Meteorological drought analysis using data-driven models for the Lakes District, Turkey

被引：41

作者：

Keskin, M. Erol ^{[1
]}

Terzi, Oezlem ^{[2
]}

Taylan, E. Dilek ^{[1
]}

Kucukyaman, Derya ^{[1
]}

机构：

[1] Suleyman Demirel Univ, Dept Civil Engn, Engn Architecture Fac, TR-32260 Isparta, Turkey

[2] Suleyman Demirel Univ, Dept Struct Educ, Tech Educ Fac, TR-32260 Isparta, Turkey

来源：

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES | 2009年 / 54卷 / 06期

关键词：

drought; standardized precipitation index (SPI); adaptive neural-based fuzzy inference system (ANFIS); fuzzy logic; the Lakes District; Turkey; TIME RESERVOIR OPERATION; FUZZY-LOGIC; LOGLINEAR MODELS; NEURAL-NETWORK; SPI; PROPAGATION; ALGORITHM; RUNOFF;

D O I：

10.1623/hysj.54.6.1114

中图分类号：

TV21 [水资源调查与水利规划];

学科分类号：

081501 ;

摘要：

Droughts may be classified as meteorological, hydrological or agricultural. When meteorological drought appears in a region, agricultural and hydrological droughts follow. In this study, the standardized precipitation index (SPI) was applied for meteorological drought analysis at nine stations located around the Lakes District, Turkey. Analyses were performed on 3-, 6-, 9- and 12-month-long data sets. The SPI drought classifications were modelled by Adaptive Neural-Based Fuzzy Inference System (ANFIS) and Fuzzy Logic, which has the advantage that, in contrast to most of the time series modelling techniques, it does not require the model structure to be known a priori. Comparison of the observed values and the modelling results shows a better agreement with SPI-12 and ANFIS models than with fuzzy logic models.

引用

页码：1114 / 1124

页数：11

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