Evaluation of Classification Algorithms to Predict Largemouth Bass (Micropterus salmoides) Occurrence
Evaluation of Classification Algorithms to Predict Largemouth Bass (Micropterus salmoides) Occurrence
复制标题
预测大口黑鲈（Micropterus salmoides）出现的分类算法评估
作者：
Zhonghyun Kim;Taeyong Shim;S. Ki;D. Seo;K. An;Jinho Jung
本研究旨在评估分类算法来预测大口黑鲈（Micropterus salmoides）在韩国的出现情况。五年（2011-2015）从四个主要流域的 581 个地点收集了鱼类监测和环境数据（温度、降水量、流量、水质、海拔和坡度）。最初，评估了 caret 包中构建的 13 个分类模型，以预测大口黑鲈的出现。根据准确率（>0.8）和kappa（>0.5）标准，选择前三种分类算法（即随机森林（rf）、C5.0和条件推理随机森林）来开发集成模型。然而，在预测大口黑鲈出现频率方面，组合最佳个体模型并不比最佳个体模型 (rf) 效果更好。此外，年平均气温（12.1 °C）和秋季平均气温（13.6 °C）是区分大口黑鲈存在与否的最重要的环境变量。本研究提出的评估过程将有助于选择预测淡水鱼类出现的预测模型，但需要进一步研究以确保生态可靠性。
This study aimed to evaluate classification algorithms to predict largemouth bass (Micropterus salmoides) occurrence in South Korea. Fish monitoring and environmental data (temperature, precipitation, flow rate, water quality, elevation, and slope) were collected from 581 locations throughout four major river basins for 5 years (2011–2015). Initially, 13 classification models built in the caret package were evaluated for predicting largemouth bass occurrence. Based on the accuracy (>0.8) and kappa (>0.5) criteria, the top three classification algorithms (i.e., random forest (rf), C5.0, and conditional inference random forest) were selected to develop ensemble models. However, combining the best individual models did not work better than the best individual model (rf) at predicting the frequency of largemouth bass occurrence. Additionally, annual mean temperature (12.1 °C) and fall mean temperature (13.6 °C) were the most important environmental variables to discriminate the presence and absence of largemouth bass. The evaluation process proposed in this study will be useful to select a prediction model for the prediction of freshwater fish occurrence but will require further study to ensure ecological reliability.
淡水鱼类分布生态位模型性能的规模效应：当地与上游地区的影响
影响因子：
3.1
作者：
Kärcher;Markovic
通讯作者：
Markovic