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Comparison between random forest and multiple linear regression to create digital maps of soil chemical properties in the Thung Kula Ronghai region, Thailand
http://hdl.handle.net/10748/00012791
http://hdl.handle.net/10748/0001279109eda50f-aeb1-421a-8f11-27b2a1d529b2
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
20005-057-001.pdf (2.0 MB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||||||||
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| 公開日 | 2022-03-29 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Comparison between random forest and multiple linear regression to create digital maps of soil chemical properties in the Thung Kula Ronghai region, Thailand | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | environmental variables | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | predictor variable | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | spatial distribution | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | spectral indices | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | topographic indices | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | departmental bulletin paper | |||||||||||
| 著者 |
Srisomkiew, Sasirin
× Srisomkiew, Sasirin
× Kawahigashi, Masayuki
× Limtong, Pitayakon
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Using machine learning (ML) algorithms to digital soil mapping (DSM) allows the elucidation of relationships between soil properties and environmental variables enabling the precise prediction of soil nutrient levels. The accuracy of the predicted values using the random forest (RF) algorithm, which is the most popular ML algorithm for digital soil mapping, and multiple linear regression (MLR) were compared to create digital maps of soil chemical properties in the Thung Kula Ronghai (TKR) region, Thailand. The spectral indices including moisture stress index (MSI), normalized difference water index (NDWI), saturation index (SI), brightness index (BI), and coloration index (CI) obtained from remote sensing (RS) data were found to be more effective for predicting the various soil properties than the topographic indices derived from the DEM in the plain area. The MLR and RF models successfully predicted soil chemical properties with good predictive accuracy. The results indicated that the RF model has a slightly higher accuracy than the MLR model. However, the MLR model is superior in interpreting the relationship with the model equations. |
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| 書誌情報 |
en : Geographical reports of Tokyo Metropolitan University 巻 57, p. 1-11, 発行日 2022 |
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| ISSN | ||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||
| 収録物識別子 | 03868710 | |||||||||||
| 書誌レコードID | ||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||
| 収録物識別子 | AA00200173 | |||||||||||
| 著者版フラグ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| 出版者 | ||||||||||||
| 出版者 | Department of Geography Tokyo Metropolitan University | |||||||||||
