Short-term forecasting of soil temperature using artificial neural network

Abstract

Soil temperature is one of the most important meteorological parameters that plays a critical role in land surface hydrological processes. In the current study, artificial neural network (ANN) models were developed and tested for 1 day ahead soil temperature forecasting at 5, 10, 20, 30, 50 and 100 cm depths. Antecedent soil temperatures plus concurrent and antecedent air temperatures were used as inputs for the ANN models. Soil and air temperature data were collected from two Iranian weather stations located in humid and arid regions for the period 2004-2005. The models' accuracies were evaluated using the Nash-Sutcliffe co-efficient of efficiency, the correlation co-efficient, the root mean square error and the mean bias error between the observed and forecasted soil temperature values. The Nash-Sutcliffe co-efficient of efficiency values >0.94 and correlation co-efficient >0.96 for all the ANN models show that the models can be applied successfully to provide accurate and reliable short-term soil temperature forecasts.

Evaluating the performance of artificial intelligence methods for estimation of monthly mean soil temperature without using meteorological data

“…Therefore, it can be said that in general, the ANN, ANFIS and GEP indicate the best and worst estimations of soil temperatures at deep and surface layers, respectively. Similar to the achieved outcomes, Tabari et al (2015) reported that deep layers had high accuracy than surface layers in estimating soil temperature by the ANN using lagged data of air and soil temperatures as inputs. Nevertheless, the attained results of the present study are in contrast to the results of Tabari et al (2011), Nahvi et al (2016.…”

“…Moreover, air temperature is the most effective parameter in modeling of soil temperature. Tabari et al (2015) developed the ANN models for daily forecasting of soil temperature in humid (Sari) and arid (Zahedan) regions in Iran at six different depths. The lagged data of soil and air temperatures were used as input variables.…”

RETRACTED: Application of adaptive neuro-fuzzy technique to determine the most relevant variables for daily soil temperature prediction at different depths

“…The results indicated the adequacy of the CANFIS for soil temperature estimation. Tabari et al (2014) employed the ANN technique to forecast shortterm soil temperature for one day ahead at six different depths at two humid and arid stations of Iran. They also considered six input combinations, using only soil temperature and average air temperature.…”

Prediction of sweet corn yield depending on cultivation technology parameters by using linear regression and artificial neural network methods

“…The method is used in crop yield prediction, climate change, soil properties, etc. (Dahikar & Rode, 2014;Dai et al, 2014;Deo & Sahin, 2015;Tabari et al, 2015;Vani et al, 2015;Le et al, 2016). The main advantages of ANNs are their high accuracy, ability to handle large and complex systems, non-linear algorithms, ability to process incomplete datasets and learning (Kalogirou, 2000;Wang et al, 2015).…”