article · Scientific Reports
Accurate daily air temperature forecasting is essential for assessing agricultural water requirements, managing hydrological processes, and understanding climate change impacts. Focusing on the semi-arid environment of Gharbia Governorate in Egypt, research evaluated five machine learning models: linear regression, additive regression, support vector machines, random subspace, and M5 pruned. The models were tested on historical daily minimum and maximum temperature data from 1979 to 2014, with best subset regression used to determine the optimal historical lag combinations for inputs. The M5 pruned model proved to be the most accurate for forecasting both minimum and maximum temperatures, achieving superior correlation coefficients, lower error metrics, and high Nash-Sutcliffe efficiency scores compared to the alternative techniques. Statistical tests confirmed significant differences among the models, establishing the M5 pruned algorithm as a robust predictive option for local temperature conditions.
In semi-arid regions facing climate pressures, dependable temperature forecasts are vital for managing scarce water resources. Precise daily temperature models enable authorities and farmers to anticipate crop water requirements, modernise irrigation planning, and maintain agricultural productivity under variable climate conditions.
This applied research provides an analytical approach that could be integrated into operational agricultural planning software or regional water management platforms. The primary users would be irrigation authorities, hydrological service providers, and agricultural managers seeking to optimise resource scheduling in semi-arid zones. The methodology represents early-stage to applied algorithmic research, needing integration into live meteorological pipelines before commercial software deployment.
AI-generated from the published abstract. Always read the original work before citing.
Abstract Air temperature plays a critical role in estimating agricultural water requirements, hydrological processes, and the climate change impacts. This study aims to identify the most accurate forecasting model for daily minimum (T min ) and maximum (T max ) temperatures in a semi-arid environment. Five machine learning models—linear regression (LR), additive regression (AR), support vector machine (SVM), random subspace (RSS), and M5 pruned (M5P)—were compared for T max and T min forecasting in Gharbia Governorate, Egypt, using data from 1979 to 2014. The dataset was divided into 75% for training and 25% for testing. Model input combinations were selected based on best subset regression analysis, result shows the best combination was T min(t−1) , T min(t−3) , T min(t−4) , T min(t−5) , T min(t−6) , T min(t−7) , T min(t−8) and T max (t−1) , T max (t−2) , T max (t−3) , T max (t−4) , T max (t−5) , T max (t−6) , T max (t−8) for daily minimum maximum air temperature forecasting, respectively. The M5P model outperformed the other models in predicting both T max and T min . For T min , the M5P model achieved the lowest root mean square error (RMSE) of 2.4881 °C, mean absolute error (MAE) of 1.9515, and relative absolute error (RAE) of 40.4887, alongside the highest Nash-Sutcliffe efficiency (NSE) of 0.8048 and Pearson correlation coefficient (PCC) of 0.8971. In T max forecasting, M5P showed a lower RMSE of 2.7696 °C, MAE of 1.9867, RAE of 29.5440, and higher NSE of 0.8720 and R² of 0.8720. These results suggest that M5P is a robust and precise model for temperature forecasting, significantly outperforming LR, AR, RSS, and SVM models. The findings provide valuable insights for improving decision-making in areas such as water resource management, irrigation systems, and agricultural productivity, offering a reliable tool for enhancing operational efficiency and sustainability in semi-arid regions. The Friedman ANOVA and Dunn’s test confirm significant differences among temperature forecasting models. Additive Regression overestimates, while Linear Regression and SVM align closely with actual values. Random Subspace and M5P exhibit high variability, with SVM differing significantly. For maximum temperature, Random Subspace and M5P perform similarly, while SVM remains distinct.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1038/s41598-025-06277-2
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
