Loading...
2 results
Search Results
Now showing 1 - 2 of 2
- Hybrid neural network based models for evapotranspiration prediction over limited weather parametersPublication . Vaz, Pedro J.; Schutz, G.; Guerrero, Carlos; Cardoso, PedroEvapotranspiration can be used to estimate the amount of water required by agriculture projects and green spaces, playing a key role in water management policies that combat the hydrological drought, which assumes a structural character in many countries. In this context, this work presents a study on reference evapotranspiration (ETo) estimation models, having as input a limited set of meteorological parameters, namely: temperature, humidity, and wind. Since solar radiation (SR) is an important parameter in the determination of ETo, SR estimation models are also developed. These ETo and SR estimation models compare the use of Artificial Neural Networks (ANN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and hybrid neural network models such as LSTM-ANN, RNN-ANN, and GRU-ANN. Two main approaches were taken for ET(o )estimation: (i) directly use those algorithms to estimate ETo, and (ii) estimate solar radiation first and then use that estimation together with other meteorological parameters in a method that predicts ETo. For the latter case, two variants were implemented: the use of the estimated solar radiation as (ii.1) a feature of the neural network regressors, and (ii.2) the use of the Penman-Monteith method (a.k.a. FAO-56PM method, adopted by the United Nations Food and Agriculture Organization) to compute ETo, which has solar radiation as one of the input parameters. Using experimental data collected from a weather station (WS) located in Vale do Lobo (Portugal), the later approach achieved the best result with a coefficient of determination (R-2) of 0.977. The developed model was then applied to data from eleven stations located in Colorado (USA), with very distinct climatic conditions, showing similar results to the ones for which the models were initially designed ((R2) > 0.95), proving a good generalization. As a final notice, the reduced-set features were carefully selected so that they are compatible with free online weather forecast services.
- A study on the prediction of evapotranspiration using freely available meteorological dataPublication . J. Vaz, Pedro; Schütz, Gabriela; Guerrero, Carlos; Cardoso, PedroDue to climate change, the hydrological drought is assuming a structural character with a tendency to worsen in many countries. The frequency and intensity of droughts is predicted to increase, particularly in the Mediterranean region and in Southern Africa. Since a fraction of the fresh water that is consumed is used to irrigate urban fabric green spaces, which are typically made up of gardens, lanes and roundabouts, it is urgent to implement water waste prevention policies. Evapotranspiration (ETO) is a measurement that can be used to estimate the amount of water being taken up or used by plants, allowing a better management of the watering volumes but, the exact computation of the evapotranspiration volume is not possible without using complex and expensive sensor systems. In this study, several machine learning models were developed to estimate reference evapotranspiration and solar radiation from a reducedfeature dataset, such has temperature, humidity, and wind. Two main approaches were taken: (i) directly estimate ETO, or (ii) previously estimate solar radiation and then inject it into a function or method that computes ETO. For the later case, two variants were implemented, namely the use of the estimated solar radiation as (ii.1) a feature of the machine learning regressors and (ii.2) the use of FAO-56PM method to compute ETO, which has solar radiation as one of the input parameters. Using experimental data collected from a weather station located in Vale do Lobo, south Portugal, the later approach achieved the best result with a coefficient of determination (R 2 ) of 0.975 over the test dataset. As a final notice, the reduced-set features were carefully selected so that they are compatible with online freely available weather forecast services.