Deep learning models for spatial prediction of fine particulate matter

Colchado Soncco, Luis Ernesto

Deep learning models for spatial prediction of fine particulate matter

dc.contributor.advisor	Ochoa Luna, Jose Eduardo
dc.contributor.author	Colchado Soncco, Luis Ernesto
dc.date.accessioned	2023-11-24T21:14:14Z
dc.date.available	2023-11-24T21:14:14Z
dc.date.issued	2023
dc.description.abstract	Studies indicate that air pollutant concentrations affect human health. Especially, Fine Particulate Matter (PM2.5) is the most dangerous pollutant because this is related to cardiovascular and respiratory diseases, among others. Therefore, governments must monitor and control pollutant concentrations. To this end, many of them have implemented Air quality monitoring (AQM) networks. However, AQM stations are usually spatially sparse due to their high costs in implementation and maintenance, leaving large áreas without a measure of pollution. Numerical models based on the simulation of diffusion and reaction process of air pollutants have been proposed to infer their spatial distribution. However, these models often require an extensive inventory of data and variables, as well as high-end computing hardware. In this research, we propose two deep learning models. The first is a generative model called Conditional Generative adversarial Network (cGAN). Additionally, we add a loss based on the predicted observation and the k nearest neighbor stations to smooth the randomness of adversarial learning. This variation is called Spatial-learning cGAN (cGANSL), which got better performance for spatial prediction. To interpolate PM2.5 on a location, cGANSL and classical methods like Inverse Distance Weighting (IDW) need to select the k nearest neighbor stations based on straight distance. However, this selection may leave out data from more distant neighbors that could provide valuable information. In this sense, the second proposed model in this study is a Neural Network with an attention-based layer. This model uses a recently proposed attention layer to build a structured graph of the AQM stations, where each station is a graph node to weight the k nearest neighbors for nodes based on attention kernels. The learned attention layer can generate a transformed feature representation for unobserved location, which is further processed by a neural network to infer the pollutant concentration. Based on data from AQM network in Beijing, meteorological conditions, and information from satellite products such as vegetation index (NDVI) and human activity or population-based on Nighttime Light producto (NTL). The cGANSL had a better performance than IDW, Ordinary Kriging (OK), and Neural Network with an attention mechanism. In this experiment, spatial prediction models that selected the k nearest neighbors had a good performance. That may be AQM station Beijing’s high correlation between them. However, using data from the AQM network of Sao Paulo, where AQM stations have a low correlation, the Neural network with an attention-based layer have better performance than IDW, OK, and cGANSL. Besides, the normalized attention weights computed by our attention model showed that in some cases, the attention given to the nearest nodes is independent of their spatial distances. Therefore, the attention model is more flexible since it can learn to interpolate PM2.5 concentration levels based on the available data of the AQM network and some context information. Finally, we found that NDVI and NTL are high related to air pollutant concentration predicted by the attention model.	es_PE
dc.description.uri	Tesis de maestría	es_PE
dc.format	application/pdf
dc.identifier.other	1080228
dc.identifier.uri	https://hdl.handle.net/20.500.12590/17845
dc.language.iso	eng
dc.publisher	Universidad Católica San Pablo
dc.publisher.country	PE
dc.rights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by-nc/4.0/
dc.subject	Spatial prediction	es_PE
dc.subject	Fine particulate matter	es_PE
dc.subject	k-Nearest neighbors	es_PE
dc.subject	Generative modeling	es_PE
dc.subject	Attention mechanism	es_PE
dc.subject	Deep learning	es_PE
dc.subject.ocde	http://purl.org/pe-repo/ocde/ford#1.02.01
dc.title	Deep learning models for spatial prediction of fine particulate matter	es_PE
dc.type	info:eu-repo/semantics/masterThesis
dc.type.version	info:eu-repo/semantics/publishedVersion
renati.advisor.dni	29738760
renati.advisor.orcid	https://orcid.org/0000-0002-8979-3785
renati.discipline	611017
renati.juror	Cuadros Vargas, Alex Jesús
renati.juror	Villanueva Talavera, Edwin Rafael
renati.juror	Calcina Ccori, Pablo Cesar
renati.level	https://purl.org/pe-repo/renati/level#maestro
renati.type	https://purl.org/pe-repo/renati/type#tesis
thesis.degree.discipline	Ciencia de la Computación	es_PE
thesis.degree.grantor	Universidad Católica San Pablo. Departamento de Ciencia de la Computación
thesis.degree.level	Maestría	es_PE
thesis.degree.name	Maestro en Ciencia de la Computación	es_PE
thesis.degree.program	Escuela Profesional Ciencia de la Computación	es_PE