Faculty of Civil Engineering, University of Tabriz, 29 Bahman Ave., Tabriz 5166616471, Iran; Faculty of Civil and Environmental Engineering, Near East University, North Cyprus, Mersin 10, Nicosia 99138, Turkey.
Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran.
Environ Res. 2019 Jan;168:306-318. doi: 10.1016/j.envres.2018.10.012. Epub 2018 Oct 15.
In mountainous regions, rainfall can be extremely variable in space and time. The need to simulate rainfall time series at different scales on one hand and the lack of recording such parameters in small scales because of administrative and economic problems, on the other hand, disaggregation of rainfall time series to the desired scale is an essential topic for hydro-environmental studies of such mountainous regions. Hybrid models development by combining data-driven methods of least square support vector machine (LSSVM) and Artificial Neural Network (ANN) and wavelet decomposition for disaggregation of rainfall time series are the purpose of this paper. In this study, for disaggregating the Tabriz and Sahand rain-gauges time series, according to nonlinear characteristics of observed time scales, wavelet-least square support vector machine (WLSSVM) and wavelet-artificial neural network (WANN) hybrid models were proposed. For this purpose, daily data of four rain-gauges and monthly data of six rain-gauges from mountainous basin of the Urmia Lake for seventeen years were decomposed with wavelet transform and then using mutual information and correlation coefficient criteria, the sub-series were ranked and superior sub-series were used as input data of LSSVM and ANN models for disaggregating the monthly rainfall time series to the daily time series. Results obtained by these hybrid disaggregation models were compared with the results of LSSVM, ANN and classic multiple linear regression (MLR) models. The efficiency of WANN model with regard to the WLSSVM, ANN, LSSVM and MLR models at validation stage in the optimized case for Tabriz rain-gauge showed up to 9.1%, 22%, 20% and 50% increase and in the optimized case for Sahand rain-gauge showed up to 4.5%, 21.1%, 30.2% and 53.3% increase, respectively.
在山区，降雨在空间和时间上的变化非常大。一方面，需要在不同尺度上模拟降雨时间序列；另一方面，由于行政和经济方面的问题，在小尺度上无法记录这些参数，因此需要将降雨时间序列离散化为所需的尺度。本文的目的是通过结合最小二乘支持向量机（LSSVM）和人工神经网络（ANN）的数据驱动方法以及小波分解来开发混合模型，以离散化降雨时间序列。在这项研究中，为了离散化 Tabriz 和 Sahand 雨量计的时间序列，根据观测时间尺度的非线性特征，提出了小波-最小二乘支持向量机（WLSSVM）和小波-人工神经网络（WANN）混合模型。为此，利用小波变换对 17 年来 Urmia 湖山区的四个雨量计的日数据和六个雨量计的月数据进行了分解，然后利用互信息和相关系数准则对亚序列进行了排序，并将优势亚序列作为 LSSVM 和 ANN 模型的输入数据，用于将月降雨时间序列离散化为日时间序列。将这些混合离散模型的结果与 LSSVM、ANN 和经典多元线性回归（MLR）模型的结果进行了比较。在优化的 Tabriz 雨量计情况下，WANN 模型相对于 WLSSVM、ANN、LSSVM 和 MLR 模型在验证阶段的效率提高了 9.1%、22%、20%和 50%，在优化的 Sahand 雨量计情况下，提高了 4.5%、21.1%、30.2%和 53.3%。
