Katipoğlu Okan Mert
Department of Civil Engineering, Erzincan Binali Yıldırım University, Erzincan, Turkey.
Environ Sci Pollut Res Int. 2023 Mar;30(15):44043-44066. doi: 10.1007/s11356-023-25369-y. Epub 2023 Jan 21.
Accurate prediction of evapotranspiration values is important in planning agricultural irrigation, crop growth research, and hydrological modeling. This study is aimed at estimating monthly evapotranspiration (ET) values in Hakkâri province by combining support vector regression, bagged tree, and boosted tree methods with wavelet transform. For this purpose, precipitation, runoff, surface net solar radiation, air temperatures, and previous ET values were divided into sub-signals with various mother wavelets such as Daubechies 4, Meyer, and Symlet 2 and presented as input to machine learning (ML) algorithms. The study's main contribution to the literature is to reveal which wavelet-based machine learning model, mother wavelet type, and combination of meteorological data show the most realistic results in ET estimation. While establishing the models, the data were divided into 80% training and 20% testing. The models' performances were based on the widely used root mean square error, mean absolute error, determination coefficient, and Taylor diagrams. As a result of the study, it was revealed that the hybrid wavelet ML, which is established with input combinations separated into subcomponents by wavelet transform, generally produces more successful predictions than the stand-alone ML model. In addition, it was revealed that the optimum ET forecasting model was obtained with the wavelet bagged tree algorithm with Symlet 2 mother wavelet. Even though the best model established is based on the precipitation and temperature inputs, it was revealed that past ET, solar radiation, and runoff values are also effective inputs in ET prediction. The results can also be used in other regions of the world with semi-arid climates, such as Hakkâri. The study's outputs provide essential resources to decision-makers and planners to manage water resources and plan agricultural irrigation.
准确预测蒸发散值对于农业灌溉规划、作物生长研究和水文建模至关重要。本研究旨在通过将支持向量回归、袋装树和提升树方法与小波变换相结合，估算哈卡里省的月蒸发散（ET）值。为此，利用Daubechies 4、Meyer和Symlet 2等各种母小波将降水、径流、地表净太阳辐射、气温和先前的ET值分解为子信号，并将其作为机器学习（ML）算法的输入。该研究对文献的主要贡献在于揭示哪种基于小波的机器学习模型、母小波类型以及气象数据组合在ET估算中显示出最符合实际的结果。建立模型时，数据被分为80%用于训练和20%用于测试。模型的性能基于广泛使用的均方根误差、平均绝对误差、决定系数和泰勒图。研究结果表明，通过小波变换将输入组合分解为子分量而建立的混合小波ML模型，通常比独立的ML模型能产生更成功的预测。此外，研究还发现使用Symlet 2母小波的小波袋装树算法可获得最优的ET预测模型。尽管所建立的最佳模型基于降水和温度输入，但研究表明过去的ET、太阳辐射和径流值在ET预测中也是有效的输入。研究结果也可用于世界其他半干旱气候地区，如哈卡里。该研究的成果为决策者和规划者管理水资源和规划农业灌溉提供了重要资源。
