Impact of employing weather forecast data as input to the estimation of evapotranspiration by deep neural network models

Migueis Vaz Martins, Pedro Jorge; Schütz, Gabriela; Guerrero, Carlos; Cardoso, Pedro

http://hdl.handle.net/10400.1/29088

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Autores

Migueis Vaz Martins, Pedro Jorge

Schütz, Gabriela

Guerrero, Carlos

Cardoso, Pedro

Resumo(s)

Reference Evapotranspiration (𝐸𝑇!) 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 𝐸𝑇! computation (FAO56PM), based on the parameterization of the Penman-Monteith equation, that is widely adopted in the literature. To compute 𝐸𝑇! using the FAO56-PM method, four main weather parameters are needed: temperature, humidity, wind, and solar radiation (SR). One way to make daily 𝐸𝑇! 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 𝐸𝑇! 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 𝐸𝑇! estimation by an Artificial Neural Network (ANN) model, and (ii) estimate SR by (another) ANN model, and then use that estimation for 𝐸𝑇! 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 (𝑅") ranging between 0.893 and 0.667, when considering forecasts up to 15 days.