ISSN: 1813-0410

Keywords : ANN

Modeling Soil Temperature at different depths and times as a function of some climatic data Using Artificial Neural Network

journal of kerbala university, 2011, Volume 7, Issue 3, Pages 1240-1250

In this study, implementation of artificial neural network model has been used to estimate soil temperatures at various depths and different measuring times, as a function of mean air temperature, number of sunshine hours, radiation, for any day of the year.ANN (artificial neural network ) of back propagation and fitness algorithms models . The data of soil temperature is taken from research department of soil and water / Nineveh province for the period from 1980 to 1983 and it include daily measurements of soil at depths of 5,10, 20, 30,50 and 100 cm and for three periods at 9, 12 and 15 clock for cultivated and bare soil. The data of two years was used to learn the network and the data of one year was used to test the network and compare its output with the measured data, three performance functions, namely root mean square errors (RMSE) and determination coefficient (R2), were used to evaluate the neural model , to find the adequacy between estimated data and the outputs of neural network for one year, the values of R2 ranging between 0.95 -0.99 and the values of RMSE decreased significantly for all cases of estimation. The results shows the possibility of using neural networks in the composition of the model that can be used in the estimation of deep soil temperatures through the use of surface soil temperature for three times of measurement, the successful use of neural networks in the composition of the model that can be used to estimate the deep soil temperatures through the use of soil-surface temperatures, which are measured at different time periods. Successful construction of General ANN model that predict soil temperature at any depth and time from soil surface temperature of any time have been made. The ability of constructing ANN of two dimension could estimate soil temperature with very high accuracy by adding time dimension and soil depth dimension.