Analysis and Prediction of Land Cover Change in Palangka Raya City Using a Cellular Automata–Neural Network Model Based on MOLUSCE

Charly Bravo Wanggai; Supardi; Ceni Febi Kurnia Sari; Zulfikar Mardiyadi; Eka Febi Selvandy Putri

doi:10.24036/jtip.v19i2.1117

Authors

Charly Bravo Wanggai Universitas Papua
Supardi Universitas Papua
Ceni Febi Kurnia Sari Universitas Papua
Zulfikar Mardiyadi Universitas Papua
Eka Febi Selvandy Putri Universitas Papua

DOI:

https://doi.org/10.24036/jtip.v19i2.1117

Keywords:

Artificial Neural Network, Cellular Automata, Palangka Raya City, Land Cover Prediction, MOLUSCE

Abstract

Land cover change represents a dynamic phenomenon driven by human activities and rapid regional growth, particularly in Palangka Raya City. This study aimed to analyze the historical dynamics of land cover changes during the 2016–2020 period and predict development trends for 2028 and 2040. The methodology integrated Cellular Automata and Artificial Neural Network (CA-ANN) models utilizing the MOLUSCE plugin within Geographic Information System software. Several driving factors were incorporated into the modeling process, including distance from road networks, distance from rivers, slope, elevation, and population density. The analysis revealed a significant transition from natural vegetated areas, such as peat swamp forests and shrublands, into anthropogenic land uses, specifically oil palm plantations and built-up areas. Model validation was performed using the Kappa coefficient test, which yielded a high level of accuracy, thereby confirming the reliability of the model for spatial projection purposes. The prediction results for 2028 and 2040 provided critical spatial insights regarding the potential continuous expansion of built-up areas. These findings were intended to serve as a crucial reference for local governments in formulating sustainable spatial planning policies to mitigate future environmental degradation.

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