Elbeltagi Ahmed, Vishwakarma Dinesh Kumar, Katipoğlu Okan Mert, Sushanth Kallem, Heddam Salim, Singh Bhaskar Pratap, Shukla Abhishek, Gautam Vinay Kumar, Pande Chaitanya Baliram, Hussain Saddam, Ghosh Subhankar, Dehghanisanij Hossein, Salem Ali
Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt.
Department of Irrigation and Drainage Engineering, College of Technology, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India.
Sci Rep. 2025 Jun 20;15(1):20200. doi: 10.1038/s41598-025-06277-2.
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) and maximum (T) 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 and T 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, T, T, T, T, T, T and T, T, T, T, T, T, T for daily minimum maximum air temperature forecasting, respectively. The M5P model outperformed the other models in predicting both T and T. For T, 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 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.
气温在估算农业用水需求、水文过程以及气候变化影响方面起着关键作用。本研究旨在确定半干旱环境下日最低气温（T）和最高气温（T）最准确的预测模型。利用埃及盖尔比亚省1979年至2014年的数据，比较了五种机器学习模型——线性回归（LR）、加法回归（AR）、支持向量机（SVM）、随机子空间（RSS）和M5剪枝算法（M5P）——对T和T的预测效果。数据集被分为75%用于训练，25%用于测试。基于最佳子集回归分析选择模型输入组合，结果表明，日最低气温和最高气温预测的最佳组合分别是T、T、T、T、T、T、T和T、T、T、T、T、T、T。M5P模型在预测T和T方面均优于其他模型。对于T，M5P模型的均方根误差（RMSE）最低，为2.4881°C，平均绝对误差（MAE）为1.9515，相对绝对误差（RAE）为40.4887，同时纳什-萨特克利夫效率（NSE）最高，为0.8048，皮尔逊相关系数（PCC）为0.8971。在T预测中，M5P模型的RMSE较低，为2.7696°C，MAE为1.9867，RAE为29.5440，NSE较高，为0.8720，决定系数（R²）为0.8720。这些结果表明，M5P是一种强大且精确的温度预测模型，显著优于LR、AR、RSS和SVM模型。研究结果为改善水资源管理、灌溉系统和农业生产力等领域的决策提供了有价值的见解，为提高半干旱地区的运营效率和可持续性提供了可靠工具。弗里德曼方差分析和邓恩检验证实了温度预测模型之间存在显著差异。加法回归存在高估现象，而线性回归和支持向量机与实际值较为接近。随机子空间和M5P表现出较高的变异性，支持向量机差异显著。对于最高气温，随机子空间和M5P表现相似，而支持向量机仍然不同。
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