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A Hybrid Gradient Boosting and Neural Network Model for Predicting Urban Happiness: Integrating Ensemble Learning with Deep Representation for Enhanced Accuracy

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Publicat a:Machine Learning and Knowledge Extraction vol. 7, no. 1 (2025), p. 4
Autor principal: Gregorius Airlangga
Altres autors: Liu, Alan
Publicat:
MDPI AG
Matèries:
Accuracy
Machine learning
Research methodology
Happiness
Cost of living
Deep learning
Neural networks
Structured data
Well being
Air quality
Outdoor air quality
Econometrics
Decision trees
Ensemble learning
Accés en línia:Citation/Abstract
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Resum: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, R2, and MAPE. The GBM + NN hybrid achieved superior performance, with the lowest RMSE of 0.3332, an R2 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.
ISSN:2504-4990
DOI:10.3390/make7010004
Font:Advanced Technologies & Aerospace Database