Knjaževačka gimnazija, Knjaževac

Faculty of Electronic Engineering, Niš, Serbia

The paper is available at the following link: PDF

Abstract:

Reliable evaluation of reference evapotranspiration depends on accurate meteorological parameter forecasts. A MATLAB-based framework for reference evapotranspiration prediction using Artificial Neural Networks (ANNs) trained on data from nearby meteorological stations is presented in this research. Minimum and maximum temperatures, vapor pressure, relative humidity, wind speed, and daylight hours are all included in the dataset. Synchronization, resampling, normalization, and reference evaportranspiration (ET0) computation are all part of preprocessing. The suggested framework shows that ANN-based methods may be expanded with sophisticated models, explicit temperature corrections, and deep learning architectures for multi- horizon prediction, In this paper we offer precise and adaptable ET0 forecasts.

Keywords:

Artificial neural networks, ET0 prediction, weather forecasting, MATLAB, performance, machine learning.

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