Integrating spatial database for predicting soil salinity using machine learning methods in Syrdarya Province, Uzbekistan
Soil salinization of irrigated lands is a global problem in providing the necessary food and feed to meet the needs of a growing world population. Salinization in arid and semiarid areas can occur when the water table is three and more meters above the soil surface. Nowadays, innovative technologies...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/08/e3sconf_afe2023_01011.pdf |
| _version_ | 1811157545092382720 |
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| author | Omonov Aziz Kato Tasuku Fitriyah Atiqotun Shirokova Yulia Suvanov Anvar Ismoilov Zukhriddin |
| author_facet | Omonov Aziz Kato Tasuku Fitriyah Atiqotun Shirokova Yulia Suvanov Anvar Ismoilov Zukhriddin |
| author_sort | Omonov Aziz |
| collection | DOAJ |
| description | Soil salinization of irrigated lands is a global problem in providing the necessary food and feed to meet the needs of a growing world population. Salinization in arid and semiarid areas can occur when the water table is three and more meters above the soil surface. Nowadays, innovative technologies are widely implemented in agriculture to increase yields and monitor changes in any area timely. Advanced technologies such as remote sensing (R.S.) data have become an economically efficient tool for assessing, detecting, mapping, and monitoring saline areas. This study aims to develop a spatial database for evaluating salinization using R.S. and GIS. This research employs various soil salinity indices based on Landsat 8 OLI images and other related geospatial datasets of the study areas. It aims to predict soil salinity using four machine learning methods (Gaussian Mixture Model (GMM), Random Forest (R.F.), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN)). Results showed that R.F. is the most suitable for predicting the soil salinity in the study area with 93 percent overall accuracy. This research contributes to improving the quality of monitoring and improvement of the state of irrigated lands. Also, it develops a preliminary step toward decision-making tools for agricultural policies, such as managing saline areas related to crop production. |
| first_indexed | 2024-04-10T05:09:00Z |
| format | Article |
| id | doaj.art-0ec568750dc34aaeb890df7324b3a5f9 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-10T05:09:00Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-0ec568750dc34aaeb890df7324b3a5f92023-03-09T11:17:21ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013710101110.1051/e3sconf/202337101011e3sconf_afe2023_01011Integrating spatial database for predicting soil salinity using machine learning methods in Syrdarya Province, UzbekistanOmonov Aziz0Kato Tasuku1Fitriyah Atiqotun2Shirokova Yulia3Suvanov Anvar4Ismoilov Zukhriddin5Tokyo University of Agriculture and TechnologyTokyo University of Agriculture and TechnologyResearch Center for Limnology and Water Resources, National Research and Innovation Agency (BRIN)Scientific Research Institute of Irrigation and Water Problems (RIIWP) of the Republic of UzbekistanNational Research University, Tashkent Institute of Irrigation and Agricultural Mechanization EngineersTokyo University of Agriculture and TechnologySoil salinization of irrigated lands is a global problem in providing the necessary food and feed to meet the needs of a growing world population. Salinization in arid and semiarid areas can occur when the water table is three and more meters above the soil surface. Nowadays, innovative technologies are widely implemented in agriculture to increase yields and monitor changes in any area timely. Advanced technologies such as remote sensing (R.S.) data have become an economically efficient tool for assessing, detecting, mapping, and monitoring saline areas. This study aims to develop a spatial database for evaluating salinization using R.S. and GIS. This research employs various soil salinity indices based on Landsat 8 OLI images and other related geospatial datasets of the study areas. It aims to predict soil salinity using four machine learning methods (Gaussian Mixture Model (GMM), Random Forest (R.F.), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN)). Results showed that R.F. is the most suitable for predicting the soil salinity in the study area with 93 percent overall accuracy. This research contributes to improving the quality of monitoring and improvement of the state of irrigated lands. Also, it develops a preliminary step toward decision-making tools for agricultural policies, such as managing saline areas related to crop production.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/08/e3sconf_afe2023_01011.pdf |
| spellingShingle | Omonov Aziz Kato Tasuku Fitriyah Atiqotun Shirokova Yulia Suvanov Anvar Ismoilov Zukhriddin Integrating spatial database for predicting soil salinity using machine learning methods in Syrdarya Province, Uzbekistan E3S Web of Conferences |
| title | Integrating spatial database for predicting soil salinity using machine learning methods in Syrdarya Province, Uzbekistan |
| title_full | Integrating spatial database for predicting soil salinity using machine learning methods in Syrdarya Province, Uzbekistan |
| title_fullStr | Integrating spatial database for predicting soil salinity using machine learning methods in Syrdarya Province, Uzbekistan |
| title_full_unstemmed | Integrating spatial database for predicting soil salinity using machine learning methods in Syrdarya Province, Uzbekistan |
| title_short | Integrating spatial database for predicting soil salinity using machine learning methods in Syrdarya Province, Uzbekistan |
| title_sort | integrating spatial database for predicting soil salinity using machine learning methods in syrdarya province uzbekistan |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/08/e3sconf_afe2023_01011.pdf |
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