Soil salinity estimation in Shule River Basin using support vector regression model
Bibliographic Data
| ID | 21647802 |
|---|---|
| Authors | Yingfu Tang (0000-0001-8205-1108, Department of Hydraulic Engineering Tsinghua University Beijing PR China), Zhongjing Wang (0000-0001-9374-564X, Department of Hydraulic Engineering Tsinghua University Beijing PR China, corresponding author), Teng Zhang (0000-0002-1897-348X, Department of Hydraulic Engineering Tsinghua University Beijing PR China) |
| Year | 2023 |
| Volume | 34 |
| Issue | 13 |
| Pages | 4094-4108 |
| Publication date | 2023-08-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Land Degradation and Development (JOURNAL) |
| Journal identifiers | ISSN: 1085-3278 • E-ISSN: 1099-145X |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/ldr.4741 |
| OpenAlex | W4376564451 |
| Language | EN |
| References cited | 67 |
Soil salinization is a progressive degradation process that spreads globally and leads to a decline in soil fertility. Assessing the scale of salinization is crucial for sustainable agricultural development and saline‐land rehabilitation. In this study, we proposed a support vector regression spatial (SVR‐S) model that utilizes spatial dependence information to predict soil salinity over the irrigation area of the Shule River Basin in northwestern China. To investigate the performance of the SVR‐S model, 50 soil samples were collected in the field. Semivariograms of soil salinity (measured as total salt content and ion concentrations) were constructed to measure spatial dependence. The SVR‐S model was compared with the original SVR model and the geographically weighted regression (GWR) model regarding the salinity prediction ability. The soil salinity in the experimental area demonstrated a strong spatial dependence pattern. The SVR‐S model delivered a better performance than the SVR‐O and GWR. SVR‐S showed a correlation coefficient R of 0.87 and a root mean square error (RMSE) of 1.83%, while the performance of SVR‐O (R = 0.75, RMSE = 3.32%) and GWR (R = 0.73, RMSE = 3.47%) was comparatively poor. Topographic indices integrating spatial information contributed the most to the estimation of salinity in the study area. This study provides a new approach to integrating spatial information for accurate soil salinity mapping
Coefficient of determination · Correlation coefficient · Kriging · Mean squared error · Salinity · Soil salinity · Soil water · Spatial variability · Statistics · Support vector machine · Computer Science · Environmental Science · Mathematics · Soil and Land Suitability Analysis · Soil Geostatistics and Mapping · Soil Moisture and Remote Sensing · Geology · Soil Science
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| Citation velocity | historical |
|---|---|
| Highly cited | No |