Bridging prediction and immersion: A GTB–FSM framework for urban heat island simulation in 3D virtual cities

Arif, Yunifa Miftachul ORCID: https://orcid.org/0000-0002-2183-0762, Rohma, Salma Ainur, Daniyal, Muhammad, Arkan, Maulana Hilmi, Kusumadewi, Tarranita ORCID: https://orcid.org/0000-0001-8290-2451, Karami, Ahmad Fahmi, Nurhayati, Hani and Nugroho, Fresy ORCID: https://orcid.org/0000-0001-9448-316X (2026) Bridging prediction and immersion: A GTB–FSM framework for urban heat island simulation in 3D virtual cities. International Journal of Intelligent Engineering and Systems. ISSN 2185-3118

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Abstract

The Urban Heat Island (UHI) effect intensifies thermal stress in cities, requiring tools that combine predictive accuracy with effective visualization. This study introduces a framework that integrates Gradient Tree Boosting (GTB) for Land Surface Temperature (LST) prediction with a Finite State Machines (FSM) for dynamic simulation in a 3D virtual city. Using multisource satellite and urban form data, the GTB model achieved high predictive accuracy (R² = 0.88). The outputs were embedded into a virtual environment where FSM logic classified thermal states and enabled real-time transitions across Cool, Transition, and Hot Zones. Usability testing involving 30 participants produced an average System Usability Scale (SUS) score of 88.92, indicating excellent user acceptance. The results show that the proposed platform not only strengthens UHI prediction but also provides an interactive and immersive medium for stakeholder engagement and climate-adaptive urban planning.

Item Type:	Journal Article
Keywords:	Urban heat island, Prediction, Gradient tree boosting, Finite state machine, Immersive simulation
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0803 Computer Software > 080305 Multimedia Programming 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Yunifa Miftachul Arif
Date Deposited:	09 Jul 2026 15:23

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