Estimation of daily soil temperature via data mining techniques in semi-arid climate conditions
This paper investigates the potential of data mining techniques to predict daily soil temperatures at 5-100 cm depths for agricultural purposes. Climatic and soil temperature data from Isfahan province located in central Iran with a semi-arid climate was used for the modeling process. A subtractive...
- Autores:
-
Sattari, Mohammad Taghi
Dodangeh, Esmaeel
Abraham, John
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2017
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/63579
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/63579
http://bdigital.unal.edu.co/64025/
- Palabra clave:
- 55 Ciencias de la tierra / Earth sciences and geology
Soil temperature
Data mining
M5 tree model
ANFIS
ANN
Temperatura del suelo
minería de datos
modelo tipo árbol M5
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
Summary: | This paper investigates the potential of data mining techniques to predict daily soil temperatures at 5-100 cm depths for agricultural purposes. Climatic and soil temperature data from Isfahan province located in central Iran with a semi-arid climate was used for the modeling process. A subtractive clustering approach was used to identify the structure of the Adaptive Neuro-Fuzzy Inference System (ANFIS), and the result of the proposed approach was compared with artificial neural networks (ANNs) and an M5 tree model. Result suggests an improved performance using the ANFIS approach in predicting soil temperatures at various soil depths except at 100 cm. The performance of the ANNs and M5 tree models were found to be similar. However, the M5 tree model provides a simple linear relation to predicting the soil temperature for the data ranges used in this study. Error analyses of the predicted values at various depths show that the estimation error tends to increase with the depth. |
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