Homogeneización de series pluviométricas de la cuenca del río Pastaza, utilizando módulos de inteligencia artificial

Abstract

In the present work a Machine Learning approach was investigated in the field of hydrology. For this purpose, were examined the existing problems of heterogeneity in the yearbooks of meteorological stations published by the National Institute of Meteorology and Hydrology (INAMHI), where the absence of values in the pluviometric series was found. In order to complete the missing data, it was used a conventional method which interpolates pluviometric data between stations in function of their distances. For this reason, in the present work was used an algorithm that contains an artificial neural network (ANN) trained to emulate the behavior of the precipitation data recorded through the scikit-learn library in a free programming software. The performance of the artificial neural network was evaluated based on the estimation of the missing pluviometric data from the meteorological stations located in the Pastaza river watershed, where the quadratic error obtained was analyzed both with the algorithm and the conventional method. To validate the running of the created software, it was obtained the correlation by ranges of the calculated values by both methods, which allowed determining the effectiveness of the machine learning based software.