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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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Related Experiment Video

Updated: Sep 18, 2025

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
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Air temperature estimation and modeling using data driven techniques based on best subset regression model in Egypt.

Ahmed Elbeltagi1, Dinesh Kumar Vishwakarma2, Okan Mert Katipoğlu3

  • 1Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt. ahmedelbeltagy81@mans.edu.eg.

Scientific Reports
|June 20, 2025
PubMed
Summary
This summary is machine-generated.

The M5 pruned (M5P) machine learning model accurately forecasts daily minimum and maximum air temperatures in semi-arid regions. This study identified M5P as the superior model for improving water resource management and agricultural planning.

Keywords:
Input selectionMachine learningRegression analysisTemperature forecasting

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Area of Science:

  • Environmental Science
  • Agricultural Science
  • Data Science

Background:

  • Accurate air temperature forecasting is crucial for water resource management, hydrological studies, and climate change impact assessments.
  • Semi-arid regions face unique challenges in water availability, making precise temperature predictions essential for agricultural planning and sustainability.

Purpose of the Study:

  • To identify the most accurate machine learning model for forecasting daily minimum (Tmin) and maximum (Tmax) air temperatures in a semi-arid environment.
  • To compare the performance of Linear Regression (LR), Additive Regression (AR), Support Vector Machine (SVM), Random Subspace (RSS), and M5 pruned (M5P) models.

Main Methods:

  • Utilized historical daily temperature data (1979-2014) from Gharbia Governorate, Egypt, divided into 75% training and 25% testing sets.
  • Applied best subset regression to determine optimal input variable combinations for Tmin and Tmax forecasting.
  • Evaluated five machine learning models based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Relative Absolute Error (RAE), Nash-Sutcliffe Efficiency (NSE), and Pearson Correlation Coefficient (PCC).

Main Results:

  • The M5 pruned (M5P) model demonstrated superior performance in forecasting both Tmax and Tmin compared to LR, AR, RSS, and SVM.
  • M5P achieved the lowest RMSE (2.4881°C for Tmin, 2.7696°C for Tmax) and MAE, along with the highest NSE (0.8048 for Tmin, 0.8720 for Tmax).
  • Statistical tests (Friedman ANOVA, Dunn's test) confirmed significant differences among the models, with M5P and RSS showing high variability and SVM differing significantly.

Conclusions:

  • The M5 pruned (M5P) model is a robust and precise tool for daily air temperature forecasting in semi-arid regions.
  • The findings offer valuable insights for enhancing decision-making in water resource management, irrigation, and agricultural productivity.
  • Implementing M5P can improve operational efficiency and promote sustainability in vulnerable semi-arid environments.