Neural computation in paleoclimatology: General methodology and a case study

被引：10

作者：

Carro-Calvo, L. ^{[1
]}

Salcedo-Sanz, S. ^{[1
]}

Luterbacher, J. ^{[2
]}

机构：

[1] Univ Alcala, Dept Signal Proc & Commun, Alcala De Henares, Spain

[2] Univ Giessen, Dept Geog, Giessen, Germany

来源：

NEUROCOMPUTING | 2013年 / 113卷

关键词：

Paleoclimatology; Neural networks; Climate reconstruction; CLIMATE RECONSTRUCTION; WINTER PRECIPITATION; LAST-MILLENNIUM; NETWORKS; EUROPE; PROXY;

D O I：

10.1016/j.neucom.2012.12.045

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present the general methodology and main issues related to the application of neural networks to paleoclimatic reconstruction problems. We establish the basic methodological framework, data selection, organization and their relation to neural networks features. We also describe a skill score to compare regressors' performance and finally the paleoclimatic variable's reconstruction. We show a case study focused on winter precipitation reconstruction in the Mediterranean back to 1700, using multi-layer perceptrons, and the comparison of the obtained results to that of the existing alternative methodologies. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：262 / 268

页数：7

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