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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F16%3A70194" target="_blank" >RIV/60460709:41330/16:70194 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1155/2016/3868519" target="_blank" >http://dx.doi.org/10.1155/2016/3868519</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1155/2016/3868519" target="_blank" >10.1155/2016/3868519</a>

Result language
angličtina
Original language name
Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks
Original language description
The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feed forward 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) andwere 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 feed forward multilayer perceptron with one hidden layer of neurons.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
DA - Hydrology and limnology
OECD FORD branch
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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