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Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model

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Abstract

Soil temperature (Ts) is an essential regulator of a plant’s root growth, evapotranspiration rates, and hence soil water content. Over the last few years, in response to the climatic change, significant amount of research has been conducted worldwide to understand the quantitative link between soil temperature and the climatic factors, and it was highlighted that the hydrothermal conditions in the soil are continuously changing in response to the change of the hydro-meteorological factors. A large amount of the models have been developed and used in the past for the analysis and modelling of soil temperature, however, none of them has investigated the robustness and feasibilities of the deep echo state network (Deep ESN) model. A more accurate model for forecasting Ts presents many worldwide opportunities in improving irrigation efficiency in arid climates and help attain sustainable water resources management. This research compares the application of the novel Deep ESN model versus three conventional machine learning models for soil temperature forecasting at 10 and 20 cm depths. We combined several critical daily hydro-meteorological data into six different input combinations for constructing the Deep ESN model. The accuracy of the developed soil temperature models is evaluated using three deterministic indices. The results of the evaluation indicate that the Deep ESN model outperformed conventional machine learning methods and can reduce the root mean square error (RMSE) accuracy of the traditional models between 30 and 60% in both stations. In the test phase, the most accurate estimation was obtained by Deep ESN at depths of 10 cm by RMSE = 2.41 °C and 20 cm by RMSE = 1.28 °C in Champaign station and RMSE = 2.17 °C (10 cm) and RMSE = 1.52 °C (20 cm) in Springfield station. The superior performance of the Deep ESN model confirmed that this model can be successfully applied for modelling Ts based on meteorological paarameters.

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Abbreviations

Deep ESN:

Deep echo state network

MLPNN:

Multilayer perceptron neural network

RF:

Random forest

TEM:

Air temperature

ET0:

Potential evapotranspiration

DEW:

Dew point temperature

HUM:

Relative humidity

RAD:

Solar radiation

WIN:

Wind speed

ST:

Soil temperature

LM:

Levenberg–Marquardt

IL:

Input layer

HL:

Hidden layer

OL:

Output layer

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Meysam Alizamir

  2. The Faculty of Civil Engineering, Duy Tan University, Da Nang, 550000, Vietnam

    Meysam Alizamir

  3. Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, Republic of Korea

    Sungwon Kim

  4. Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    Mohammad Zounemat-Kermani

  5. Agronomy Department, Hydraulics Division, Faculty of Science, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria

    Salim Heddam

  6. Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran

    Amin Hasanalipour Shahrabadi

  7. School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada

    Bahram Gharabaghi

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  1. Meysam Alizamir
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  2. Sungwon Kim
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  3. Mohammad Zounemat-Kermani
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  4. Salim Heddam
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  6. Bahram Gharabaghi
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Alizamir, M., Kim, S., Zounemat-Kermani, M. et al. Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model. Artif Intell Rev 54, 2863–2890 (2021). https://doi.org/10.1007/s10462-020-09915-5

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