Abstract
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.
Highlights
Soil temperature prediction is important for various agricultural purposes, especially in arid and semi-arid regions, such as Iran
The higher values of coefficients of variation in the surface layers indicate that higher variability of the soil temperature at this level is possibly due to the variety of causal mechanisms influencing soil temperature
As can be seen from the figure, a wide range of fluctuations exists for the surface layers which decreases with increasing depth
Summary
Soil temperature prediction is important for various agricultural purposes, especially in arid and semi-arid regions, such as Iran. Temporal patterns of soil temperatures in these regions show large seasonal and daily fluctuations. These variations in soil temperature affect plant growth directly through their effect on physiological activities and indirectly through the effect on soil nutrient availability (Tuntiwaranurk et al, 2006). It helps agronomists and engineers to decide the proper plantation date, design drainage and irrigation systems, and to optimize the application of pesticides and fertilizers to reduce chemical pollution of soils and groundwater. For these reasons, understanding of the variation in soil temperature is vital. To aid in this measurement process, application of data-driven models are used to estimate daily soil temperatures in ungaged homogeneous regions
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