Prediction and Modelling of Land Use Change in Pesawaran District Lampung Using ANN and Cellular Automata

Authors

  • Irma lusi Nugraheni

    Geography Department, Universitas Lampung, Bandar Lampung 35145, Indonesia
  • Mustofa Usman

    Math Department, Universitas Lampung, Bandar Lampung 35145, Indonesia
  • Sutarto Sutarto

    Medical Department, Universitas Lampung, Bandar Lampung 35145, Indonesia

DOI:

https://doi.org/10.30564/jees.v7i6.8934
Received: 3 March 2025 | Revised: 31 March 2025 | Accepted: 22 April 2025 | Published Online: 27 May 2025

Abstract

The simultaneous increase in development in Pesawaran Regency is closely correlated with the intense competition for land use. However, low policy implementation effectiveness has led to construction beyond designated spatial plan. The study used a quantitative survey using Landsat images in 2016, 2019, and 2022. The data analysis techniques used geographic information systems integrated with Artificial Neural Network (ANN) and Cellular Automata (CA) models. This study aims to predict land-use change in 2031, evaluate its alignment with spatial planning, and provide guidance for controlling land-use change. The results showed that there has been an increase in land use. In 2019, built-up land reached 7,069.65 Ha. The model shows its ability to predict land simulation and transformation, where it is predicted that built-up land in 2031 will experience an increase of up to 40.10%, so development and change cannot be avoided every year. This study also suggests that decision-makers and local governments should reconsider spatial planning strategies. This study shows that there have been many land use changes from 2016 to 2022. The model shows its ability to predict simulation and land transformation. When using the model, there are many changes in the land use area in 2031. This is due to wet agricultural land turning into built-up land by almost 70%. This study shows that road network influence land-use change. The cellular automata model managed to capture the complexity with simple rules. Predictions for future research should focus on conserving wetlands and primary forests.

Keywords:

Land Use Model; System Information Geography; Cellular Automata; Artificial Neural Network (ANN)

References

[1] Siraj, B., Moges, A., Dananto, M., 2023. Trend Analysis Past Dynamics and Future Prediction of Land use and Land Cover Change in Upper Wabe-Shebele River Basin. Heliyon. 9(9), e19128. DOI: https://doi.org/10.1016/j.heliyon.2023.e19128

[2] Juhadi, 2007. Land Use Patterns and Environmental Degradation in Hilly Areas. Journal of Geography Information Media for Development and Geography Profession.4(1), 11–24. DOI: https://doi.org/10.15294/jg.v4i1.108

[3] Chao, C., Yang, X., Jiang, S., et al., 2023. Mapping and Spatiotemporal Dynamics of Land-Use and Land-Cover Change Based on The Google Earth Engine Cloud Platform from Landsat Imagery: A Case Study of Zhoushan Island China. Heliyon. 9(9), e19654. DOI: https://doi.org/10.1016/j.heliyon.2023.e19654

[4] Law of the Republic of Indonesia No. 33 of 2007 concerning the Establishment of Pesawaran Regency in Lampung Province. https://.bpk.go.id/Details/39944/

[5] Pesawaran, 2018. Agricultural Development in Rural Areas. Available from: https://potensi.pesawaran kab.go.id/2018/07/18/pembangunan-pertanian-di-kabupaten-pesawaran/ (cited July 18th 2018).

[6] Emalia, Z., Farida, I., 2018. Identification of Growth Centers and Spatial Interaction in Lampung Province. Journal of Economics & Development Studies. 19(1), 61–74. DOI: https://doi.org/10.18196/jesp.19.1.4100

[7] BPS Pesawaran, 2020. Pesawaran Demography 2020. Available from: https://pesawarankab.bps.go.id/subject/12/ population (cited 20 January 20th 2020).

[8] Ridwan, F., Ardiansyah, M., Gandasasmita, K., 2017. Modeling Land Use/Cover Change Using Artificial Neural Network and Logistic Regression Approach (Case Study: Citarum Watershed, West Jawa). Land and Soil Bulletin. 1(1), 30–36.

[9] Sudarma, W., 2017. Conversion of Food Land in Pesawaran Regency, Lampung Province. Acculturation: Scientific Journal of Fisheries Agribusiness. 5(10). DOI: https://doi.org/10.35800/akulturasi.5.10.2017.17826

[10] Sari, S.W., Wirosoedarmo, R., 2014. Identification of Land Use on Stream Buffer Sumbergunung River in Batu City . Journal of Natural Resources and Environment. 1(2), 25–30.

[11] Fara, Z., Putu, A.G., 2018. Factors Affecting Urban Compactness Size in South Tangerang City. ITS Engineering Journal. 7(2), C88–C94. DOI: https://doi.org/10.12962/j23373539.v7i2.37019

[12] Kusniawati, I., Sawitri, S., 2020. Land Use Change Model Analysis Using Artificial Neural Network in Salatiga City. Jurnal Geodesi Undip. 9(1), 1–11.

[13] Muhammad, S.H., 2019. Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton. Sustainability. 11(11), 3024. DOI: https://doi.org/10.3390/su11113024

[14] Zhang, J., Liu, L., 2024. Neural Cellular Automata-based Land Use Changes Simulation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. XLVIII-1-2024, 843–848. DOI: https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-843-2024

[15] Ali, M.A., Roy, L.B., 2024. Muzaffarpur City Land Changes And Impact On Urban Runoff And Water Quality Of The River Burhi Gandak. International Journal of Environmental Science and Technology. 21(2), 2071–2082. DOI: https://doi.org/10.1007/s13762-023-05008-2

[16] Zhou, Z., Chen, Y., Wang, Z., et al., 2024. Integrating Cellular Automata With Long Short-Term Memory Neural Network To Simulate Urban Expansion Using Time-Series Data. International Journal of Applied Earth Observation and Geoinformation. 127, 103676. DOI: https://doi.org/10.1016/j.jag.2024.103676

[17] Wu, J., Luo, J., Zhang, H., et al., 2022. Projections Of Land Use Change And Habitat Quality Assessment By Coupling Climate Change And Development Patterns. Science of the Total Environment. 847, 157491. DOI: https://doi.org/10.1016/j.scitotenv.2022.157491

[18] Novianty, P.C., Efani, A., 2025. Analyzing Land Conversion Rate and Conversion Farmer Household Food Security in Bakalan Village, Pasuruan Regency. Journal of Science Education Research. 11(1), 415–424. DOI: https://doi.org/10.29303/jppipa.v11i1.7583

[19] Silambi, N., Ismail, N., Septarini, D.F., et al., 2023. Protection Of Agricultural Land In Realizing Sustainable Food Security. Proceedings of the 3rd International Conference on Environmental Ecology of Food Security; 14–16 February 2023; Palu, Indonesia. IOP Conference Series: Earth and Environmental Science. 1253, 012022. DOI: https://doi.org/10.1088/1755-1315/1253/1/012022

[20] Basse, R.M., Omrani, H., Charif, O., et al., 2014. Land Use Changes Modelling Using Advanced Methods: Cellular Automata And Artificial Neural Networks. The Spatial And Explicit Representation Of Land Cover Dynamics At The Cross-Border Region Scale. Applied Geography. 53, 160–171. DOI: https://doi.org/10.1016/j.apgeog.2014.06.016

[21] Anne, G., Shaamala, A., Obeidat, R., et al., 2020. Improving Land-Use Change Modeling By Integrating ANN With Cellular Automata-Markov Chain Model. Heliyon. 6, 1–18. DOI: https://doi.org/10.1016/j.heliyon.2020.e05092

[22] Girsang, M.A.G., Banurea, D.M., Lestari, P., et al., 2025. Dynamics Prediction Of Land Use Changes Using Cellular Automata And Artificial Neural Network Modeling. Global Journal of Environmental Science and Management. 11(2), 427–442. DOI: https://doi.org/10.22034/gjesm.2025.02.04

[23] Ketema, D., Bekele, A., 2024. Modeling the Land use Land cover changes and its impacts on agricultural land area by using GIS and RS at Fitche town, Central Ethiopia. Bule Hora University: Oromiya, Ethiopia. DOI: https://doi.org/10.21203/rs.3.rs-5201138/v1

[24] Hidayat, A., Mustofa, U., 2024. Land Use Change by Cellular Automata (CA) – Markov Method. Proceedings of the Borneo International Conference (BICAME 2024): Symposium on Digital Innovation, Sustainable Design and Planning (DSP); 18–19 September 2024; Balikpapan, Indonesia (Offline). pp. 45–58. DOI: https://doi.org/10.2991/978-2-38476-329-0

[25] Khalid, W., Shamim, S.K., Ahmad, A., 2024. Synergistic Approach for Land Use and Land Cover Dynamics Prediction in Uttarakhand using Cellular Automata and Artificial Neural Network. DOI: https://doi.org/10.1016/j.geomat.2024.100017

[26] Prosperous, N., John, M., 2025. Modelling Bush Encroachment Dynamics Using Intensity Analysis And The Cellular Automata Model. Environmental Monitoring And Assessment. DOI: https://doi.org/10.1007/s10661-025-13808-x

[27] Tamal, K.S., Pal, S., Sarkar, R., 2021. Prediction of Wetland Area and Depth Using Linear Regression Model and Artificial Neural Network Based Cellular Automata. Ecological Informatics. 62, 101272. DOI: https://doi.org/10.1016/j.ecoinf.2021.101272

[28] Labisa, W., 2021. Accuracy Assessment of Land Use/Land Cover Classification Data from Sentinel-2 & ASTER Imagery Interpretation using Unsupervised Classification Algorithm. Proceeding International Conference on Science and Engineering. 4, 229–234.

[29] Rizwan, M., Wenyin, Z., Zaheer, A., et al., 2022. Spatiotemporal Change Analysis and Prediction of Future Land Use and Land Cover Changes Using QGIS MOLUSCE Plugin and Remote Sensing Big Data: A Case Study of Linyi, China. Land. 11(3), 419. DOI: https://doi.org/10.3390/land11030419.

[30] Abbas, T., Shoaib, M., Albano, R., et al., 2025. Artificial-Intelligence-Based Investigation on Land Use and Land Cover (LULC) Changes in Response to Population Growth in South Punjab, Pakistan. Land. 14(1), 154. DOI: https://doi.org/10.3390/land14010154

[31] Rai, R., Zhang, Y., Paudel, B., et al., 2017. A Synthesis of Studies On Land Use And Land Cover Dynamics During 1930–2015 In Bangladesh. Sustainability. 9(10), 1–20. DOI: https://doi.org/10.3390/su9101866

[32] Dwarakish, G.S., Ganasri, B.P., 2015. Impact of Land Use Change on Hydrological Systems: A Review of Current Modeling Approaches. Cogent Geoscience. 1(1), 1115691. DOI: https://doi.org/10.1080/23312041.2015.1115691

[33] Nicolas, D., Rounsevell, M., Bogaert, P., 2007. Spatial Analysis and Modelling of Land Use Distributions in Belgium. Computers, Environment and Urban Systems. 31(2), 188–205. DOI: https://doi.org/10.1016/j.compenvurbsys.2006.06.004

[34] Bandit, M., 2018. Analysis of Land Use and Land Cover Changes and the driving forces: A case study in Kaysone Phomvihan District, Laos. University of Porto: Porto, Portugal.

[35] Wu, F., Mo, C., Dai, X., 2022. Analysis of the Driving Force of Land Use Change Based on Geographic Detection and Simulation of Future Land Use Scenarios. Sustainability. 14(9). DOI: https://doi.org/10.3390/su14095254

[36] Mustafidah, H., Giarto, W.G.P., 2021. Web Based Application for Data Analysis Using Pearson Bivariate Correlation. Sainteks. 18(1), 39. DOI: https://doi.org/10.30595/sainteks.v18i1.10564

[37] Ayu, H.M.S., Subiyanto, S., Firdaus, H.S., 2021. Land Use Change Prediction Analysis Using Artificial Neural Network Approach and Logistic Regression in Balikpapan City. Geodesi Journal UNDIP. 10(2), 88–97. DOI: https://doi.org/10.14710/jgundip.2021.30637

[38] Sourn, T., Pok, S., Chou, P., et al., 2021. Evaluation of Land Use and Land Cover Change and its Drivers in Battambang province, Cambodia from 1998 to 2018. Sustainability. 13(20), 11170. DOI: https://doi.org/10.3390/su132011170

[39] Swapan, T., Eibek, K.U., Akhter, S., et al., 2021. Modeling Fragmentation Probability of Land-Use and Land-Cover Using the Bagging, Random Forest and Random Subspace in The Teesta River Basin Bangladesh. Ecological Indicators. 126, 107612. DOI: https://doi.org/10.1016/j.ecolind.2021.107612

[40] Epstein, P.R., 2004. Climate Change and Public Health: Emerging Infectious Diseases. Encyclopedia of Energy. 1, 381–392. DOI: https://doi.org/10.1016/b0-12-176480-x/00432-0

[41] Arumdani, I.S., Berlian, A.I., 2021. Implementation of Environmental Health Services (Sanitation Clinic) in Public Health Centers Indonesia. Precipitation Journal: Communication Media and Environmental Engineering Development. 18(3), 536–545. DOI: https://doi.org/10.14710/presipitasi.v18i3.536-545

[42] Rizkyanto, I., 2020. Prediction of Built-up Land Development in Pekalongan City Using Cellular Automata Model Using Geographic Information System. Geo-Image. 9(1), 1–7.

[43] Chu, W.Y., 2007. Spatiotemporal Changes of Landscape Pattern in Response to Urbanization. Landscape and Urban Planning. 81(4), 341–353. DOI: https://doi.org/10.1016/j.landurbplan.2007.01.009

[44] Brock, W., Xepapadeas, A., 2025. Land Use, Climate Change And The Emergence of Infectous Diseases: A Synthesis. Environmental And Resource Economics. 88, 795–854. DOI: https://doi.org/10.1007/s10640-024-00949-9

[45] Shah, H.A., Huxley, P., Elmes, J., et al., 2019. Agricultural Land-Uses Consistently Exacerbate Infectious Disease Risks in Southeast Asia. Nature Communications. 10(1). DOI: https://doi.org/10.1038/s41467-019-12333-z

[46] Ortiz, D., Piche-ovares, M., Romero-vega, L.M., et al., 2022. The Impact of Deforestation, Urbanization, And Changing Land Use Patterns On The Ecology Of Mosquito And Tick-Borne Diseases In Central America. Insects. 13(1), 20. DOI: https://doi.org/10.3390/insects13010020

[47] Muliansyah, T.B., 2016. Analysis of Dengue Fever Distribution Patterns on Land Use with a Spatial Approach in Banggai Regency, Central Sulawesi Province in 2011–2013. Journal of Information Systems for Public Health. 1(2), 46–54. DOI: https://doi.org/10.22146/jisph.5961

[48] Verburg, P.H., Soepboer, W., Veldkamp, A., et al., 2002. Modeling the spatial dynamics of regional land use: The CLUE-S model. Environmental Management. 30(3), 391–405. DOI: https://doi.org/10.1007/s00267-002-2630-x

[49] Samuel, Y.A., Awuah, E., Oduro-Kwarteng, S., et al., 2023. Environmental Management and Sanitation as a Malaria Vector Control Strategy: A Qualitative Cross-Sectional Study Among Stakeholders, Sunyani Municipality, Ghana. Environmental Health Insights. 17. DOI: https://doi.org/10.1177/11786302221146890

[50] Gerald, F., Cofie, O., 2011. Dynamics of Land-Use And Land-Cover Change In Freetown, Sierra Leone And Its Effects On Urban And Peri-Urban Agriculture - A Remote Sensing Approach. International Journal of Remote Sensing. 32(4), 1017–1037. DOI: https://doi.org/10.1080/01431160903505302

[51] Widyawati, B., Suparman, B.C., 2023. Conversion of Agricultural Land Affects the Environment and Food Security in Indonesia. Pro Law Journal. 12(3), 1002–1018.

[52] Gandharum, L., Hartono, D.M., Sadmono, H., et al., Agricultural Land Change And Its Farmers' Perception In The North Coast Region Of West Java Province, Indonesia. IOP Conference Series: Earth and Environmental Science. 1462(1), 012027. DOI: https://doi.org/10.1088/1755-1315/1462/1/012027

[53] Amelia, R.N., Halim, R., Lanita, U., 2021. Factors Related to Ownership of Healthy Toilets in Sungai Itik Village, Sadu District, East Tanjung Jabung Regency in 2021. Electronic Journal of Science, Environment and Health Disease. 2(1), 52–62. DOI: https://doi.org/10.22437/esehad.v2i1.13575

[54] Rokhmah, D., Ma'rufi, I., 2022. Study of Nutrition Food Access to Family With Stunting Toddlers in Stunting Countermeasures System in Lumajang Indonesia. Amerta Nutrition. 6(1SP). DOI: https://doi.org/10.20473/amnt.v6i1sp.2022.32-37

[55] Patricia, E., 2025. Effective Land Management-Based Agricultural Management. International Journal of Social and Social Research Innovation. 1(1), 14–20.

[56] Hernández, R.C., Camerin, F., 2024. The Application of Ecosystem Assessments in Land Use Planning: A Case Study for Supporting Decisions Toward Ecosystem Protection. Futures. 161, 103399. DOI: https://doi.org/10.1016/j.futures.2024.103399

[57] Wang, D., Wang, M., Zheng, W., et al., 2025. A Multi-Level Spatial Assessment Framework For Identifying Land Use Conflict Zones. Land Use Policy. 148, 107382. DOI: https://doi.org/10.1016/j.landusepol.2024.107382

[58] Kundu, S., Kumar, V., Singh, S., 2024. Managing Sustainable Development by Land Cover Prediction Accuracy using Redefined State Transition Matrices in Cellular Automata Markov Chains. Journal of Himalayan Ecology and Sustainable Development. 19.

[59] Li, M., Li, J., Haq, S.U., et al., 2024. Agriculture Land Use Transformation: A Threat To Sustainable Food Production Systems, Rural Food Security, And Farmer Well-Being? Plos one Journal. 19(1), 1–20. DOI: https://doi.org/10.1371/journal.pone.0296332

[60] Rondhi, M., Pratiwi, P.A., Handini, V.T., et al., 2018. Agricultural Land Conversion, Land Economic Value, And Sustainable Agriculture: A Case Study In East Java, Indonesia. Land. 7(4), 1–16. DOI: https://doi.org/10.3390/land7040148

[61] Tahir, Z., Haseeb, M., Mahmood, S.A., et al., 2025. Predicting Land Use And Land Cover Changes For Sustainable Land Management Using CA-Markov Modelling And GIS Techniques. Scientific Reports. 15(1), 3271. DOI: https://doi.org/10.1038/s41598-025-87796-w

[62] Briassoulis, 2019. Analysis of Land Use Change: Theoretical and Modeling. Regional Research Institute.

[63] Reid, H., Scorgie, S., Muller, H., 2018. Ecosystem-Based Approaches To Adaptation: Strengthening The Evidence And Informing Policy. Research Results From The Mountain Eba Project, Nepal. International Institute for Environment and Development (IEED): London, UK. Available from: https://www.iied.org/17622iie

Downloads

How to Cite

Nugraheni, I. lusi, Mustofa Usman, & Sutarto Sutarto. (2025). Prediction and Modelling of Land Use Change in Pesawaran District Lampung Using ANN and Cellular Automata. Journal of Environmental & Earth Sciences, 7(6), 46–62. https://doi.org/10.30564/jees.v7i6.8934

Issue

Vol. 7 , Iss. 6 (June 2025): In Progress

Article Type

Article

License

Copyright © 2025 Irma lusi Nugraheni, Mustofa Usman, Sutarto Sutarto

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC