TY  - JOUR
AU  - Sharma, Sneh
AU  - Devgan, Kashish
AU  - Jangra, Devanshi
AU  - Bhardwaj, Aanshi
AU  - Aggarwal, Shubhani
PY  - 2025
DA  - 2025/09/12
TI  - Neuro-Inspired Alert System for Air Quality Prediction Using Ensemble Preprocessing and SNN Classification
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 1
IS  - 2
SP  - 69
EP  - 79
DO  - 10.62762/TMI.2025.403059
UR  - https://www.icck.org/article/abs/TMI.2025.403059
KW  - air quality index (AQI)
KW  - ensemble learning
KW  - voting regression
KW  - spiking neural network (SNN)
KW  - real-time prediction
KW  - environmental monitoring
KW  - respirable suspended particulate matter (RSPM)
KW  - suspended particulate matter (SPM)
KW  - temporal coding
KW  - machine learning
KW  - neuro-inspired systems
AB  - Air pollution has emerged as a critical challenge, directly affecting human health, urban sustainability, and climate systems. Traditional air-quality index (AQI) prediction models often struggle to provide timely alerts because they are not very sensitive to changes over time and are hard to understand. This paper proposes a Neuro-Inspired Alert System for Air Quality Prediction (NAS--AQP) that incorporates an ensemble learning approach using voting regression to enhance input quality, followed by classification through a Spiking Neural Network (SNN). The system is designed such that it captures the temporal and nonlinear relationships between air pollutants such as Nitrogen Dioxide ($NO_2$), Sulphur Dioxide ($SO_2$), Respirable Suspended Particulate Matter (RSPM) and Suspended Particulate Matter (SPM). The proposed method starts with preprocessing of the data and normalizing the features. After that, models like Linear Regression (LR), Random Forest (RF), and Decision Tree (DT) are trained and evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination ($R^2$) metrics. After the training of above models, a voting based ensemble approach is used to improve AQI regression accuracy. A threshold based rule is then used to convert received, AQI predictions into binary alerts. Finally, an SNN is trained to classify these alerts, achieving energy-efficient, real-time alerting through temporal coding and sparse activation. The ensemble voting regression model achieved an RMSE of 27.91 and MAE of 20.56, while the SNN classifier attained a classification accuracy of 92.4%.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Sharma2025NeuroInspi,
  author = {Sneh Sharma and Kashish Devgan and Devanshi Jangra and Aanshi Bhardwaj and Shubhani Aggarwal},
  title = {Neuro-Inspired Alert System for Air Quality Prediction Using Ensemble Preprocessing and SNN Classification},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {69-79},
  doi = {10.62762/TMI.2025.403059},
  url = {https://www.icck.org/article/abs/TMI.2025.403059},
  abstract = {Air pollution has emerged as a critical challenge, directly affecting human health, urban sustainability, and climate systems. Traditional air-quality index (AQI) prediction models often struggle to provide timely alerts because they are not very sensitive to changes over time and are hard to understand. This paper proposes a Neuro-Inspired Alert System for Air Quality Prediction (NAS--AQP) that incorporates an ensemble learning approach using voting regression to enhance input quality, followed by classification through a Spiking Neural Network (SNN). The system is designed such that it captures the temporal and nonlinear relationships between air pollutants such as Nitrogen Dioxide (\$NO\_2\$), Sulphur Dioxide (\$SO\_2\$), Respirable Suspended Particulate Matter (RSPM) and Suspended Particulate Matter (SPM). The proposed method starts with preprocessing of the data and normalizing the features. After that, models like Linear Regression (LR), Random Forest (RF), and Decision Tree (DT) are trained and evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (\$R^2\$) metrics. After the training of above models, a voting based ensemble approach is used to improve AQI regression accuracy. A threshold based rule is then used to convert received, AQI predictions into binary alerts. Finally, an SNN is trained to classify these alerts, achieving energy-efficient, real-time alerting through temporal coding and sparse activation. The ensemble voting regression model achieved an RMSE of 27.91 and MAE of 20.56, while the SNN classifier attained a classification accuracy of 92.4\%.},
  keywords = {air quality index (AQI), ensemble learning, voting regression, spiking neural network (SNN), real-time prediction, environmental monitoring, respirable suspended particulate matter (RSPM), suspended particulate matter (SPM), temporal coding, machine learning, neuro-inspired systems},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}