基于深度学习的水质评估有效的混合模型
Anıl Utku1, Esen Damla Utku2, Banu Kutlu3
1Department of Computer Engineering, Munzur University, Tunceli, Turkey.
概括
使用卷积神经网络-长期短期记忆 (CNN-LSTM) 的新混合模型有效评估水质. 这种先进的方法通过准确识别污染水平来确保安全的用水.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水资源管理 水资源管理
背景情况:
- 由于气候变化,污染和人口增长,淡水资源正在减少.
- 水污染严重影响生态平衡和人类使用,需要严格的质量控制.
- 有效的水质评估对于可持续的水资源管理至关重要.
研究的目的:
- 开发和评估一种新的混合混合卷积神经网络-长期短期记忆 (CNN-LSTM) 模型,用于准确的水质评估.
- 将拟议的CNN-LSTM模型与已建立的机器学习算法的性能进行比较.
- 根据既定的质量标准,验证模型在确保水安全方面的能力.
主要方法:
- 开发了一种融合卷积神经网络 (CNN) 和长短期记忆 (LSTM) 的混合模型.
- 模型的性能使用包括准确性,精度,回忆,F-score和曲线下的面积 (AUC) 在内的指标进行了评估.
- 对诸如AdaBoost,决策树 (DT),高斯天真贝斯 (GNB),k-最近邻居 (kNN),LightGBM (LGBM) 和随机森林 (RF) 等算法进行了比较分析.
主要成果:
- 拟议的CNN-LSTM模型实现了98.81%的高分类精度.
- 特别的性能得到了99.03%的精度,99.65%的回忆率和99.33%的F-score.
- 该模型显示了93%的强曲线下面面积 (AUC),表明了强大的预测能力.
结论:
- 开发的CNN-LSTM混合模型为水质评估提供了高度准确和可靠的解决方案.
- 该模型显著优于传统方法,为环境监测提供了有价值的工具.
- 这些发现支持该模型在确保安全和可持续利用水资源方面的应用.
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