Explainable AI analysis for smog rating prediction

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 8, 2025

Smog poses a direct threat to human health and the environment. Addressing this issue requires understanding how smog is formed. While major contributors include industries, fossil fuels, crop burning, ammonia from fertilizers, vehicles play significant role. Individually, vehicle's contribution may be small, but collectively, vast number of has substantial impact. Manually assessing each vehicle impractical. However, advancements in machine learning make it possible quantify contribution. By creating dataset with features such as model, year, fuel consumption (city), type, predictive model can classify based on their impact, rating them scale 1 (poor) 8 (excellent). This study proposes novel approach using Random Forest Explainable Boosting Classifier models, along SMOTE (Synthetic Minority Oversampling Technique), predict individual vehicles. The results outperform previous studies, proposed achieving an accuracy 86%. Key performance metrics Mean Squared Error 0.2269, R-Squared (R2) 0.9624, Absolute 0.2104, Explained Variance Score 0.9625, Max 4.3500. These incorporate explainable AI techniques, both agnostic specific provide clear actionable insights. work represents step forward, was last updated only five months ago, underscoring timeliness relevance research.

Language: Английский

Prediction of air quality levels to support sustainable development goal – 11 using multiple deep learning classifiers

Smart and Sustainable Built Environment, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Purpose Sustainable Development Goal (SDG) 11 emphasizes the importance of monitoring air quality to develop cities that are resilient, safe and sustainable on a global scale. Particulate matter pollutants such as PM2.5 PM10 have detrimental impact both human health environment. Traditional methods for assessing often face challenges related scalability accuracy. This paper aims introduce an automated system designed predict levels (AQLs). These categorized good, moderate, unhealthy hazardous, based index. Design/methodology/approach uses dataset 8.1 million records from various US cities. The data undergoes preprocessing remove inconsistencies ensure uniformity. Scaling techniques applied standardize values across dataset. Augmentation methods, including K Nearest Neighbour, z -score normalization Synthetic Minority Oversampling Technique (SMOTE), employed balance enhance Later, used train eight deep learning models, standard, bidirectional stacked architectures. Additionally, two hybrid models also developed by combining features different Findings validation results demonstrate system’s exceptional performance. Bidirectional GRU model achieves highest accuracy 99.98%. Similarly, RNN + impressive 99.92%. Furthermore, Stacked Gated Recurrent Unit stands out, achieving perfect scores 100% precision, recall F1 score. Originality/value assessment approaches rely heavily basic statistical limited scope their datasets. In contrast, this study presents innovative methodology employs advanced By incorporating sophisticated techniques, proposed significantly enhances detection classification AQLs, setting new benchmark development objectives.

Language: Английский

Palm Oil Production Prediction Using Support Vector Regression Algorithm and Long Short-Term Memory

Published: June 28, 2024

Language: Английский