A COMPARISION USING STATISTICAL AND MACHINE LEARNING METHODS FOR STREAMFLOW TIME SERIES

Lourdes Milagros Mendoza Villavicencio, David Mendes, Felipe Ferreira Monteiro, Lara de Melo Barbosa Andrade, Cássia Monalisa dos Santos Silva

Resumo


This study was carried out in the Sibinacocha lake watershed in the Peruvian Andes. In this region the long-term meteorological data are scarce and there are few studies of flow forecasts. Based on this evidence, in this study we present the monthly flow simulation, using statistical models and data-oriented model, with the purpose of evaluating the performance of these methodologies. The results of the comparative statistical analyses indicated that the data-oriented models, specifically the Recurrent Neural Networks, provided great improvements over the other models applied, specifically the ability to capture the minimum and maximum monthly flow, resulting in excellent statistical values (R2=0.85, d=0.96), thus suggesting this methodology as a possible application for flow forecasts.


Palavras-chave


Time-series analysis; Streamflow Forecasting; Neural Networks.

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DOI: http://dx.doi.org/10.5380/abclima.v26i0.70245

Direitos autorais 2020 Lourdes Villavicencio, David Mendes, Felipe Monteiro, Lara Andrade, Cassia Silva