在环境科学和工程学研究中推进多模式学习的观点
Wenjia Liu1, Jingwen Chen1, Haobo Wang1
1Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
Environmental science & technology
|September 3, 2024
概括
多模式学习 (MML) 通过整合各种数据,为复杂的环境问题提供先进的解决方案. 这种方法增强了环境科学和工程 (ES&E) 的预测模型,以更好地评估环境质量和控制污染.
科学领域:
- 环境科学与工程 (ES&E)
- 多式模式学习 (MML)
- 环境研究中的人工智能
背景情况:
- 人为影响日益增加,导致复杂的环境问题,通常涉及不同的数据模式.
- 目前在ES&E中的机器学习 (ML) 模型经常忽视多式联络数据的潜力.
- 环境挑战威胁着对人类福祉至关重要的自然资本.
研究的目的:
- 探索多模式学习 (MML) 在环境科学和工程 (ES&E) 中的应用.
- 总结MML方法及其对环境建模的潜在好处.
- 确定在ES&E中对MML的挑战和未来研究方向.
主要方法:
- 审查和总结现有的多式模式学习 (MML) 方法.
- 确定MML在环境科学和工程 (ES&E) 中的潜在应用.
- 讨论实施挑战和未来的研究途径.
主要成果:
- 多模式学习 (MML) 可以通过整合各种数据,提供更全面的环境问题的描述.
- MML有可能显著提高ES&E预测模型的准确性和稳定性.
- 拟议的应用包括环境质量评估,化学危害预测和污染控制优化.
结论:
- 多模式学习 (MML) 是解决复杂环境挑战的一个有希望的方法.
- 通过MML利用各种数据模式,可以带来更好的环境建模和解决方案.
- 需要进一步的研究来克服实施挑战,并充分实现MML在ES&E中的潜力.
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