Modeling Urban Land Transformation In Malang City: A Cellular Automata Model With Artificial Neural Networks And Logistic Regression
DOI:
https://doi.org/10.58411/g67mj831Keywords:
Artificial Neural Networks, Cellular Automata, Logistic Regression, land-use change, Malang CityAbstract
This study aims to model urban land transformation in Malang City using a Cellular Automata (CA) approach integrated with Artificial Neural Networks (ANN) and Logistic Regression (LR). The model was developed to predict land-use changes over the next 10 years (2024-2034) by utilizing spatial data from 2014 and 2024. The method involves spatial analysis using Quantum GIS (QGIS) software with the MOLUSCE plugin, which enables the simulation of land cover changes based on transition probability matrices. The results show that the CA-LR model provides higher accuracy compared to the CA-ANN model, with a Kappa value reaching 1 at the location level. The simulations indicate a significant decrease in non-built-up land, from 4,090.85 ha in 2024 to 3,731.40 ha in 2044, while built-up land increased from 7,030.70 ha to 7,390.15 ha over the same period. Factors such as population growth, accessibility, and land prices were identified as the main drivers of land-use change. The findings of this study can serve as a reference for stakeholders in planning sustainable urban development, particularly in managing settlement growth and maintaining a balance between built-up areas and green open spaces.
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Abebe, G., Getachew, D., & Ewunetu, A. (2022). Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia. SN Applied Sciences, 4(1), 30.
Alshari, E., & Gawali, B. (2023). Evaluation of the Multispectral Satellites with Object-Based Classifiers for Land Use and Land Cover Classification (pp. 602–625). https://doi.org/10.2991/978-94-6463-196-8_46
Amen, M. A. (2022). The effects of buildings’ physical characteristics on urban network centrality. Ain Shams Engineering Journal, 13(6), 101765.
Arafah, F., Santrum, I., Sunaryo, D., & Purwanto, H. (2024). ANALISA PERBANDINGAN METODE
CELLULAR AUTOMATA ANN DAN MARKOV UNTUK PREDIKSI TUTUPAN LAHAN DI KOTA BLITAR. Jurnal Tekno Global, 13, 58–65. https://doi.org/10.36982/jtg.v13i02.4853
Atef, I., Ahmed, W., Abdel-Maguid, R., Baraka, M., Darwish, W., Senousi, A., & atef, eslam. (2023). LAND USE AND LAND COVER SIMULATION BASED ON INTEGRATION OF ARTIFICIAL NEURAL NETWORKS WITH CELLULAR AUTOMATA-MARKOV CHAIN MODELS APPLIED TO EL-FAYOUM GOVERNORATE. X, 7. https://doi.org/10.5194/isprs-annals-X-1-W1-2023-771-2023
Bahrudin, M. L., Wagistina, S., Masruroh, H., & Astuti, I. S. (2023). Klasterisasi Spasial dan Penggunaan Lahan Wilayah Peri Urban Lowokwaru Kota Malang. Jurnal Integrasi Dan Harmoni Inovatif Ilmu-Ilmu Sosial, 3(10), 1111–1127. https://doi.org/10.17977/um063v3i10p1111-1127
Belihu, M., Tekleab, S., Abate, B., & Bewket, W. (2020). Hydrologic response to land use land cover change in the Upper Gidabo Watershed, Rift Valley Lakes Basin, Ethiopia. HydroResearch, 3, 85–94. https://doi.org/10.1016/j.hydres.2020.07.001
Blissag, B., & Kessar, C. (2024). Spatiotemporal Influence of Lulc Change and Their Effect on the Hydrological Response of a Semi-Arid Watershed. https://doi.org/10.1109/M2GARSS57310.2024.10537487
Boakye, D., Ahinful, G. sam, & Nsor-Ambala, R. (2020). Sustainable Environmental Practices and Financial Performance Relationships. Are they moderated by Cash Resources? Evidence from Alternative Investment Market in the UK. International Journal of Technology and Management Research, 5. https://doi.org/10.47127/ijtmr.v5i1.80
BPS. (2024). Kota Malang Dalam Angka 2024.
Cao, Y., Zhang, X., Fu, Y., Lu, Z., & Shen, X. (2020). Urban spatial growth modeling using logistic regression and cellular automata: A case study of Hangzhou. Ecological Indicators, 113(December 2019), 106200. https://doi.org/10.1016/j.ecolind.2020.106200
Gasparovic, M., & Jogun, T. (2018). The effect of fusing Sentinel-2 bands on land-cover classification. International Journal of Remote Sensing, 39, 822–841. https://doi.org/10.1080/01431161.2017.1392640
Lewar, P. N., Sari, Y. I., & Kurniawati, D. (2022). Dinamika Sosial : Jurnal Pendidikan Ilmu Pengetahuan Sosial Dinamika Sosial : Jurnal Pendidikan Ilmu Pengetahuan Sosial bumi . Setiap wilayah memiliki penggunaan lahan yang beragam tergantung dari jenis disesuaikan dengan kebutuhan baik bersifat permanen. 1(2), 149–160.
Lion, J. C. L. (2022). Penentuan Faktor Transformasi Spasial Di Wilayah Peri Urban Kota Malang Provinsi Jawa Timur (Studi Kasus: Kecamatan Kedungkandang).
Long, H., Zhang, Y., Ma, L., & Tu, S. (2021). Land use transitions: Progress, challenges and prospects. Land, 10(9), 903.
Lukas, P., Melesse, A. M., & Kenea, T. T. (2023). Prediction of Future Land Use/Land Cover Changes Using a Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia. Remote Sensing, 15(4), 1148. https://doi.org/10.3390/rs15041148
Mahendra, Y. I., & Pradoto, W. (2016). Transformasi Spasial di Kawasan Peri Urban Kota Malang. Jurnal Pembangunan Wilayah & Kota, 12(1), 112. https://doi.org/10.14710/pwk.v12i1.11462
Nadeem, F., Seyedalhosseini, S., Hanaee, T., & Sarvari, H. (2023). Identifying the Principles of
Informal Settlements Physical Development as a Context-based Pattern Using the Meta-Synthesis Method 1. 7, 91–110. https://doi.org/10.22124/UPK.2023.22806.1801
Nagappan, E., & Egambaram, S. (2023). Quantum Geographic Information System (QGIS) for Mapping of Study Areas.
Niu, X., Liao, F., Liu, Z., & Wu, G. (2022). Spatial–temporal characteristics and driving mechanisms of land–use transition from the perspective of urban–rural transformation development: A case study of the Yangtze River Delta. Land, 11(5), 631.
Nofirman, Ahmada, N., & Fauzan, T. (2024). Integration of Geographic Information Systems and Spatial Data Analysis in Location Decision Making for Manufacturing Industries. International Journal Software Engineering and Computer Science (IJSECS), 4, 196–209. https://doi.org/10.35870/ijsecs.v4i1.2027
Nugroho, F. I., Setyono, D. A., & Kurniawan, E. B. (2022). Identifikasi Kriteria Layak Huni Permukiman Di Kota Malang. Tata Kota Dan Daerah, 14(1), 1–8. https://doi.org/10.21776/ub.takoda.2022.014.01.1
Osman, M. A. A., Abdel-Rahman, E. M., Onono, J. O., Olaka, L. A., Elhag, M. M., Adan, M., & Tonnang, H. E. Z. (2023). Mapping, intensities and future prediction of land use/land cover dynamics using google earth engine and CA- artificial neural network model. PLOS ONE, 18(7), e0288694. https://doi.org/10.1371/journal.pone.0288694
Pan, C., Wu, S., Li, E., Li, H., & Liu, X. (2023). Identification of urban functional zones in Macau Peninsula based on POI data and remote information sensors technology for sustainable development. Physics and Chemistry of the Earth, Parts a/b/C, 131, 103447.
Susetyo, J. A., Rachmadian, R. H., Lestari, H. D., Cendikia, R., Marshanda, U. F., & Bachri, S. (2024). Evaluasi dan Penilaian Kepadatan Bangunan Perkotaan menggunakan Analisis Spasial NDBI di Kota Malang Jawa Timur. 25(2), 320–331.
Wekesa, B. W., Steyn, G. S., & Otieno, F. A. O. (Fred). (2011). A review of physical and socio-economic characteristics and intervention approaches of informal settlements. Habitat International, 35(2), 238–245. https://doi.org/10.1016/j.habitatint.2010.09.006
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