Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

Computational Intelligence and Neuroscience
Petr Maca, Pavel Pech

Abstract

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948-2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

Methods Mentioned

BETA
one hybrid

Software Mentioned

R package
MOPEX
tPI
RootSig
JADE
PI2
hANN
sANN
TA

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