Spatiotemporal Graph Convolutional Network-Based Long Short-Term Memory Model with A* Search Path Navigation and Explainable Artificial Intelligence for Carbon Monoxide Prediction in Northern Cape Province, South Africa

Guardado en:

Detalles Bibliográficos
Publicado en:	Atmosphere vol. 16, no. 9 (2025), p. 1107-1136
Autor principal:	Agbehadji Israel Edem
Otros Autores:	Christiana, Obagbuwa Ibidun
Publicado:	MDPI AG
Materias:	Karoo South Africa Global warming Nitrogen dioxide Capes (landforms) Artificial neural networks Navigation Fossil fuels Vandalism Air pollution Sea level rise Long short-term memory Random sampling Sampling techniques Prediction models Relative humidity Cognition & reasoning Carbon monoxide Data integrity Air Search methods Statistical sampling Root-mean-square errors Particulate matter Artificial intelligence Nitrogen compounds Pollutants Relocation Deep learning Climate and human activity Trends Climate change Navigation systems Outdoor air quality Explainable artificial intelligence Machine learning Roads & highways Neural networks Weather patterns Literature reviews
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Descripción
Resumen:	Background: The emission of air pollutants into the atmosphere is a global issue as it contributes to global warming and climate-related issues. Human activities like the burning of fossil fuel influence changes in weather patterns—resulting in issues such as a rise in sea levels, among other things. Identifying road network routes within Northern Cape Province in South Africa that are less exposed to air pollutants like carbon monoxide is the issue this study seeks to address. Methods: The method used for our predictions is based on a graph convolutional network (GCN) and long short-term memory (LSTM). The GCN extracts geospatial characteristics, and the LSTM captures both nonlinear relationships and temporal dependencies in an air pollutant and meteorological dataset. Furthermore, an A* search strategy identifies the path from one location to another with the lowest carbon monoxide concentrations within a road network. The explainable artificial intelligence (xAI) technique is used to describe the nonlinear relationship between the target variable and features. Meteorological and air pollutant data in the form of statistical mean, minimum, and maximum values were leveraged, and a random sampling technique was utilized to fill the data gap to help train the predictive model (GCN-LSTM-A*). Results: The predictive model was evaluated with mean squared error (MSE) and root mean squared error (RMSE) values within two multi-time steps (8 and 16 h) with MSEs of 0.1648 and 0.1701, respectively. The LIME technique, which provides explanations of features, shows that Wind_speed and NO2 and NOx concentrations decreased the predicted CO, whereas PM2.5, PM10, relative humidity, and O3 increased the predicted CO of the route.
ISSN:	2073-4433
DOI:	10.3390/atmos16091107
Fuente:	Publicly Available Content Database