Application of the GMDH Neural Network in Monitoring Soil Salinity of Saveh Plain using Spectral and Topographic Indicators
In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the...
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| Format: | Article |
| Language: | fas |
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Isfahan University of Technology
2023-03-01
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| Series: | علوم آب و خاک |
| Subjects: | |
| Online Access: | http://jstnar.iut.ac.ir/article-1-4252-en.html |
| _version_ | 1797845537619181568 |
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| author | V. Habibi Arbatani M. Akbari Z. Moghaddam A.M. Bayat |
| author_facet | V. Habibi Arbatani M. Akbari Z. Moghaddam A.M. Bayat |
| author_sort | V. Habibi Arbatani |
| collection | DOAJ |
| description | In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the form of topographic and spectral categories. Land area parameters such as the Topographic Wetness Index (TWI), Terrain Classification Index (TCI), Stream Power Index (STP), Digital Elevation Model (DEM), and Length of Slope (LS) were considered as topographic inputs using Arc-GIS and SAGA software. Also, salinity spatial and vegetation indices were extracted from Landsat 8 images and were considered spectral inputs. The GMDH neural network was used to model salinity with a ratio of 70% for training and 30% for validation. The results showed that the soil salinity values were between 0.1 and 18 with mean and standard deviation of 5 and 4.7 dS/m, respectively. Also, the results of modeling indicated that the statistical parameters R2, MBE, and NRMSE in the training step were 0.80, 0.06, and 42.1%, respectively. The same values in the validation step were 0.79, 0.13, and 48.7%, respectively. Therefore, the application of spectral, topographic, and GMDH neural network indices for modeling soil salinity is effective. |
| first_indexed | 2024-04-09T17:40:37Z |
| format | Article |
| id | doaj.art-5e5288f562524849bf8582a89dc43f24 |
| institution | Directory Open Access Journal |
| issn | 2476-3594 2476-5554 |
| language | fas |
| last_indexed | 2024-04-09T17:40:37Z |
| publishDate | 2023-03-01 |
| publisher | Isfahan University of Technology |
| record_format | Article |
| series | علوم آب و خاک |
| spelling | doaj.art-5e5288f562524849bf8582a89dc43f242023-04-17T05:42:14ZfasIsfahan University of Technologyعلوم آب و خاک2476-35942476-55542023-03-01264249259Application of the GMDH Neural Network in Monitoring Soil Salinity of Saveh Plain using Spectral and Topographic IndicatorsV. Habibi Arbatani0M. Akbari1Z. Moghaddam2A.M. Bayat3 Science and Research Branch, Islamic Azad University, Tehran Arak University Payam Noor University, Tehran Province Isfahan University of Technology In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the form of topographic and spectral categories. Land area parameters such as the Topographic Wetness Index (TWI), Terrain Classification Index (TCI), Stream Power Index (STP), Digital Elevation Model (DEM), and Length of Slope (LS) were considered as topographic inputs using Arc-GIS and SAGA software. Also, salinity spatial and vegetation indices were extracted from Landsat 8 images and were considered spectral inputs. The GMDH neural network was used to model salinity with a ratio of 70% for training and 30% for validation. The results showed that the soil salinity values were between 0.1 and 18 with mean and standard deviation of 5 and 4.7 dS/m, respectively. Also, the results of modeling indicated that the statistical parameters R2, MBE, and NRMSE in the training step were 0.80, 0.06, and 42.1%, respectively. The same values in the validation step were 0.79, 0.13, and 48.7%, respectively. Therefore, the application of spectral, topographic, and GMDH neural network indices for modeling soil salinity is effective.http://jstnar.iut.ac.ir/article-1-4252-en.htmlhypercube techniquevegetation indextopographic wetness indexlandsat 8digital elevation model |
| spellingShingle | V. Habibi Arbatani M. Akbari Z. Moghaddam A.M. Bayat Application of the GMDH Neural Network in Monitoring Soil Salinity of Saveh Plain using Spectral and Topographic Indicators علوم آب و خاک hypercube technique vegetation index topographic wetness index landsat 8 digital elevation model |
| title | Application of the GMDH Neural Network in Monitoring Soil Salinity of Saveh Plain using Spectral and Topographic Indicators |
| title_full | Application of the GMDH Neural Network in Monitoring Soil Salinity of Saveh Plain using Spectral and Topographic Indicators |
| title_fullStr | Application of the GMDH Neural Network in Monitoring Soil Salinity of Saveh Plain using Spectral and Topographic Indicators |
| title_full_unstemmed | Application of the GMDH Neural Network in Monitoring Soil Salinity of Saveh Plain using Spectral and Topographic Indicators |
| title_short | Application of the GMDH Neural Network in Monitoring Soil Salinity of Saveh Plain using Spectral and Topographic Indicators |
| title_sort | application of the gmdh neural network in monitoring soil salinity of saveh plain using spectral and topographic indicators |
| topic | hypercube technique vegetation index topographic wetness index landsat 8 digital elevation model |
| url | http://jstnar.iut.ac.ir/article-1-4252-en.html |
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