Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks
Soil nutrient is an important aspect that contributes to the soil fertility and environmental effects. Traditional evaluation approaches of soil nutrient are quite hard to operate, making great difficulties in practical applications. In this paper, we present a series of comprehensive evaluation mod...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/478569 |
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| _version_ | 1832556898227847168 |
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| author | Hao Li Weijia Leng Yibing Zhou Fudi Chen Zhilong Xiu Dazuo Yang |
| author_facet | Hao Li Weijia Leng Yibing Zhou Fudi Chen Zhilong Xiu Dazuo Yang |
| author_sort | Hao Li |
| collection | DOAJ |
| description | Soil nutrient is an important aspect that contributes to the soil fertility and environmental effects. Traditional evaluation approaches of soil nutrient are quite hard to operate, making great difficulties in practical applications. In this paper, we present a series of comprehensive evaluation models for soil nutrient by using support vector machine (SVM), multiple linear regression (MLR), and artificial neural networks (ANNs), respectively. We took the content of organic matter, total nitrogen, alkali-hydrolysable nitrogen, rapidly available phosphorus, and rapidly available potassium as independent variables, while the evaluation level of soil nutrient content was taken as dependent variable. Results show that the average prediction accuracies of SVM models are 77.87% and 83.00%, respectively, while the general regression neural network (GRNN) model’s average prediction accuracy is 92.86%, indicating that SVM and GRNN models can be used effectively to assess the levels of soil nutrient with suitable dependent variables. In practical applications, both SVM and GRNN models can be used for determining the levels of soil nutrient. |
| format | Article |
| id | doaj-art-7cadec7b5fa0472994dcd41d894f4207 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-7cadec7b5fa0472994dcd41d894f42072025-02-03T05:44:06ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/478569478569Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural NetworksHao Li0Weijia Leng1Yibing Zhou2Fudi Chen3Zhilong Xiu4Dazuo Yang5College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, ChinaCollege of Chemistry, Sichuan University, Chengdu, Sichuan 610064, ChinaKey Laboratory of Marine Bio-Resources Restoration and Habitat Reparation in Liaoning Province, Dalian Ocean University, Dalian 116023, ChinaKey Laboratory of Marine Bio-Resources Restoration and Habitat Reparation in Liaoning Province, Dalian Ocean University, Dalian 116023, ChinaCollege of Life Science and Technology, Dalian University of Technology, Dalian 116021, ChinaKey Laboratory of Marine Bio-Resources Restoration and Habitat Reparation in Liaoning Province, Dalian Ocean University, Dalian 116023, ChinaSoil nutrient is an important aspect that contributes to the soil fertility and environmental effects. Traditional evaluation approaches of soil nutrient are quite hard to operate, making great difficulties in practical applications. In this paper, we present a series of comprehensive evaluation models for soil nutrient by using support vector machine (SVM), multiple linear regression (MLR), and artificial neural networks (ANNs), respectively. We took the content of organic matter, total nitrogen, alkali-hydrolysable nitrogen, rapidly available phosphorus, and rapidly available potassium as independent variables, while the evaluation level of soil nutrient content was taken as dependent variable. Results show that the average prediction accuracies of SVM models are 77.87% and 83.00%, respectively, while the general regression neural network (GRNN) model’s average prediction accuracy is 92.86%, indicating that SVM and GRNN models can be used effectively to assess the levels of soil nutrient with suitable dependent variables. In practical applications, both SVM and GRNN models can be used for determining the levels of soil nutrient.http://dx.doi.org/10.1155/2014/478569 |
| spellingShingle | Hao Li Weijia Leng Yibing Zhou Fudi Chen Zhilong Xiu Dazuo Yang Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks The Scientific World Journal |
| title | Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks |
| title_full | Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks |
| title_fullStr | Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks |
| title_full_unstemmed | Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks |
| title_short | Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks |
| title_sort | evaluation models for soil nutrient based on support vector machine and artificial neural networks |
| url | http://dx.doi.org/10.1155/2014/478569 |
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