Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq.
Department of Civil Engineering, Jamia Millia Islamia, New Delhi, India.
PLoS One. 2022 Nov 3;17(11):e0277079. doi: 10.1371/journal.pone.0277079. eCollection 2022.
Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels' U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models' efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.
大气温度是最重要的气象参数。尽管它对水文学、环境、灌溉和农业等多个领域都有影响，但这个参数能够很好地描述气候变化和全球变暖。因此，准确和及时的气温预测至关重要，因为它提供了更重要的信息，可供未来规划参考。本研究应用了四种数据驱动方法，即支持向量回归（SVR）、回归树（RT）、分位数回归树（QRT）、ARIMA、随机森林（RF）和梯度提升回归（GBR），来预测大陆气候条件下北美的短期和中期气温（日和周）。时间序列数据相对较长（2000 年至 2021 年），70%的数据用于模型校准（2000 年至 2015 年），其余数据用于验证。自相关和偏自相关函数用于选择预测模型的最佳输入组合。使用几种统计指标和图形比较来评估预测模型的质量。对于日尺度，SVR 生成的估计值比其他模型更准确，均方根误差（RMSE=3.592°C）、相关系数（R=0.964）、平均绝对误差（MAE=2.745°C）和 Thiels'U 统计量（U=0.127）。此外，研究发现 RT 和 SVR 在预测周气温方面表现非常出色。本研究发现，所使用数据的持续时间及其逐月的分散性和波动性极大地影响了预测模型的效果。此外，还使用随机化方法将数据分为训练和测试阶段进行了第二个场景的研究。研究发现，模型在第二个场景中的表现明显优于第一个场景，这表明气候变化影响了所研究站点的温度模式。研究结果为使用数据驱动方法生成高分辨率日度和周度温度预测提供了技术支持。
