神经网络向深度学习的演变,用于预测空气,水污染及其印度背景
International journal of environmental science and technology : IJEST
|June 26, 2023
概括
现代生活方式影响环境质量,需要污染预测. 这篇评论探讨了用于预测空气和水污染的神经网络和深度学习算法,重点关注印度数据集.
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:
- 现代生活方式对环境质量产生负面影响,对人类和野生动物造成健康风险.
- 环境污染物的准确测量和预测对于公共安全和生态保护至关重要.
- 文明的副产品,如污染,需要先进的方法来评估和减轻损害.
研究的目的:
- 审查神经网络和深度学习算法在预测空气和水污染方面的应用.
- 突出空气和水污染研究的算法,数据集和预测参数.
- 探索人工神经网络和深度学习技术的潜力,以实现跨应用的环境监测.
主要方法:
- 文献综述专注于利用神经网络和深度学习进行污染预测的研究.
- 从选定的研究论文中分析算法,数据集和预测的环境参数.
- 强调印度环境中的空气和水污染研究.
主要成果:
- 神经网络和深度学习算法在预测空气和水污染方面显示出显著的前景.
- 已经使用了各种算法和数据集,并确定了用于预测的特定参数.
- 印度的研究为开发本地化污染预测模型提供了巨大的潜力.
结论:
- 神经网络和深度学习技术是环境污染预测的有效工具.
- 对数据集和参数的标准化方法可以增强未来的研究.
- 这些人工智能技术在不同类型的环境污染中交叉应用是可行的和有益的.
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