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Soil temperature estimation at different depths using machine learning paradigms based on meteorological data.

Anurag Malik1, Gadug Sudhamsu2, Manjinder Kaur Wratch3

  • 1Punjab Agricultural University, Regional Research Station, Bathinda, 151001, Punjab, India. amalik19@pau.edu.

Environmental Monitoring and Assessment
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Summary

Accurate soil temperature (ST) estimation is crucial for environmental analysis and crop growth. The co-active neuro-fuzzy inference system (CANFIS) model demonstrated superior accuracy in predicting daily ST at various depths using meteorological data.

Keywords:
BathindaGamma testML paradigmsPerformance metricsSoil temperature

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Area of Science:

  • Environmental Science
  • Agricultural Science
  • Data Science

Background:

  • Soil temperature (ST) is critical for understanding environmental conditions, climate change impacts, and agricultural productivity, influencing crop growth and seed germination.
  • Accurate ST monitoring is essential for effective land management and climate modeling.

Purpose of the Study:

  • To estimate daily soil temperature (ST) at multiple depths (5, 15, and 30 cm) using machine learning (ML) models.
  • To compare the performance of four ML paradigms: Random Forest (RF), Radial Basis Neural Network (RBNN), Multi-layer Perceptron Neural Network (MLPNN), and Co-active Neuro-Fuzzy Inference System (CANFIS).

Main Methods:

  • Utilized meteorological data including mean air temperature (Tmean), relative humidity (RH), wind speed (WS), and bright sunshine hours (SSH).
  • Employed the Gamma Test (GT) to select the optimal combination of input variables for each soil depth.
  • Evaluated model performance using metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Scatter Index (SI), Coefficient of Efficiency (COE), Pearson Correlation Coefficient (PCC), and Index of Agreement (IOA).

Main Results:

  • The CANFIS model achieved the highest accuracy across all soil depths.
  • CANFIS demonstrated low error rates (MAE: 0.788-0.806 °C, RMSE: 0.854-1.074 °C) and high agreement (COE: 0.985-0.986, PCC: 0.993-0.995, IOA: 0.996-0.998).
  • The study confirmed the effectiveness of meteorological parameters as inputs for ST estimation models.

Conclusions:

  • The CANFIS model, utilizing Tmean, RH, WS, and SSH, is highly capable for accurate daily soil temperature estimation at various depths.
  • The findings provide a reliable method for soil temperature prediction, supporting agricultural and environmental monitoring efforts.
  • Machine learning approaches offer robust tools for analyzing complex environmental data and improving predictive accuracy.