Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation
The generated vis‐NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The...
Lưu vào:
| Tác giả chính: | |
|---|---|
| Đồng tác giả: | |
| Định dạng: | BB |
| Ngôn ngữ: | English |
| Thông tin xuất bản: |
2020
|
| Chủ đề: | |
| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/9928 |
| Từ khóa: |
Thêm từ khóa bạn đọc
Không có từ khóa, Hãy là người đầu tiên gắn từ khóa cho biểu ghi này!
|
| id |
oai:localhost:DHTL-9928 |
|---|---|
| record_format |
dspace |
| spelling |
oai:localhost:DHTL-99282020-12-16T08:39:01Z Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation Knadel, M. de Jonge, L.W. Tuller, M. Rehman, H.U. Jensen, P.W. Moldrup, P. Greve, M.H. Arthur, E. Artificial neural network Ethylene glycol monoethyl ether Soil organic carbon Standardized root mean square error Soil specific surface area The generated vis‐NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSATO and SSAGAB were generated and were nearly identical to that of SSAEGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSATO, SSAGAB, and SSAEGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis‐NIRS with the WSI as a reference technique for vis‐NIRS models provides SSA estimations akin to the EGME method. https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20007 2020-12-16T08:39:01Z 2020-12-16T08:39:01Z 2020 BB 1539-1663 http://tailieuso.tlu.edu.vn/handle/DHTL/9928 en Vadose Zone Journal, Volume 19, Issue 1 (2020), pp.1-13 |
| institution |
Trường Đại học Thủy Lợi |
| collection |
DSpace |
| language |
English |
| topic |
Artificial neural network Ethylene glycol monoethyl ether Soil organic carbon Standardized root mean square error Soil specific surface area |
| spellingShingle |
Artificial neural network Ethylene glycol monoethyl ether Soil organic carbon Standardized root mean square error Soil specific surface area Knadel, M. Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| description |
The generated vis‐NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSATO and SSAGAB were generated and were nearly identical to that of SSAEGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSATO, SSAGAB, and SSAEGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis‐NIRS with the WSI as a reference technique for vis‐NIRS models provides SSA estimations akin to the EGME method. |
| author2 |
de Jonge, L.W. |
| author_facet |
de Jonge, L.W. Knadel, M. |
| format |
BB |
| author |
Knadel, M. |
| author_sort |
Knadel, M. |
| title |
Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| title_short |
Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| title_full |
Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| title_fullStr |
Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| title_full_unstemmed |
Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| title_sort |
combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| publishDate |
2020 |
| url |
http://tailieuso.tlu.edu.vn/handle/DHTL/9928 |
| work_keys_str_mv |
AT knadelm combiningvisiblenearinfraredspectroscopyandwatervaporsorptionforsoilspecificsurfaceareaestimation |
| _version_ |
1787739527995981824 |