Environmental Data Analytics for Smart Cities: A Machine Learning and Statistical Approach
Effectively managing carbon monoxide (CO) pollution in complex industrial cities like Jubail remains challenging due to the diversity of emission sources and local environmental dynamics. This study analyzes spatiotemporal CO patterns and builds accurate predictive models using five years (2018–2022...
Saved in:
| Main Authors: | Ali Suliman AlSalehy, Mike Bailey |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Smart Cities |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-6511/8/3/90 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Improving Time Series Data Quality: Identifying Outliers and Handling Missing Values in a Multilocation Gas and Weather Dataset
by: Ali Suliman AlSalehy, et al.
Published: (2025-05-01) -
Automated curation of spatial metadata in environmental monitoring data
by: İlhan Mutlu, et al.
Published: (2025-05-01) -
IoT-driven real-time weather measurement and forecasting mobile application with machine learning integration
by: Jul Jalal Al-Mamur Sayor, et al.
Published: (2025-09-01) -
Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics
by: Snehal Satish, et al.
Published: (2025-05-01) -
Healthcare Analytics Teaching Cases
by: Concetta A. DePaolo, et al.
Published: (2025-07-01)