Articles | Volume 23, issue 1
Nonlin. Processes Geophys., 23, 13–20, 2016
Nonlin. Processes Geophys., 23, 13–20, 2016

Research article 27 Jan 2016

Research article | 27 Jan 2016

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

T. Soares dos Santos et al.

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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
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Short summary
Statistical downscaling is widely used in large operational centers around the world, using exclusively linear relations (MLR); this study uses a statistical downscaling methodology using a nonlinear technique known as ANNs with CMIP5 project data. The artificial neural network can perform tasks that a linear program cannot. The main advantages of this are its temporal processing ability and its ability to incorporate several preceding predictor values as input without any additional effort.