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Artificial intelligence in land use prediction modeling: a review

IAES International Journal of Artificial Intelligence · Vol. 13 No. 3 (2024) · hlm. 2514–2523

DOI
10.11591/ijai.v13.i3.pp2514-2523
ISSN
2252-8938, 2089-4872
Terbit
2024-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To anticipate this, it is necessary to select the appropriate image resolution and interpretation method in accordance with digital data segmentation. It is also recommended to use spatial regression methods to determine the driving forces that affect land use. Hybrid models such as multilayer perceptron neural network Markov chain (MLPNN-MC), random forest algorithm (RFA), and cellular automata (CA)-Markov chain (MC) are recommended for modelling. The selection of a model should be based on the data's characteristics and tested for accuracy. The use of AI for land use prediction modelling is expected to provide accurate predictions that can be used as a basis for land use policy.

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29 penulis · 82 tautan kepenulisan bersama · 24 lintas organisasi

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  • Politeknik Agraria STPN
  • Universitas Gadjah Mada
  • Uganda Christian University
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
M. Nazir SalimPoliteknik Agraria STPN5
Mujiati MujiatiPoliteknik Agraria STPN3
Nurhadi NurhadiUniversitas Gadjah Mada3
Andrew MulabbiUganda Christian University3
Rinanda Amdalista Prastia LarasatiUniversitas Gadjah Mada3
Rindhang Bima YudhaPoliteknik Agraria STPN3
Ahnad YuniantoUniversitas Gadjah Mada1
Ageni Trifi KasihUniversitas Gadjah Mada1
Dwi Wulan PujiriyaniPoliteknik Agraria STPN1
Dian Aries MujiburohmanPoliteknik Agraria STPN1
Rohmat JunartoPoliteknik Agraria STPN1
Dwi Wulan Titik AndariPoliteknik Agraria STPN1
Mustofa MustofaUniversitas Gadjah Mada1
Akhmad Akbar SusamtoUniversitas Gadjah Mada1
Baskara SuprojoPoliteknik Agraria STPN1
Luthfi Adela SaraswatiPoliteknik Agraria STPN1
Diffa Alifia NabilaPoliteknik Agraria STPN1
Ika Noor Muslihah MUniversitas Gadjah Mada1
Candra Sari Djati KartikaUniversitas Gadjah Mada1
Yuli Ardianto WibowoPoliteknik Agraria STPN1
Ahmad Haris HadiPoliteknik Agraria STPN1
Fajar Buyung PermadiPoliteknik Agraria STPN1
Tjahyo Nugroho AdjiUniversitas Gadjah Mada1
Kevin Philips BarakatiUniversitas Gadjah Mada1
Tri Ida WijayantiPoliteknik Agraria STPN1
Lutfia Nursetya FuadinaPoliteknik Agraria STPN1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Artificial intelligence in land use prediction modeling: a review</dc:title>
	<dc:creator>Utami, Westi</dc:creator>
	<dc:creator>Sugiyanto, Catur</dc:creator>
	<dc:creator>Rahardjo, Noorhadi</dc:creator>
	<dc:subject xml:lang="en-US">Geographic Information System; Remote Sensing; Modelling; Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">Deep learning; Driving forces; Land use prediction; Machine learning; Spatial modelling</dc:subject>
	<dc:description xml:lang="en-US">This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To anticipate this, it is necessary to select the appropriate image resolution and interpretation method in accordance with digital data segmentation. It is also recommended to use spatial regression methods to determine the driving forces that affect land use. Hybrid models such as multilayer perceptron neural network Markov chain (MLPNN-MC), random forest algorithm (RFA), and cellular automata (CA)-Markov chain (MC) are recommended for modelling. The selection of a model should be based on the data's characteristics and tested for accuracy. The use of AI for land use prediction modelling is expected to provide accurate predictions that can be used as a basis for land use policy.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Universitas Gadjah Mada</dc:contributor>
	<dc:date>2024-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24476</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i3.pp2514-2523</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 3: September 2024; 2514-2523</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24476/14057</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>