RT Journal Article
T1 Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting
A1 Troncoso-García, Ángela R.
A1 Brito, Isabel Sofia
A1 Troncoso, Alicia
A1 Martínez-Álvarez, Francisco
K1 XAI
K1 Deep learning
K1 Evapotranspiration forecasting
K1 Hyperparameter optimization
AB Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long short-term memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time.
PB Elsevier
YR 2023
FD 2023-12
LK https://hdl.handle.net/10433/25816
UL https://hdl.handle.net/10433/25816
LA en
NO A.R. Troncoso-García, I.S. Brito, A. Troncoso, F. Martínez-Álvarez, Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting, Computers and Electronics in Agriculture, Volume 215, 2023, 108387, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2023.108387
NO UPO
DS RIO
RD May 9, 2026