Potential erosion and sedimentation based on land use change by using cellular automata-artificial neural network
Erosion and sedimentation are global environmental threats that cause land degradation, reduced agricultural productivity and increased flooding risks, leading to the loss of 75 billion tons of fertile soil annually. This study employs advanced remote sensing and machine learning techniques to analy...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2025.2461058 |
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| author | Aditya Nugraha Putra Istika Nita Kurniawan Sigit Wicaksono Novandi Rizky Prasetya Michelle Talisia Sugiarto Fahmi Hidayat Zainal Alim Sugik Edy Sartono Pandham Giri Sasangka Tiar Ranu Kusuma Bilawal Abbasi Alena Gessert Mohd Hasmadi Ismail Watit Khokthong |
| author_facet | Aditya Nugraha Putra Istika Nita Kurniawan Sigit Wicaksono Novandi Rizky Prasetya Michelle Talisia Sugiarto Fahmi Hidayat Zainal Alim Sugik Edy Sartono Pandham Giri Sasangka Tiar Ranu Kusuma Bilawal Abbasi Alena Gessert Mohd Hasmadi Ismail Watit Khokthong |
| author_sort | Aditya Nugraha Putra |
| collection | DOAJ |
| description | Erosion and sedimentation are global environmental threats that cause land degradation, reduced agricultural productivity and increased flooding risks, leading to the loss of 75 billion tons of fertile soil annually. This study employs advanced remote sensing and machine learning techniques to analyze land use changes and their impacts on erosion and sedimentation at the sub-watershed level. Sentinel-2A images from multiple years were used and classified into 17 distinct land use classes through a supervised classification technique. The baseline land use data served as the foundation for future predictions, with a business-as-usual scenario modelled using cellular automata and artificial neural networks (CA-ANN). Land use factors were incorporated into the USLE model to generate an erosion map and to perform sediment retention analysis using the InVEST model. By 2025, over 35% of the total area is projected to experience significant deforestation, with forested areas being converted into orchards, shrubs, bare land, agricultural dry land and settlements. In 2022, forest area transformation resulted in a 25% increase in erosion and an 18% rise in sedimentation, with these figures expected to climb further by 2025. Our study recommends the CA-ANN model as a tool to predict land use changes and guide interventions, ensuring sustainable management of sub-watershed areas. |
| format | Article |
| id | doaj-art-ed25db286f6f4f1ca75e9bf97c10b66c |
| institution | DOAJ |
| issn | 1947-5705 1947-5713 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj-art-ed25db286f6f4f1ca75e9bf97c10b66c2025-08-20T03:05:42ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132025-12-0116110.1080/19475705.2025.2461058Potential erosion and sedimentation based on land use change by using cellular automata-artificial neural networkAditya Nugraha Putra0Istika Nita1Kurniawan Sigit Wicaksono2Novandi Rizky Prasetya3Michelle Talisia Sugiarto4Fahmi Hidayat5Zainal Alim6Sugik Edy Sartono7Pandham Giri Sasangka8Tiar Ranu Kusuma9Bilawal Abbasi10Alena Gessert11Mohd Hasmadi Ismail12Watit Khokthong13Department of Soil Science, Faculty of Agriculture, Brawijaya University, Malang, IndonesiaDepartment of Soil Science, Faculty of Agriculture, Brawijaya University, Malang, IndonesiaDepartment of Soil Science, Faculty of Agriculture, Brawijaya University, Malang, IndonesiaDepartment of Soil Science, Faculty of Agriculture, Brawijaya University, Malang, IndonesiaDepartment of Soil Science, Faculty of Agriculture, Brawijaya University, Malang, IndonesiaPerum Jasa Tirta I Corporation, Malang, IndonesiaPerum Jasa Tirta I Corporation, Malang, IndonesiaPerum Jasa Tirta I Corporation, Malang, IndonesiaPerum Jasa Tirta I Corporation, Malang, IndonesiaPerum Jasa Tirta I Corporation, Malang, IndonesiaInstitute of Agro-Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, ChinaInstitute of Geography, Faculty of Sciences, Pavol Jozef Šafárik University, Košice, SlovakiaDepartment of Forest Production, Faculty of Forestry and Environment, Universiti Putra Malaysia, Serdang, Selangor, MalaysiaDepartment of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, ThailandErosion and sedimentation are global environmental threats that cause land degradation, reduced agricultural productivity and increased flooding risks, leading to the loss of 75 billion tons of fertile soil annually. This study employs advanced remote sensing and machine learning techniques to analyze land use changes and their impacts on erosion and sedimentation at the sub-watershed level. Sentinel-2A images from multiple years were used and classified into 17 distinct land use classes through a supervised classification technique. The baseline land use data served as the foundation for future predictions, with a business-as-usual scenario modelled using cellular automata and artificial neural networks (CA-ANN). Land use factors were incorporated into the USLE model to generate an erosion map and to perform sediment retention analysis using the InVEST model. By 2025, over 35% of the total area is projected to experience significant deforestation, with forested areas being converted into orchards, shrubs, bare land, agricultural dry land and settlements. In 2022, forest area transformation resulted in a 25% increase in erosion and an 18% rise in sedimentation, with these figures expected to climb further by 2025. Our study recommends the CA-ANN model as a tool to predict land use changes and guide interventions, ensuring sustainable management of sub-watershed areas.https://www.tandfonline.com/doi/10.1080/19475705.2025.2461058Hydrometeorologymachine learningremote sensingsoil degradationsoil conservation |
| spellingShingle | Aditya Nugraha Putra Istika Nita Kurniawan Sigit Wicaksono Novandi Rizky Prasetya Michelle Talisia Sugiarto Fahmi Hidayat Zainal Alim Sugik Edy Sartono Pandham Giri Sasangka Tiar Ranu Kusuma Bilawal Abbasi Alena Gessert Mohd Hasmadi Ismail Watit Khokthong Potential erosion and sedimentation based on land use change by using cellular automata-artificial neural network Geomatics, Natural Hazards & Risk Hydrometeorology machine learning remote sensing soil degradation soil conservation |
| title | Potential erosion and sedimentation based on land use change by using cellular automata-artificial neural network |
| title_full | Potential erosion and sedimentation based on land use change by using cellular automata-artificial neural network |
| title_fullStr | Potential erosion and sedimentation based on land use change by using cellular automata-artificial neural network |
| title_full_unstemmed | Potential erosion and sedimentation based on land use change by using cellular automata-artificial neural network |
| title_short | Potential erosion and sedimentation based on land use change by using cellular automata-artificial neural network |
| title_sort | potential erosion and sedimentation based on land use change by using cellular automata artificial neural network |
| topic | Hydrometeorology machine learning remote sensing soil degradation soil conservation |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2025.2461058 |
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