Applications of Machine Learning and Remote Sensing in Soil and Water Conservation
The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, re...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/11/11/183 |
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| _version_ | 1846153445165236224 |
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| author | Ye Inn Kim Woo Hyeon Park Yongchul Shin Jin-Woo Park Bernie Engel Young-Jo Yun Won Seok Jang |
| author_facet | Ye Inn Kim Woo Hyeon Park Yongchul Shin Jin-Woo Park Bernie Engel Young-Jo Yun Won Seok Jang |
| author_sort | Ye Inn Kim |
| collection | DOAJ |
| description | The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on the research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review of the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applications in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the fields of soil and water conservation. |
| format | Article |
| id | doaj-art-80bdd4ffd59d431fb69c26fec800b01b |
| institution | Kabale University |
| issn | 2306-5338 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj-art-80bdd4ffd59d431fb69c26fec800b01b2024-11-26T18:06:16ZengMDPI AGHydrology2306-53382024-10-01111118310.3390/hydrology11110183Applications of Machine Learning and Remote Sensing in Soil and Water ConservationYe Inn Kim0Woo Hyeon Park1Yongchul Shin2Jin-Woo Park3Bernie Engel4Young-Jo Yun5Won Seok Jang6Department of Landscape Architecture, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Landscape Architecture, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Agricultural Civil Engineering, Kyungpook National University, Daegu 41566, Republic of KoreaDivision of Forest Sciences, College of Forest and Environmental Sciences, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USADepartment of Ecological Landscape Architecture Design, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Ecological Landscape Architecture Design, Kangwon National University, Chuncheon 24341, Republic of KoreaThe application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on the research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review of the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applications in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the fields of soil and water conservation.https://www.mdpi.com/2306-5338/11/11/183machine learningremote sensingsoil conservationwater conservationenvironmental analysisdata-driven decision-making |
| spellingShingle | Ye Inn Kim Woo Hyeon Park Yongchul Shin Jin-Woo Park Bernie Engel Young-Jo Yun Won Seok Jang Applications of Machine Learning and Remote Sensing in Soil and Water Conservation Hydrology machine learning remote sensing soil conservation water conservation environmental analysis data-driven decision-making |
| title | Applications of Machine Learning and Remote Sensing in Soil and Water Conservation |
| title_full | Applications of Machine Learning and Remote Sensing in Soil and Water Conservation |
| title_fullStr | Applications of Machine Learning and Remote Sensing in Soil and Water Conservation |
| title_full_unstemmed | Applications of Machine Learning and Remote Sensing in Soil and Water Conservation |
| title_short | Applications of Machine Learning and Remote Sensing in Soil and Water Conservation |
| title_sort | applications of machine learning and remote sensing in soil and water conservation |
| topic | machine learning remote sensing soil conservation water conservation environmental analysis data-driven decision-making |
| url | https://www.mdpi.com/2306-5338/11/11/183 |
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