Prediction model of algal blooms using logistic regression and confusion matrix
Prediction model of algal blooms using logistic regression and confusion matrix
复制标题
使用逻辑回归和混淆矩阵的藻华预测模型
DOI：
10.11591/ijece.v11i3.pp2407-2413
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发表时间：
2021
影响因子：
--
通讯作者：
Hongwon Yun
中科院分区：
文献类型：
--
作者：
Hongwon Yun
采集藻类水华数据，并将其提炼为藻类水华预报的实验数据。采用Logistic回归分析方法对精细化藻华数据集进行分析，并进行统计检验和正则化处理，找出影响藻华发生的海洋环境因子。利用影响赤潮的海洋环境因子进行Logistic回归分析，得到赤潮的预测值。将藻华数据集的实际值和预测值应用于混淆矩阵。通过改进现有Logistic回归的决策边界，提高了藻华预报的准确性、敏感性和精确度。本文利用Logistic回归和混淆矩阵，采用集成方法建立了赤潮预报模型。改进了水华预报，并通过大数据分析验证了这一点。
Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.