MLP Enhanced CO2 Emission Prediction Model with LWSSA Nature Inspired Optimization
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel machine learning framework for accurate carbon dioxide (CO₂) emission prediction. The hybrid model identifies key emission drivers, offering insights for environmental policy and sustainable development.
Area Of Science
- Environmental Science
- Computer Science
- Data Science
Background
- Rising carbon dioxide (CO₂) emissions pose a significant environmental threat, demanding advanced prediction methods.
- Machine learning (ML) offers powerful tools for analyzing complex emission patterns and identifying influential factors.
- Traditional optimization methods for ML models often suffer from limitations like premature convergence.
Purpose Of The Study
- To develop and validate a novel hybrid ML framework for precise CO₂ emission prediction.
- To enhance the optimization process for ML models, overcoming limitations of existing techniques.
- To identify and rank the most significant factors contributing to CO₂ emissions.
Main Methods
- A hybrid framework combining a Multi-Layer Perceptron (MLP) with an enhanced Locally Weighted Salp Swarm Algorithm (LWSSA).
- The LWSSA integrates a Locally Weighted Mechanism (LWM) and a Mutation Mechanism (MM) for improved optimization.
- Permutation feature significance analysis was employed to determine factor importance.
Main Results
- The LWSSA-MLP framework achieved a high prediction accuracy of 97%.
- The proposed model demonstrated superior performance compared to traditional optimizer-based MLP models.
- Key drivers identified include global trade, coal energy, export levels, urbanization, and natural resources.
Conclusions
- The study presents a reliable and scalable framework for CO₂ emission prediction.
- The findings provide valuable insights for developing targeted interventions and sustainable development strategies.
- The enhanced LWSSA-MLP model offers a robust solution for environmental monitoring and policy-making.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
