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基于深度学习技术的水质预测研究进展:一篇综述

Wenhao Li^1,2, Yin Zhao¹, Yining Zhu^2,3

¹School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China.

Environmental science and pollution research international

|March 28, 2024

概括

准确的水质预测对于资源管理至关重要. 深度学习模型对预测污染物水平充满希望,有助于环境保护工作.

科学领域:

环境科学环境科学
计算机科学计算机科学
数据科学数据科学数据科学

背景情况:

水是一种关键的,不可再生的资源,对生存和发展至关重要.
准确的水质预测是信息化的环境管理和干预的关键.
深度学习为水质预测提供了强大的数据分析.

研究的目的:

审查深度学习在水质预测中的应用.
探索单模型和混合模型深度学习方法.
确定优化和数据融合策略,以提高预测准确度.

主要方法:

对水质预测中的深度学习方法的综合文献综述.
分析单模型和混合模型深度学习框架.
检查优化技术和数据融合策略.

主要成果:

深度学习模型越来越多地被部署用于水质预测.
包括优化和数据融合在内的各种因素会影响模型的有效性.
挑战包括数据稀缺性和长期预测准确性.

结论:

深度学习显示了准确预测水质的巨大潜力.
解决数据稀缺等局限性对于未来的进步至关重要.
精细的算法和大规模应用将增强水资源管理.

关键词:

数据分解算法数据分解算法深度学习是一种深度学习.混合动力模型混合动力模型神经网络的神经网络的神经网络优化算法优化算法预测水质水质预测

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