AiM:城市空气质量预测与嵌入式网络的经常性MLP模型.
Kalyan Chatterjee1, Bhoomeshwar Bala1, Mudassir Khan2
1Computer Science & Engineering, Nalla Malla Reddy Engineering College, Hyderabad, Telangana, 500088, India.
Scientific reports
|November 29, 2025
概括
一个新的混合模型,AiM,通过整合空间和时间数据来改善城市空气质量预测. 这种先进的系统为智能城市应用提供了更准确的预测和更快的处理.
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:
- 城市空气污染带来了重大的公共卫生和环境挑战.
- 准确的,低延迟的空气质量预测对于现实世界的智能城市基础设施至关重要.
研究的目的:
- 开发和评估一种新的混合模型,即嵌入式循环多层感知器 (AiM),用于改进城市空气质量预测.
- 通过考虑空间和时间因素,提高空气质量预测的准确性和效率.
主要方法:
- 这是一个混合模型,它结合了循环多层感知器 (R-MLP) 和嵌入式网格框架.
- 基于城市区域的网格分区,以捕捉局部污染物分散模式.
- 特性工程包括污染物相互作用,气象数据和电网相邻性,用于跨区域的相关性分析.
主要成果:
- 与传统模型 (LSTM,GRU,CNN-RNN混合) 相比,AiM显示出更高的预测准确度,降低了RMSE高达12.4%.
- 在边缘设备上减少了35%的推理延迟.
- 该模型显示出高可扩展性和适合于智能城市空气质量管理中的实时部署.
结论:
- 该AiM模型为城市空气质量预测提供了显著的进步,平衡了准确性和效率.
- 它与物联网节点集成的能力使其成为实时智能城市环境监测的可行解决方案.
相关概念视频
01:07End Point Prediction: Gran Plot
1.1K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
1.1K
01:20Maxwell-Boltzmann Distribution: Problem Solving
2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K