生态感:智慧城市城市城市空气质量预测的革命
Kalyan Chatterjee1, Machakanti Navya Thara2, Mandadi Sriya Reddy3
1Computer Science & Engineering, Nalla Malla Reddy Engineering College, Hyderabad, 500088, Telangana, India.
BMC research notes
|February 11, 2025
概括
本研究介绍了EcoSense (BlaSt),这是一个先进的城市空气质量预测模型,用于智能城市. 它通过将历史数据与天气智能网格集成,显著提高了预测准确性和效率.
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:
- 智能城市 (SC) 框架增强了城市生活,但面临着物联网 (IoT) 设备浪费和资源使用带来的挑战.
- 将天气智能电网 (WSG) 与SC集成,对于环境保护和居民福祉至关重要.
研究的目的:
- 提出EcoSense (BlaSt),这是智慧城市城市空气质量预测的新方法.
- 通过整合历史数据和WSG,提高空气质量预测的准确性和效率.
主要方法:
- 开发了BlaSt,一种使用双向堆叠LSTM与天气智能电网的模型.
- 纳入历史空气污染物和气象数据以捕捉时间依赖.
- 设计了1小时预测模型,以高精度生成12小时的预测.
主要成果:
- 与现有模型相比,BlaSt实现了显著的准确性改进 (例如,比SVR提高36%,比MLP提高26%).
- 获得了0.10的平均绝对误差 (MAE) 和0.08的平均平方误差 (MSE).
- 减少了25%的计算复杂性,提高了大规模数据处理的效率.
结论:
- 在城市空气质量预测方面,BlaSt表现出卓越的准确性和效率.
- 该模型显示了在智能城市中推进空气质量管理的巨大潜力.
- EcoSense提供了一种可持续的解决方案,解决与物联网和环境影响相关的SC挑战.
关键词:
空气污染物的度 (APC)空气质量 空气质量物联网 (IoT) 的物联网 (IoT) 的物联网.气象因素 (MFs) 是指气象因素.智能城市 (SC)天气智能电网 (WSG) 是一个智能电网.更多相关视频
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