基于人工智能的河流水污染检测和控制策略:全面审查
Deepak Bhatt1, Mahendra Swain2, Dhananjay Yadav3
1Department of Computer Science and Engineering, Quantum University, Roorkee, 247667, UK, India.
Journal of contaminant hydrology
|March 20, 2025
概括
人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 为河水质量预测和控制提供了先进的解决方案. 将这些技术与物联网 (IoT) 集成,可以增强实时监控和可持续的水资源管理.
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:
- 河流水质对于生态系统健康和人类安全至关重要.
- 人类活动严重污染了全球的河流系统.
- 管理复杂的,非线性水质数据带来了巨大的挑战.
研究的目的:
- 为预测和控制河水质量 (WQ) 提供人工智能 (AI) 应用的全面审查.
- 分析2019-2024年WQ模拟机器学习 (ML) 和深度学习 (DL) 模型的进展.
- 探索物联网 (IoT) 与人工智能的集成,以加强WQ监控和管理.
主要方法:
- 系统审查和分析超过110篇关于河流WQ建模 (2019-2024) 的研究文章.
- 突出关键的人工智能技术,包括人工神经网络 (ANN),深度神经网络 (DNN),长期短期记忆 (LSTM) 和随机森林 (RF).
- 研究物联网 (IoT) 在实时数据收集和人工智能驱动的WQ分析中的协同作用.
主要成果:
- 人工智能,ML和DL模型在模拟和预测河流WQ方面显示出巨大的潜力.
- 物联网集成提高了预测准确性,并使得连续的,实时的WQ监控.
- 像LSTM和RF这样的特定ML / DL算法对复杂的WQ数据集显示出希望.
结论:
- 先进的AI,ML和DL技术对于应对河水污染控制方面的挑战至关重要.
- 人工智能和物联网的结合为可持续的水资源管理提供了强大的工具.
- 未来的研究应该专注于利用这些创新来保护水生生态系统并确保水安全.
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