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基于卷积神经网络随机森林的水环境风险预测方法

Yanan Zhao¹, Lili Zhang¹, Yue Chen¹

¹School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China.

Marine pollution bulletin

|November 13, 2024

概括

本研究提出了一种结合卷积神经网络 (CNN) 和随机森林 (RF) 的新方法,以准确预测水环境风险. 综合方法显著提高了预测准确度,并有助于保护水资源.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
生态生态学生态生态学

背景情况:

城市化和工业化增加了水生环境风险,威胁到水资源和生态系统健康.
准确的水环境风险预测对于识别污染源,保护资源和制定政策至关重要.

研究的目的:

为水环境风险开发一种创新的预测方法.
提高水环境风险评估的准确性和适用性.

主要方法:

卷积神经网络 (CNN) 的集成用于空间特征提取.
随机森林 (RF) 的应用用于多变量数据分析.
将预测结果与卫星图像合并用于可视化.

主要成果:

提高了5.8%的确定系数 (R2).
降低了21.5%的平均绝对误差 (MAE).
降低了41.5%的平均偏差误差 (MBE) 和56.82%的根平均平方误差 (RMSE).

结论:

拟议的CNN-RF方法在预测水环境风险方面取得了重大进展.
该方法促进了直观的可视化,并增强了对复杂的环境数据的决策.
该研究阐明了水环境风险的发展趋势,支持可持续的水资源管理.

关键词:

卷积神经网络是一种卷积神经网络.经验分析是经验分析.预测方法预测方法随机的森林随机的森林水环境风险水环境风险

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