揭示新加坡空间PM2.5预测模型的透明度:不同机器学习方法与可解释人工智能的比较

IF 3.1 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
M. S. Shyam Sunder, Vinay Anand Tikkiwal, Arun Kumar, Bhishma Tyagi
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引用次数: 0

摘要

由于气溶胶的直接和间接影响,如散射和吸收辐射能,它们在气候系统中起着至关重要的作用。它们还对能见度和人类健康产生不利影响。人类暴露在细PM2.5中,这对心血管和呼吸系统相关疾病有不利的健康影响。PM浓度的长期趋势受排放和气象变化的影响,而气象因素主要驱动短期变化。植被覆盖度、相对湿度、温度、风速等因素影响着地表PM2.5浓度的发散。事实证明,机器学习可以很好地预测空气质量。本研究的重点是将这些参数作为时空信息的输入来预测PM2.5。本文分析了新加坡7年(2014-2021年)5个地点的PM2.5现场观测数据,并将这些数据集用于PM2.5的空间预测。该研究旨在提供一个基于随机森林(RF)、梯度增强(GB)回归和基于树的管道优化工具(TP)的基于时间的预测的新框架。TP产生合理的全局绩效指标值;2016年8月GPI值最高为7.4,2019年6月最低为- 0.6。这表明TP模型的积极性能;甚至负值也小于其他模型,表示预测不那么悲观。使用可解释人工智能(XAI)技术解释了这些结果,这些技术有助于研究机器学习模型的特征重要性的保真度,以提取有关PM2.5模式节奏变化的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence
Aerosols play a crucial role in the climate system due to direct and indirect effects, such as scattering and absorbing radiant energy. They also have adverse effects on visibility and human health. Humans are exposed to fine PM2.5, which has adverse health impacts related to cardiovascular and respiratory-related diseases. Long-term trends in PM concentrations are influenced by emissions and meteorological variations, while meteorological factors primarily drive short-term variations. Factors such as vegetation cover, relative humidity, temperature, and wind speed impact the divergence in the PM2.5 concentrations on the surface. Machine learning proved to be a good predictor of air quality. This study focuses on predicting PM2.5 with these parameters as input for spatial and temporal information. The work analyzes the in situ observations for PM2.5 over Singapore for seven years (2014–2021) at five locations, and these datasets are used for spatial prediction of PM2.5. The study aims to provide a novel framework based on temporal-based prediction using Random Forest (RF), Gradient Boosting (GB) regression, and Tree-based Pipeline Optimization Tool (TP) Auto ML works based on meta-heuristic via genetic algorithm. TP produced reasonable Global Performance Index values; 7.4 was the highest GPI value in August 2016, and the lowest was −0.6 in June 2019. This indicates the positive performance of the TP model; even the negative values are less than other models, denoting less pessimistic predictions. The outcomes are explained with the eXplainable Artificial Intelligence (XAI) techniques which help to investigate the fidelity of feature importance of the machine learning models to extract information regarding the rhythmic shift of the PM2.5 pattern.
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来源期刊
CiteScore
7.20
自引率
0.00%
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审稿时长
11 weeks
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