Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene, BP 32 Al Alia, Babezzouar, Algiers, Algeria.
Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India.
Environ Sci Pollut Res Int. 2020 Aug;27(24):30001-30019. doi: 10.1007/s11356-020-08792-3. Epub 2020 May 23.
Accurate estimation of reference evapotranspiration (ET) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation, and irrigation scheduling, since it accounts for more than two-thirds of global precipitation losses. Therefore, ET-based estimation is a major concern in the hydrological cycle. The estimation of ET can be determined using various methods, including field measurement (the scale of the lysimeter), experimental methods, and mathematical equations. The Food and Agriculture Organization recommended the Penman-Monteith (FAO-56 PM) method which was identified as the standard method of ET estimation. However, this equation requires a large number of measured climatic data (maximum and minimum air temperature, relative humidity, solar radiation, and wind speed) that are not always available on meteorological stations. Over the decade, the artificial intelligence (AI) models have received more attention for estimating ET on multi-time scales. This research explores the potential of new hybrid AI model, i.e., support vector regression (SVR) integrated with grey wolf optimizer (SVR-GWO) for estimating monthly ET at Algiers, Tlemcen, and Annaba stations located in the north of Algeria. Five climatic variables namely relative humidity (RH), maximum and minimum air temperatures (T and T), solar radiation (R), and wind speed (U) were used for model construction and evaluation. The proposed hybrid SVR-GWO model was compared against hybrid SVR-genetic algorithm (SVR-GA), SVR-particle swarm optimizer (SVR-PSO), conventional artificial neural network (ANN), and empirical (Turc, Ritchie, Thornthwaite, and three versions of Valiantzas methods) models by using root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI), and through graphical interpretation. Through the results obtained, the performance of the SVR-GWO provides very promising and occasionally competitive results compared to other data-driven and empirical methods at study stations. Thus, the proposed SVR-GWO model with five climatic input variables outperformed the other models (RMSE = 0.0776/0.0613/0.0374 mm, NSE = 0.9953/ 0.9990/0.9995, PCC = 0.9978/0.9995/0.9998 and WI = 0.9988/0.9997/0.9999) for estimating ET at Algiers, Tlemcen, and Annaba stations, respectively. In conclusion, the results of this research indicate the suitability of the proposed hybrid artificial intelligence model (SVR-GWO) at the study stations. Besides, promising results encourage researchers to transfer and test these models in other locations in the world in future works.
准确估算参考蒸散量（ET）对于作物模型、可持续管理、水文水资源模拟和灌溉调度至关重要，因为它占全球降水损失的三分之二以上。因此，基于 ET 的估算在水文循环中是一个主要关注点。ET 的估算可以通过各种方法确定，包括现场测量（蒸渗仪的规模）、实验方法和数学方程。粮农组织推荐了彭曼-蒙蒂思（FAO-56 PM）方法，该方法被确定为 ET 估算的标准方法。然而，该方程需要大量的实测气候数据（最高和最低空气温度、相对湿度、太阳辐射和风速），而这些数据并非气象站都能提供。在过去十年中，人工智能（AI）模型在多时间尺度上估算 ET 受到了更多关注。本研究探讨了新型混合人工智能模型的潜力，即支持向量回归（SVR）与灰狼优化器（SVR-GWO）集成，用于估算位于阿尔及利亚北部的阿尔及尔、特莱姆森和安纳巴站的月 ET。五个气候变量，即相对湿度（RH）、最高和最低空气温度（T 和 T）、太阳辐射（R）和风速（U），用于模型构建和评估。将提出的混合 SVR-GWO 模型与混合 SVR-遗传算法（SVR-GA）、SVR-粒子群优化器（SVR-PSO）、传统人工神经网络（ANN）和经验（Turc、Ritchie、Thornthwaite 和 Valiantzas 的三种版本）模型进行比较，使用均方根误差（RMSE）、纳什-苏特克里夫效率（NSE）、皮尔逊相关系数（PCC）和威尔莫特指数（WI），并通过图形解释。通过获得的结果，与研究站的其他数据驱动和经验方法相比，SVR-GWO 的性能提供了非常有前景且偶尔具有竞争力的结果。因此，提出的具有五个气候输入变量的 SVR-GWO 模型在估算阿尔及尔、特莱姆森和安纳巴站的 ET 方面优于其他模型（RMSE=0.0776/0.0613/0.0374mm，NSE=0.9953/0.9990/0.9995，PCC=0.9978/0.9995/0.9998 和 WI=0.9988/0.9997/0.9999）。总之，本研究结果表明，该混合人工智能模型（SVR-GWO）在研究站具有适用性。此外，有希望的结果鼓励研究人员在未来的工作中将这些模型转移并在世界其他地方进行测试。
