{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T22:01:42Z","timestamp":1779141702782,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Atma Jaya Catholic University of Indonesia and the National Science and Technology Council, Taiwan","award":["112-2221-E-194-014-MY3"],"award-info":[{"award-number":["112-2221-E-194-014-MY3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/make7010004","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T03:38:41Z","timestamp":1736221121000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A Hybrid Gradient Boosting and Neural Network Model for Predicting Urban Happiness: Integrating Ensemble Learning with Deep Representation for Enhanced Accuracy"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8162-6942","authenticated-orcid":false,"given":"Gregorius","family":"Airlangga","sequence":"first","affiliation":[{"name":"Department of Information Systems, Atma Jaya Catholic University of Indonesia, Jakarta 12930, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8884-6662","authenticated-orcid":false,"given":"Alan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103229","DOI":"10.1016\/j.cities.2021.103229","article-title":"Urban planning and quality of life: A review of pathways linking the built environment to subjective well-being","volume":"115","author":"Mouratidis","year":"2021","journal-title":"Cities"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103972","DOI":"10.1016\/j.cities.2022.103972","article-title":"Promoting livability through urban planning: A comprehensive framework based on the \u201ctheory of human needs\u201d","volume":"131","author":"Sheikh","year":"2022","journal-title":"Cities"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"152332","DOI":"10.1016\/j.scitotenv.2021.152332","article-title":"COVID-19 and the compact city: Implications for well-being and sustainable urban planning","volume":"811","author":"Mouratidis","year":"2022","journal-title":"Sci. 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