这些鱼成熟时，它们会像鲑鱼一样去它们父母产卵的水源吗？如果Alburnus tarichi去与其父母相同的地点产卵和孵化，那么随着时间的推移，该物种的新品系将开始形成。本研究将两种机器学习分类算法，k-最近邻 (k-NN) 和支持向量机 (SVM)，应用于A. tarichi的原始数据集人口分析。捕获来自九个区域的鱼以准备原始数据集。在 Van Lake Basin 用机器学习分类算法发现了 5 株菌，结果表明，k-NN 算法的准确度水平优于 SVM 算法在Alburnus tarichi种群分析中的准确度水平。