Impact of Employing Weather Forecast Data as Input to the Estimation of Evapotranspiration by Deep Neural Network Models
Universidade do Algarve（阿尔加维大学）；CEOT – Centre for Electronics, Optoelectronics and Telecommunications（电子、光电子与电信中心）；MED – Mediterranean Institute for Agriculture, Environment and Development（地中海农业、环境与发展研究所）；LARSyS – Laboratory of Robotics and Engineering Systems（机器人与工程系统实验室）
Pedro J. Vaz, Gabriela Schütz, Carlos Guerrero, Pedro J. S. Cardoso
备注：
A partial version of the work submitted to ESRE/INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCES AND RENEWABLE ENERGY
参考蒸散量（ET0）是设计智能灌溉调度的关键参数，因为它通过一个系数与作物的需水量相关联。联合国粮食及农业组织提出了一种基于Penman-Monteith方程参数化的ET0计算标准方法（FAO56PM），该方法在文献中被广泛采用。使用FAO56-PM方法计算ET0需要四个主要气象参数：温度、湿度、风速和太阳辐射（SR）。对未来几天的每日ET0进行估算的一种方法是使用免费提供的天气预报服务（WFSs），这些服务可提供未来最多15天的多项气象参数估算。该方法的一个问题是，目前大多数在线服务不提供免费的SR预报参数，或者此类预报通常需要承担财务成本。因此，文献中开发并提出了几种使用机器学习和深度学习的ET0估算模型，这些模型使用经过精心挑选的精简气象参数集作为输入特征，这些参数与常见的免费WFSs兼容。然而，关于该主题的大多数研究仅使用气象站（WSs）的数据评估模型性能，而没有考虑使用天气预报数据的影响。在本研究中，作者评估了其先前模型在使用来自两个在线WFSs的天气预报数据时的性能，评估场景如下：(i) 通过人工神经网络（ANN）模型直接估算ET0；(ii) 通过ANN模型估算SR，然后使用该估算值通过FAO56-PM方法计算ET0。采用从位于葡萄牙Vale do Lobo的两个WFSs和一个WS收集的数据，后一种方法取得了最佳结果，在考虑未来最多15天的预报时，决定系数（R2）在0.893至0.667之间。
英文摘要：
Reference Evapotranspiration (ET0) is a key parameter for designing smart irrigation scheduling, since it is related by a coefficient to the water needs of a crop. The United Nations Food and Agriculture Organization, proposed a standard method for ET0 computation (FAO56PM), based on the parameterization of the Penman-Monteith equation, that is widely adopted in the literature. To compute ET0 using the FAO56-PM method, four main weather parameters are needed: temperature, humidity, wind, and solar radiation (SR). One way to make daily ET0 estimations for future days is to use freely available weather forecast services (WFSs), where many meteorological parameters are estimated up to the next 15 days. A problem with this method is that currently, SR is not provided as a free forecast parameter on most of those online services or, normally, such forecasts present a financial cost penalty. For this reason, several ET0 estimation models using machine and deep learning were developed and presented in the literature, that use as input features a reduced set of carefully selected weather parameters, that are compatible with common freely available WFSs. However, most studies on this topic have only evaluated model performance using data from weather stations (WSs), without considering the effect of using weather forecast data. In this study, the performance of authors' previous models is evaluated when using weather forecast data from two online WFSs, in the following scenarios: (i) direct ET0 estimation by an ANN model, and (ii) estimate SR by ANN model, and then use that estimation for ET0 computation, using the FAO56-PM method. Employing data collected from two WFSs and a WS located in Vale do Lobo, Portugal, the latter approach achieved the best result, with a coefficient of determination (R2) ranging between 0.893 and 0.667, when considering forecasts up to 15 days.
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