模拟溶解氧度使用机器学习技术与缩小维度的方法
Farid Hassanbaki Garabaghi1, Semra Benzer2, Recep Benzer3
1Graduate School of Natural and Applied Sciences, Gazi University, Teknikokullar, 06500, Turkey. fgarabaghi@gmail.com.
Environmental monitoring and assessment
|June 24, 2023
概括
准确的溶解氧 (DO) 预测对于水生生态系统至关重要. 这项研究发现随机森林 (RF) 模型,通过特征选择进行增强,显著提高了DO度预测的准确性.
科学领域:
- 环境科学 环境科学
- 水质监测 水质监测
- 预测建模预测建模
背景情况:
- 溶解氧 (DO) 对水生生物和生态系统健康至关重要.
- 准确预测DO水平对于有效的水资源管理至关重要.
- 现有的监测方法需要增强预测能力.
研究的目的:
- 为水生系统中溶解氧 (DO) 度开发一个准确的预测模型.
- 评估随机森林 (RF) 和多层感知子 (MLP) 算法的DO预测性能.
- 评估缩小尺寸对模型性能的影响.
主要方法:
- 使用随机森林 (RF) 和多层感知子 (MLP) 算法进行回归建模.
- 应用的封装特征选择,以减少数据集的维度.
- 使用皮尔森相关系数,平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 评估模型性能.
主要成果:
- 射频回归器在预测DO度方面表现优于MLP算法.
- 尺寸缩小显著减少了两个模型的估计误差偏差.
- 射频回归器在较小的可变数据集下实现了最佳性能,显示了0.8052.2的皮尔森相关系数.
结论:
- 随机森林 (RF) 是一个强大的回归器,用于预测DO度.
- 尺寸缩小对DO预测模型的准确性产生了积极的影响.
- 特性选择提高了机器学习模型对水质参数的预测能力.
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