Abstract

In this study, wavelet transform (W), which is one of the data pre-processing techniques, adaptive neural-based fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural networks (ANNs) were used to develop the drought estimation models of Çanakkale, Turkey. For these models, 3-, 6-, 9- and 12-months drought indices were calculated by standard precipitation index (SPI) and by using precipitation data of Çanakkale, Gökçeada and Bozcaada stations between 1975 and 2010 years. Firstly, ANFIS, SVM and ANNs models were developed to estimate calculated drought indices. Then SPI values of Gökçeada and Bozcaada stations were divided into sub-series by wavelet transform technique and these sub-series were used as input in W-ANFIS, W-SVM and W-ANNs models. When the developed models were compared, it was determined that the hybrid models developed by using preprocessing technique performed better. Among these models, it was observed that the W-ANFIS model gave the best results for 6-months period.

Research Highlights

• Calculating of 3-, 6-, 9- and 12- months meteorological drought index with SPI

• Developing ANFIS, SVM and ANNs drought models using SPI values

• Decomposition of SPI values into sub-series by wavelet transform technique and developing hybrid drought models (W-ANFIS, W-SVM and W-ANNs) using subseries of SPI values

• Comparing ANFIS, SVM and ANNs models with hybrid models

• Obtaining appropriate results with hybrid models in meteorological drought estimation

中文翻译：

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气象干旱估计中的混合小波-人工智能模型

摘要

在这项研究中，小波变换（W）是数据预处理技术之一，它是基于自适应神经网络的模糊推理系统（ANFIS），支持向量机（SVM）和人工神经网络（ANN）来开发的。土耳其恰纳卡莱干旱评估模型。对于这些模型，通过标准降水指数（SPI）以及1975年至2010年之间的Çanakkale，Gökçeada和Bozcaada站的降水数据，计算了3、6、9和12个月的干旱指数。首先，开发了ANFIS，SVM和ANNs模型来估算干旱指数。然后，通过小波变换技术将Gökçeada和Bozcaada站的SPI值划分为子系列，并将这些子系列用作W-ANFIS，W-SVM和W-ANNs模型的输入。比较开发的模型时，确定使用预处理技术开发的混合模型性能更好。在这些模型中，可以观察到W-ANFIS模型在6个月内给出了最佳结果。

研究重点

• 用SPI计算3、6、9和12个月的气象干旱指数

• 使用SPI值开发ANFIS，SVM和ANNs干旱模型

• 通过小波变换技术将SPI值分解为子序列，并使用SPI值的子序列开发混合干旱模型（W-ANFIS，W-SVM和W-ANN）

• 将ANFIS，SVM和ANNs模型与混合模型进行比较

• 用混合模型在气象干旱估计中获得适当的结果