An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost
Soil salinization is a serious land degradation phenomenon, posing a severe threat to regional agricultural resource utilization and sustainable development. It has been a mainstream trend to use machine-learning methods to achieve monitoring of large-scale salinized soil quickly. However, machine l...
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MDPI AG
2024-12-01
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| Series: | Agronomy |
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| author | Xia Liu Yu Hu Xiang Li Ruiqi Du Youzhen Xiang Fucang Zhang |
| author_facet | Xia Liu Yu Hu Xiang Li Ruiqi Du Youzhen Xiang Fucang Zhang |
| author_sort | Xia Liu |
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| description | Soil salinization is a serious land degradation phenomenon, posing a severe threat to regional agricultural resource utilization and sustainable development. It has been a mainstream trend to use machine-learning methods to achieve monitoring of large-scale salinized soil quickly. However, machine learning model training requires many samples and hyper-parameter optimization and lacks solvability. To compare the performance of different machine-learning models, this study conducted a soil sampling experiment on saline soils along the south bank of the Yellow River in Dalate Banner. The experiment lasted two years (2022 and 2023) during the spring bare soil period, collecting 304 soil samples. The soil salinity was estimated with the multi-source remote sensing satellite data by combining the extreme gradient boosting model (XGBoost), Optuna hyper-parameter optimization, and Shapley addition (SHAP) interpretable model. Correlation analysis and continuous variable projection were employed to identify key inversion factors. The regression effects of partial least squares regression (PLSR), geographically weighted regression (GWR), long short-term memory networks (LSTM), and extreme gradient boosting (XGBoost) were compared. The optimal model was selected to estimate soil salinity in the study area from 2019 to 2023. The results showed that the XGBoost model fitted optimally, the test set had high R<sup>2</sup> (0.76) and the ratio of performance to deviation (2.05), and the estimation results were consistent with the measured salinity values. SHAP analysis revealed that the salinity index and topographic factors were the primary inversion factors. Notably, the same inversion factor influenced varying soil salinity estimates at different locations. The saline soils of the study area in 2019 and 2023 were 65% and 44%, respectively, and the overall trend of soil salinization decreased. From the viewpoint of spatial distribution, the degree of soil salinization showed a gradually increasing trend from south to north, and it was most serious on the side near the Yellow River. This study is of great significance for the quantitative estimation of salinized soil in the irrigated area on the south bank of the Yellow River, the prevention and control of soil salinization, and the sustainable development of agriculture. |
| format | Article |
| id | doaj-art-9bad1dd1dbe04406b2b6911958f44046 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-9bad1dd1dbe04406b2b6911958f440462025-01-24T13:16:22ZengMDPI AGAgronomy2073-43952024-12-011511810.3390/agronomy15010018An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoostXia Liu0Yu Hu1Xiang Li2Ruiqi Du3Youzhen Xiang4Fucang Zhang5College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Water Resources and Architecture Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Water Resources and Architecture Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Water Resources and Architecture Engineering, Northwest A&F University, Yangling 712100, ChinaSoil salinization is a serious land degradation phenomenon, posing a severe threat to regional agricultural resource utilization and sustainable development. It has been a mainstream trend to use machine-learning methods to achieve monitoring of large-scale salinized soil quickly. However, machine learning model training requires many samples and hyper-parameter optimization and lacks solvability. To compare the performance of different machine-learning models, this study conducted a soil sampling experiment on saline soils along the south bank of the Yellow River in Dalate Banner. The experiment lasted two years (2022 and 2023) during the spring bare soil period, collecting 304 soil samples. The soil salinity was estimated with the multi-source remote sensing satellite data by combining the extreme gradient boosting model (XGBoost), Optuna hyper-parameter optimization, and Shapley addition (SHAP) interpretable model. Correlation analysis and continuous variable projection were employed to identify key inversion factors. The regression effects of partial least squares regression (PLSR), geographically weighted regression (GWR), long short-term memory networks (LSTM), and extreme gradient boosting (XGBoost) were compared. The optimal model was selected to estimate soil salinity in the study area from 2019 to 2023. The results showed that the XGBoost model fitted optimally, the test set had high R<sup>2</sup> (0.76) and the ratio of performance to deviation (2.05), and the estimation results were consistent with the measured salinity values. SHAP analysis revealed that the salinity index and topographic factors were the primary inversion factors. Notably, the same inversion factor influenced varying soil salinity estimates at different locations. The saline soils of the study area in 2019 and 2023 were 65% and 44%, respectively, and the overall trend of soil salinization decreased. From the viewpoint of spatial distribution, the degree of soil salinization showed a gradually increasing trend from south to north, and it was most serious on the side near the Yellow River. This study is of great significance for the quantitative estimation of salinized soil in the irrigated area on the south bank of the Yellow River, the prevention and control of soil salinization, and the sustainable development of agriculture.https://www.mdpi.com/2073-4395/15/1/18arid saline landsoil salinity estimationhyperparameter optimizationexplainabilitymulti-source remotely sensed data |
| spellingShingle | Xia Liu Yu Hu Xiang Li Ruiqi Du Youzhen Xiang Fucang Zhang An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost Agronomy arid saline land soil salinity estimation hyperparameter optimization explainability multi-source remotely sensed data |
| title | An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost |
| title_full | An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost |
| title_fullStr | An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost |
| title_full_unstemmed | An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost |
| title_short | An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost |
| title_sort | interpretable model for salinity inversion assessment of the south bank of the yellow river based on optuna hyperparameter optimization and xgboost |
| topic | arid saline land soil salinity estimation hyperparameter optimization explainability multi-source remotely sensed data |
| url | https://www.mdpi.com/2073-4395/15/1/18 |
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