Quantifying uncertainty: Air quality forecasting based on dynamic spatial-temporal denoising diffusion Probabilistic model

计算机科学 概率逻辑 背景(考古学) 空气质量指数 人工智能 机器学习 数据挖掘 气象学 地理 考古
作者
Xiaowen Chu,G.Q. Li,H. Li,Yue Wang,Wenzhe Wang,Qingyi Liu,Hongcheng Wang
出处
期刊:Environmental Research [Elsevier BV]
卷期号:: 118438-118438
标识
DOI:10.1016/j.envres.2024.118438
摘要

Air pollution constitutes a substantial peril to human health, thereby catalyzing the evolution of an array of air quality prediction models. These models span from mechanistic and statistical strategies to machine learning methodologies. The burgeoning field of deep learning has given rise to a plethora of advanced models, which have demonstrated commendable performance. However, previous investigations have overlooked the salience of quantifying prediction uncertainties and potential future interconnections among air monitoring stations. Moreover, prior research typically utilized static predetermined spatial relationships, neglecting dynamic dependencies. To address these limitations, we propose a model named Dynamic Spatial-Temporal Denoising Diffusion Probabilistic Model (DST-DDPM) for air quality prediction. Our model is underpinned by the renowned denoising diffusion model, aiding us in discerning indeterminacy. In order to encapsulate dynamic patterns, we design a dynamic context encoder to generate dynamic adjacency matrices, whilst maintaining static spatial information. Furthermore, we incorporate a spatial-temporal denoising model to concurrently learn both spatial and temporal dependencies. Authenticating our model's performance using a real-world dataset collected in Beijing, the outcomes indicate that our model eclipses other baseline models in terms of both short-term and long-term predictions by 1.36% and 11.62% respectively. Finally, we conduct a case study to exhibit our model's capacity to quantify uncertainties.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Zyou完成签到,获得积分10
刚刚
PRIPRO完成签到,获得积分20
2秒前
FashionBoy应助haiwei采纳,获得10
2秒前
刻苦的三问完成签到,获得积分10
3秒前
Mila发布了新的文献求助60
4秒前
风中夜天完成签到,获得积分10
6秒前
sun_lin发布了新的文献求助10
7秒前
10秒前
张同学要谦虚完成签到,获得积分10
10秒前
ding应助智慧女孩采纳,获得10
11秒前
墨菲特发布了新的文献求助10
11秒前
王艺霖完成签到 ,获得积分10
13秒前
脑洞疼应助安静曼寒采纳,获得10
14秒前
14秒前
14秒前
自信河马发布了新的文献求助10
14秒前
陈美丽发布了新的文献求助20
14秒前
16秒前
haiwei发布了新的文献求助10
17秒前
风趣绯发布了新的文献求助10
18秒前
怕黑嘉懿发布了新的文献求助10
20秒前
DijiaXu应助林夕采纳,获得10
20秒前
月倚樱落时完成签到,获得积分10
23秒前
WNing发布了新的文献求助10
24秒前
24秒前
QUA应助科研通管家采纳,获得10
24秒前
丘比特应助科研通管家采纳,获得10
24秒前
24秒前
24秒前
24秒前
24秒前
24秒前
25秒前
25秒前
25秒前
归尘应助欣慰凌丝采纳,获得10
25秒前
25秒前
echo完成签到,获得积分10
27秒前
28秒前
5160完成签到,获得积分10
28秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance 500
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969917
求助须知:如何正确求助?哪些是违规求助? 3514626
关于积分的说明 11175060
捐赠科研通 3249928
什么是DOI,文献DOI怎么找? 1795165
邀请新用户注册赠送积分活动 875617
科研通“疑难数据库(出版商)”最低求助积分说明 804891