Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA
The main purpose of this study, is to evaluate an advanced feature selection technique, artificial bee colony (ABC) algorithm; to reduce the number of auxiliary variables derived from a digital elevation model (DEM) and remotely sensed data (e.g. Landsat images). A combination of depth functions (e....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-05-01
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| Series: | Carbon Management |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17583004.2017.1330593 |
| _version_ | 1797678434623684608 |
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| author | Ruhollah Taghizadeh-Mehrjardi Ram Neupane Kunal Sood Sandeep Kumar |
| author_facet | Ruhollah Taghizadeh-Mehrjardi Ram Neupane Kunal Sood Sandeep Kumar |
| author_sort | Ruhollah Taghizadeh-Mehrjardi |
| collection | DOAJ |
| description | The main purpose of this study, is to evaluate an advanced feature selection technique, artificial bee colony (ABC) algorithm; to reduce the number of auxiliary variables derived from a digital elevation model (DEM) and remotely sensed data (e.g. Landsat images). A combination of depth functions (e.g. power, logarithmic and spline) and data miner methods (artificial neural network: ANN and support vector regression: SVR) were applied for three-dimensional mapping of soil organic matter (SOM) in Big Sioux River watershed, South Dakota, USA. Unsurprisingly, the ABC feature selection algorithm indicated that remote sensing data (e.g. NDVI) are powerful predictors at soil surface, however, with the increasing soil depth, the terrain parameters (e.g. wetness index) became more relevant. Our findings from this study demonstrated that both the spatial models generally performed well. The mean R2 values calculated by 10-fold cross validation suggested that SVR and ANN models could explain approximately 50 and 57% of total SOM variability, respectively. However, predictive power of both models increased when ABC feature selection algorithm applied, particularly when it combined with the ANN model. Results showed that DSM approaches are very important and powerful tool to explain the 3D spatial distribution of SOM across the study watershed. |
| first_indexed | 2024-03-11T22:59:44Z |
| format | Article |
| id | doaj.art-8b1a2d82d2f94668bf8698593ef51d16 |
| institution | Directory Open Access Journal |
| issn | 1758-3004 1758-3012 |
| language | English |
| last_indexed | 2024-03-11T22:59:44Z |
| publishDate | 2017-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Carbon Management |
| spelling | doaj.art-8b1a2d82d2f94668bf8698593ef51d162023-09-21T15:09:04ZengTaylor & Francis GroupCarbon Management1758-30041758-30122017-05-018327729110.1080/17583004.2017.13305931330593Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USARuhollah Taghizadeh-Mehrjardi0Ram Neupane1Kunal Sood2Sandeep Kumar3South Dakota State UniversityIndiana UniversitySouth Dakota State UniversitySouth Dakota State UniversityThe main purpose of this study, is to evaluate an advanced feature selection technique, artificial bee colony (ABC) algorithm; to reduce the number of auxiliary variables derived from a digital elevation model (DEM) and remotely sensed data (e.g. Landsat images). A combination of depth functions (e.g. power, logarithmic and spline) and data miner methods (artificial neural network: ANN and support vector regression: SVR) were applied for three-dimensional mapping of soil organic matter (SOM) in Big Sioux River watershed, South Dakota, USA. Unsurprisingly, the ABC feature selection algorithm indicated that remote sensing data (e.g. NDVI) are powerful predictors at soil surface, however, with the increasing soil depth, the terrain parameters (e.g. wetness index) became more relevant. Our findings from this study demonstrated that both the spatial models generally performed well. The mean R2 values calculated by 10-fold cross validation suggested that SVR and ANN models could explain approximately 50 and 57% of total SOM variability, respectively. However, predictive power of both models increased when ABC feature selection algorithm applied, particularly when it combined with the ANN model. Results showed that DSM approaches are very important and powerful tool to explain the 3D spatial distribution of SOM across the study watershed.http://dx.doi.org/10.1080/17583004.2017.1330593digital soil mappingauxiliary variablessupport vector regressionartificial neural networkssouth dakota |
| spellingShingle | Ruhollah Taghizadeh-Mehrjardi Ram Neupane Kunal Sood Sandeep Kumar Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA Carbon Management digital soil mapping auxiliary variables support vector regression artificial neural networks south dakota |
| title | Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA |
| title_full | Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA |
| title_fullStr | Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA |
| title_full_unstemmed | Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA |
| title_short | Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA |
| title_sort | artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in south dakota usa |
| topic | digital soil mapping auxiliary variables support vector regression artificial neural networks south dakota |
| url | http://dx.doi.org/10.1080/17583004.2017.1330593 |
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