Abstract:Precipitation forecast is key for water resources management in semi-arid climates. The traditional hybrid models simulate linear and nonlinear components of precipitation series separately. But they do not still provide accurate forecasts. This research aims to improve hybrid models by using an ensemble of linear and nonlinear models. Preprocessing configurations and each of the Gene Expression Programming (GEP), Support Vector Regression (SVR), and Group Method of Data Handling (GMDH) models were used as in … Show more
Meteorological events have always been of great interest because they have influenced everyday activities in critical areas, such as water resource management systems. Weather forecasts are solved with numerical weather prediction models. However, it sometimes leads to unsatisfactory performance due to the inappropriate setting of the initial state. Precipitation forecasting is essential for water resource management in semi-arid climate and seasonal rainfall areas such as the Ebro basin. This research aims to improve the estimation of the uncertainty associated with real-time precipitation predictions presenting a machine learning-based method to evaluate the uncertainty of a weather forecast obtained by the Weather Research and Forecasting model. We use a model trained with ground-truth data from the Confederación Hidrográfica del Ebro, and WRF forecast results to compute uncertainty. Experimental results show that Decision Tree-based ensemble methods get the lowest generalization error. Prediction models studied have above 90% accuracy, and root mean square error has similar results compared to those obtained with the ground truth data. Random Forest presents a difference of -0.001 concerning the 0.535 obtained with the ground truth data. Generally, using the ML-based model offers good results with robust performance over more traditional forms for uncertainty calculation and an effective alternative for real-time computation.
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