A Hybrid Gradient Boosting and Neural Network Model for Predicting Urban Happiness: Integrating Ensemble Learning with Deep Representation for Enhanced Accuracy
Urban happiness prediction presents a complex challenge, due to the nonlinear and multifaceted relationships among socio-economic, environmental, and infrastructural factors. This study introduces an advanced hybrid model combining a gradient boosting machine (GBM) and neural network (NN) to address...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Machine Learning and Knowledge Extraction |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4990/7/1/4 |
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| Summary: | Urban happiness prediction presents a complex challenge, due to the nonlinear and multifaceted relationships among socio-economic, environmental, and infrastructural factors. This study introduces an advanced hybrid model combining a gradient boosting machine (GBM) and neural network (NN) to address these complexities. Unlike traditional approaches, this hybrid leverages a GBM to handle structured data features and an NN to extract deeper nonlinear relationships. The model was evaluated against various baseline machine learning and deep learning models, including a random forest, CNN, LSTM, CatBoost, and TabNet, using metrics such as RMSE, MAE, R<sup>2</sup>, and MAPE. The GBM + NN hybrid achieved superior performance, with the lowest RMSE of 0.3332, an R<sup>2</sup> of 0.9673, and an MAPE of 7.0082%. The model also revealed significant insights into urban indicators, such as a 10% improvement in air quality correlating to a 5% increase in happiness. These findings underscore the potential of hybrid models in urban analytics, offering both predictive accuracy and actionable insights for urban planners. |
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| ISSN: | 2504-4990 |