2024-04-102024-04-102023-03Jiménez Navarro, M.J., Martínez Ballesteros, M.d.M. y Sofia Brito, I. (2023). A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal. En 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23) (441-448), Tallin (Estonia): Association for Computing Machinery.978-1-4503-9517-5978-1-4503-9517-5https://hdl.handle.net/11441/156740The year 2022 was the driest year in Portugal since 1931 with 97% of territory in severe drought. Water is especially important for the agricultural sector in Portugal, as it represents 78% total consumption according to theWater Footprint report published in 2010. Reference evapotranspiration is essential due to its importance in optimal irrigation planning that reduces water consumption. This study analyzes and proposes a framework to forecast daily reference evapotranspiration at eight stations in Portugal from 2012 to 2022 without relying on public meteorological forecasts. The data include meteorological data obtained from sensors included in the stations. The goal is to perform a multi-horizon forecasting of reference evapotranspiration using the multiple related covariates. The framework combines the data processing and the analysis of several state-of-the-art forecasting methods including classical, linear, tree-based, artificial neural network and ensembles. Then, an ensemble of all trained models is proposed using a recent bioinspired metaheuristic named Coronavirus Optimization Algorithm to weight the predictions. The results in terms of MAE and MSE are reported, indicating that our approach achieved a MAE of 0.658.application/pdf8engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Computing methodologiesEnsemble methodsSupervised learning by regressionApplied computingEnvironmental sciencesinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess10.1145/3555776.3578634