Cellular Automata–Artificial Neural Network Approach to Dynamically Model Past and Future Surface Temperature Changes: A Case of a Rapidly Urbanizing Island Area, Indonesia

Wenang Anurogo, Agave Putra Avedo Tarigan, Debby Seftyarizki, Wikan Jaya Prihantarto, Junhee Woo, Leon dos Santos Catarino, Amarpreet Singh Arora, Emilien Gohaud, Birte Meller and Thorsten Schuetze ()
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Wenang Anurogo: Department of Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
Agave Putra Avedo Tarigan: Geomatics Technology Study Program Politeknik Negeri Batam, Batam Municipality 29466, Indonesia
Debby Seftyarizki: Department of Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
Wikan Jaya Prihantarto: Remote Sensing and Geographic Information System, Vocational School, Universitas Negeri Padang, Padang 25131, Indonesia
Junhee Woo: Department of Global Smart City, Sungkyunkwan University, Suwon 16419, Republic of Korea
Leon dos Santos Catarino: Department of Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
Amarpreet Singh Arora: Department of Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
Emilien Gohaud: Department of Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
Birte Meller: Department of Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
Thorsten Schuetze: Department of Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea

Land, 2025, vol. 14, issue 8, 1-20

Abstract: In 2024, significant increases in surface temperature were recorded in Batam City and Bintan Regency, marking the highest levels observed in regional climate monitoring. The rapid conversion of vegetated land into residential and industrial areas has been identified as a major contributor to the acceleration of local climate warming. Climatological analysis also revealed extreme temperature fluctuations, underscoring the urgent need to understand spatial patterns of temperature distribution in response to climate change and weather variability. This research uses a Cellular Automata–Artificial Neural Network (CA−ANN) approach to model spatial and temporal changes in land surface temperature across the Riau Islands. To overcome the limitations of single-model predictions in a geographically diverse and unevenly developed region, Landsat satellite imagery from 2014, 2019, and 2024 was analyzed. Surface temperature data were extracted using the Brightness Temperature Transformation method. The CA−ANN model, implemented via the MOLUSCE platform in QGIS, incorporated additional environmental variables, such as rainfall distribution, vegetation density, and drought indices, to simulate future climate scenarios. Model validation yielded a Kappa accuracy coefficient of 0.72 for the 2029 projection, demonstrating reliable performance in capturing complex climate–environment interactions. The projection results indicate a continued upward trend in surface temperatures, emphasizing the urgent need for effective mitigation strategies. The findings highlight the essential role of remote sensing and spatial modeling in climate monitoring and policy formulation, especially for small island regions susceptible to microclimatic changes. Despite the strengths of the CA−ANN modeling framework, several inherent limitations constrain its application, particularly in the complex and heterogeneous context of tropical island environments. Notably, the accuracy of model predictions can be limited by the spatial resolution of satellite imagery and the quality of auxiliary environmental data, which may not fully capture fine-scale microclimatic variations.

Keywords: land surface temperature; cellular automata–artificial neural network; urban heat island effect; spatio-temporal modelling; remote sensing analysis; climate change adaptation; urbanization impacts; tropical island environment; landsat satellite imagery; SDG 13 implementation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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