Abstract
Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models’ outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.
Highlights
For different climatic zones whether it is tropical, arid or semi-arid, soil temperature is considered as one of the most essential variables affection the agricultural water management and process
That the results showed that Fractionally Autoregressive Integrated Moving Average (FARIMA) outperformed the Feed Forward Back Propagation Neural Network (FFBPNN) and Gene Expression Programming (GEP) methods, the prediction accuracy for soil temperature (ST) using FARIMA were relatively inadequate for the extreme ST values, Mehdizadeh et al [20]
In case of 5 cm depth, including climatic variables (RH, Rs and W) in inputs does not affect the accuracy of the classification and regression trees (CART) and group method of data handling (GMDH) while the exactness of the extreme learning machine (ELM) and artificial neural networks (ANN) decreases and the one input model with temperature data has the lowest Root mean square error (RMSE) for the all methods
Summary
For different climatic zones whether it is tropical, arid or semi-arid, soil temperature is considered as one of the most essential variables affection the agricultural water management and process. In this study, there is a need to investigate the potential for developing accurate ST prediction model relying on the most suitable model’s input pattern In this context, it will be curious to introduce a method that might able to automatically prior select the most appropriate input selections. The current study, an investigation for predicting the soil temperature utilizing several machine learning methods has been proposed and assessed As it has been reported earlier, it could be noticed that there were a lot of research efforts have been developed to predict the ST at different depths. For the recently classical machine learning models, prior interrelationship information between different used variables, for example, covariance, variance and correlation values have to be accurately recognized to select the proper model’ input-output architecture. It should be noticed here that it is the first attempt to utilize both the CART and GMDH models as a predictor for soil temperature
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