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使用集成人工神经网络预测标准化降水蒸散指数（SPEI）和标准化降水指数（SPI）干旱指标

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

作者信息

Maca Petr, Pech Pavel

机构信息

Department of Water Resources and Environmental Modeling, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamycka 1176, Suchdol, 165 21 Prague 6, Czech Republic.

出版信息

Comput Intell Neurosci. 2016;2016:3868519. doi: 10.1155/2016/3868519. Epub 2015 Dec 30.

DOI:10.1155/2016/3868519
PMID:26880875
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4736223/
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.

摘要

本文比较了基于两种不同人工神经网络模型的干旱指数预测。第一种模型基于前馈多层感知器，即sANN，第二种是集成神经网络模型，即hANN。分析的干旱指数是标准化降水指数（SPI）和标准化降水蒸发指数（SPEI），它们是针对1948 - 2002年期间美国两个集水区得出的。气象和水文数据来自MOPEX实验。两种神经网络模型的训练均采用差分进化的自适应版本JADE。模型比较基于六种模型性能指标。由四个模型性能指标值所解释的干旱指数预测结果表明，集成神经网络模型优于具有一个隐藏神经元层的前馈多层感知器。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca03/4736223/09e1b5c21f2d/CIN2016-3868519.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca03/4736223/d916b2f10e7b/CIN2016-3868519.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca03/4736223/cbbb1f629563/CIN2016-3868519.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca03/4736223/fa46ec60255e/CIN2016-3868519.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca03/4736223/3382503d2379/CIN2016-3868519.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca03/4736223/09e1b5c21f2d/CIN2016-3868519.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca03/4736223/d916b2f10e7b/CIN2016-3868519.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca03/4736223/cbbb1f629563/CIN2016-3868519.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca03/4736223/fa46ec60255e/CIN2016-3868519.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca03/4736223/3382503d2379/CIN2016-3868519.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca03/4736223/09e1b5c21f2d/CIN2016-3868519.005.jpg

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