Hybridization of cokriging and gaussian process regression modelling techniques in mapping soil sulphur
文献类型：期刊论文
| 作者 | John, Kingsley3; Agyeman, Prince Chapman3; Kebonye, Ndiye Michael3; Isong, Isong Abraham1; Ayito, Esther O.1; Ofem, Kokei Ikpi1; Qin, Cheng-Zhi2
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| 刊名 | CATENA
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| 出版日期 | 2021-11-01 |
| 卷号 | 206页码:18 |
| 关键词 | Soil nutrient distribution Digital soil mapping Machine learning Cokriging Agricultural productivity |
| ISSN号 | 0341-8162 |
| DOI | 10.1016/j.catena.2021.105534 |
| 通讯作者 | John, Kingsley(johnk@af.czu.cz) |
| 英文摘要 | As a widely used soil mapping method, the kriging method involves a high sampling point to generate quality and accurate maps. Combining kriging and machine learning (ML) can produce soil maps with fewer number sampling points. This study's objective was to implement a hybrid approach based on the Cokriging (Cok) and an ML technique [i.e., Gaussian process regression (GPR)]. The hybrid method (called the Cok-GPR method) uses the Cok (Coki, i = 1 to n) as a predictor method of the soil sulphur and then uses GPR to improve the prediction accuracy. The proposed method was compared with the Cok and the GPR models, respectively, in a case study. Soil samples (n = 115) were collected from the topsoil (0-20) at the agricultural site of approximately 889.8 km2 size. S, Ca, K, Mg, Na, P, and V were estimated via Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) equipment and presented as S_ICP-OES (response variable), and predictors (Ca_ICP-OES, K_ICP-OES, Mg_ICP-OES, Na_ICP-OES, P_ICP-OES, and V_ICP-OES), respectively. For GPR and Cok-GPR, an 80% (calibration) to 20% (validation) random dataset split was performed. The calibration dataset was implemented under k = 10-fold cross-validation, repeated five times. All the models were evaluated by MAE, RMSE and R2 criteria. According to the model and map performances. Cok1 model via Ca_ICP-OES, K_ICP-OES, Mg_ICP-OES gave the best model (MAE = 1.28 mg/kg RMSE = 164.42 mg/kg, R2 = 0.85). Its corresponding GPR1 approach, modelled with the same predictors produced the best (MAE = 85.43 mg/kg, RMSE = 137.59 mg/kg, R2 = 0.83). While the hybrid Cok1-GPR model produced MAE = 76.84 mg/kg, RMSE = 102.11 mg/kg, and R2 = 0.91. The model outperformed both the Cok and GPR models, respectively. The proposed Cok-GPR model can be applied to efficiently predict soil nutrient element levels at the regional level and be useful during policymaking. |
| WOS关键词 | MACHINE LEARNING-METHODS ; ORGANIC-MATTER ; RANDOM FOREST ; ELEMENT ; INFORMATION ; PREDICTION ; FERTILIZER ; POTASSIUM ; MAGNESIUM ; CALCIUM |
| 资助项目 | Faculty of Agrobiology, Food and Natural Resources of the Czech University of Life Sciences Prague (CZU)[SV20-5-21130] ; European Regional Development Fund[CZ.02.1.01/0.0/0.0/16_019/0000845] |
| WOS研究方向 | Geology ; Agriculture ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000688449100043 |
| 出版者 | ELSEVIER |
| 资助机构 | Faculty of Agrobiology, Food and Natural Resources of the Czech University of Life Sciences Prague (CZU) ; European Regional Development Fund |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/165391]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | John, Kingsley |
| 作者单位 | 1.Univ Calabar, Dept Soil Sci, Calabar, Nigeria 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Czech Univ Life Sci, Fac Agrobiol Food & Nat Resources, Dept Soil Sci & Soil Protect, Kamycka 129, Prague 16500, Czech Republic |
| 推荐引用方式 GB/T 7714 | John, Kingsley,Agyeman, Prince Chapman,Kebonye, Ndiye Michael,et al. Hybridization of cokriging and gaussian process regression modelling techniques in mapping soil sulphur[J]. CATENA,2021,206:18. |
| APA | John, Kingsley.,Agyeman, Prince Chapman.,Kebonye, Ndiye Michael.,Isong, Isong Abraham.,Ayito, Esther O..,...&Qin, Cheng-Zhi.(2021).Hybridization of cokriging and gaussian process regression modelling techniques in mapping soil sulphur.CATENA,206,18. |
| MLA | John, Kingsley,et al."Hybridization of cokriging and gaussian process regression modelling techniques in mapping soil sulphur".CATENA 206(2021):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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