Predicting municipal solid waste generation using artificial intelligence: A hybrid approach of entropy analysis and SHAP for optimal feature selection
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View abstract on PubMed
Summary
This summary is machine-generated.This study enhances municipal solid waste (MSW) generation predictions using a hybrid AI model. The MI-SHAP feature selection method improved accuracy by identifying key factors like population and income.
Area Of Science
- Environmental Science
- Data Science
- Urban Planning
Background
- Municipal solid waste (MSW) management is a critical urban challenge.
- Accurate waste generation prediction is essential for effective MSW management.
- Existing prediction models often lack precision in identifying key influencing factors.
Purpose Of The Study
- To develop and validate a hybrid Artificial Intelligence (AI) approach for improved MSW generation prediction.
- To integrate Mutual Information (MI) and Shapley Additive Explanations (SHAP) for advanced feature selection.
- To identify dominant factors influencing MSW generation across diverse urban environments.
Main Methods
- Employed a hybrid feature selection method combining Mutual Information (MI) and Shapley Additive Explanations (SHAP).
- Utilized Feed Forward Neural Network (FFNN) and Long Short-Term Memory (LSTM) models for prediction.
- Applied the methodology to meteorological and socio-economic data from Austin (USA), Ballarat (Australia), and Boralesgamuwa (Sri Lanka).
Main Results
- The MI-SHAP approach effectively identified key predictors: population, income, Consumer Price Index (CPI), and lagged MSW variables (5, 10, 20 days).
- FFNN models showed good performance in Austin (Training DC: 0.7226, Testing DC: 0.6529) and Ballarat (Training DC: 0.7037, Testing DC: 0.6941).
- Data limitations in Boralesgamuwa resulted in poor model performance, underscoring data quality importance.
Conclusions
- The MI-SHAP hybrid method enhances MSW prediction accuracy by capturing complex variable relationships.
- The study highlights the impact of data quality and socio-economic stability on model performance.
- This methodology offers a pathway for developing data-driven, sustainable MSW management strategies globally.
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