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Indian Journal of Radio & Space Physics (IJRSP) >
IJRSP Vol.41 [2012] >
IJRSP Vol.41(1) [February 2012] >
| Title: | Prediction of annual and seasonal soil temperature variation using artificial neural network |
| Authors: | Jebamalar, A S
Raja, S Abraham Thambi
Bai, S Jeslin Sunitha |
| Keywords: | Artificial Neural Network (ANN)
Back Propagation Algorithm
Multi-layer Perceptron
Soil Temperature Prediction. |
| Issue Date: | Feb-2012 |
| Publisher: | NISCAIR-CSIR, India |
| PACS No.: | 92.40.Lg; 84.35.+i |
| Abstract: | The back propagation algorithm, an artificial neural network (ANN) training algorithm, is a widely applied mathematical implementation
for spatial monitoring and is used in the present study for the analysis and prediction
of soil temperature. The soil temperature data at 10 and 20 cm soil depths were
collected from the Agricultural
College and Research
Institute, Killikulam, Tuticorin District of Tamil Nadu. The observed values during
the year 2004 at 10 and 20 cm
soil depths were plotted to understand the annual and seasonal behaviour of the
temperature wave. The wave characteristics such as range of soil temperature
and rate of change of temperature/week were estimated and tabulated. Data for 1993 – 1997 (5 years) and 1993 – 2002 (10 years) were separately used as inputs
for the prediction of soil temperature in 2004 using ANN. The predicted values
were compared with the observed values and statistically validated. The
characteristics of predicted annual and seasonal wave were also compared with
observed values. It was found that the predicted values of annual wave fitted
well with observed ones with little variation for the seasonal waves. The range
of soil temperature for predicted values coincided almost with the observed
ones with regard to the annual and the seasonal waves for both 10 and 20 cm soil depths. The rate
of change of temperature/week of the predicted values coincided well with the observed ones for 10 cm soil depth. For 20 cm soil depth, the
predicted values deviated from the observed ones for the winter season while
the annual and pre-monsoon seasonal waves coincided well with the observed
values. The surface temperature was also predicted independently from 10 and 20 cm soil temperature and
error validation was done. From these, it may be convincingly stated that the
ANN can be used as a good mathematical model for the prediction of soil
temperature. |
| Page(s): | 48-57 |
| CC License: |  CC Attribution-Noncommercial-No Derivative Works 2.5 India |
| ISSN: | 0975-105X (Online); 0367-8393 (Print) |
| Source: | IJRSP Vol.41(1) [February 2012]
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