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Related Concept Videos

Temperature Measurement Sites01:14

Temperature Measurement Sites

A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...

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Updated: May 13, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils
08:20

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Monthly soil temperature modelling across different depths using machine learning algorithms in northeast India.

Abhaya Kumar Pradhan1, Deepak Jhajharia2, Kiran Bala Behura1

  • 1Department of Soil and Water Conservation Engineering, College of Agricultural Engineering & Technology, OUAT, Bhubaneswar, Odisha, 751003, India.

Scientific Reports
|May 11, 2026
PubMed
Summary

This study models soil temperature in northeast India using machine learning (ML) and meteorological data. Random Forest Regression and Bayesian Neural Network models accurately predicted soil temperature at various depths.

Keywords:
Accumulated local effectsBayesian neural networkMachine learningNortheast IndiaRandom forest regressionSoil temperature prediction

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Published on: December 21, 2019

Area of Science:

  • Agricultural Science
  • Environmental Science
  • Data Science

Background:

  • Soil temperature is critical for agricultural systems and hydrogeological processes.
  • Accurate soil temperature data is scarce in data-limited regions like northeast India.
  • Machine learning (ML) offers a promising approach for modeling soil temperature using available meteorological variables.

Purpose of the Study:

  • To model soil temperature at 5, 15, and 30 cm depths in Jorhat, Assam (northeast India).
  • To evaluate the performance of five ML techniques: Random Forest Regression (RFR), Support Vector Regression (SVR), Boosted Regression Trees (BRT), Classification And Regression Tree (CART), and Bayesian Neural Network (BNN).
  • To identify the most influential meteorological variables for soil temperature prediction.

Main Methods:

  • Utilized eight long-term meteorological variables: air temperatures (min, mean, max), rainfall, sunshine hours, relative humidity (min, max), and number of rainy days.
  • Applied five ML algorithms (RFR, SVR, BRT, CART, BNN) to predict soil temperature at three different depths (5, 15, 30 cm).
  • Assessed model performance using R-squared (R²) and Nash-Sutcliffe Efficiency (NSE) metrics.

Main Results:

  • RFR and BNN demonstrated superior performance in predicting 5 cm soil temperature (R² ≈ 0.983).
  • BNN and RFR were the top models for 15 cm soil temperature prediction (R² ≈ 0.988).
  • BNN and CART achieved the highest accuracy for 30 cm soil temperature prediction (R² ≈ 0.975).
  • Mean air temperature was identified as the most significant predictor of soil temperature across all depths.

Conclusions:

  • Machine learning models, particularly RFR and BNN, can effectively predict soil temperature in data-limited regions.
  • Air temperature is the primary driver of soil temperature variations.
  • These findings support the development of soil temperature-based crop models in areas lacking direct soil temperature measurements.