Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review

Nourani, Vahid; Baghanam, Aida Hosseini; Adamowski, Jan; Kisi, Özgür

doi:10.1016/j.jhydrol.2014.03.057

Cited by 635 publications

(238 citation statements)

References 146 publications

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“…Results of this study show that the reason for considering wavelet transform is the benefit and usefulness of multilateral resolution analysis, the removal of signal-related disorders, and the powerful capability of artificial intelligence in optimization, versatility, and estimation of processes (Nourani et al 2014). Different models were compared based on WANN for rainfall-runoff modeling.…”

Section: Introductionmentioning

confidence: 95%

A new approach to flow simulation using hybrid models

et al. 2017

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The necessity of flow prediction in rivers, for proper management of water resource, and the need for determining the inflow to the dam reservoir, designing efficient flood warning systems and so forth, have always led water researchers to think about models with highspeed response and low error. In the recent years, the development of Artificial Neural Networks and Wavelet theory and using the combination of models help researchers to estimate the river flow better and better. In this study, daily and monthly scales were used for simulating the flow of Gamasiyab River, Nahavand, Iran. The first simulation was done using two types of ANN and ANFIS models. Then, using wavelet theory and decomposing input signals of the used parameters, sub-signals were obtained and were fed into the ANN and ANFIS to obtain hybrid models of WANN and WANFIS. In this study, in addition to the parameters of precipitation and flow, parameters of temperature and evaporation were used to analyze their effects on the simulation. The results showed that using wavelet transform improved the performance of the models in both monthly and daily scale. However, it had a better effect on the monthly scale and the WANFIS was the best model.

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Section: Introductionmentioning

confidence: 95%

A new approach to flow simulation using hybrid models

et al. 2017

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“…The basic idea of data-driven models is to use certain mathematical tools to describe correlations of hydrological variables, without requiring modeling of the internal structure of a watershed system [4,6,7]. An important and extensively accepted viewpoint about these models is that accurate identification of characteristics in hydrological time series is the basis of forecasting by the data-driven models [8,9]. However, many data-driven models cannot fully meet these needs.…”

Section: Introductionmentioning

confidence: 99%

Discussion on the Choice of Decomposition Level for Wavelet Based Hydrological Time Series Modeling

Sang

Liu

Wang

2016

Water

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Abstract:The combination of wavelet analysis methods with data-driven models is a prevalent approach to conducting hydrological time series forecasting, but the results are affected by the accuracy of the wavelet decomposition of the series. The choice of decomposition level is one of the key factors for the wavelet decomposition. In this paper, the data of daily precipitation and streamflow time series measured in the upper reach of the Heihe River Basin in Northwest China were used as an example, and the influence of the decomposition level on wavelet-based hydrological time series forecasting was investigated. The true components of the precipitation series were identified, and the modeling results using 10 decomposition levels and two decomposition types were compared. The results affirmed that the wavelet-based modeling performance is sensitive to the choice of decomposition level, which is determined by the time series analyzed, but has no relation with the decomposition type used. The essence of the choice of decomposition level is to reveal the complex variability of hydrological time series under multi-temporal scales, and first knowing the true components of series could guide the choice of decomposition level. Through this study, the relationship among original series' characteristics, the choice of decomposition level, and the accuracy of wavelet-based hydrological time series forecasting can be more clearly understood, and it can be an improvement for wavelet-based data-driven modeling.

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“…Hydrological time series analysis and forecasting is an effective approach to determine the variability of hydrological processes and predict future values. During recent years wavelet modeling has become popular for hydrological time series forecasting because the wavelet analysis method has the superiority of handling the nonstationary variability of hydrological processes (Nourani et al 2014;Sang et al 2015). For wavelet model inputs, the original hydrological series are usually decomposed into a set of subsignals by continuous or discrete wavelet method, called data preprocessing.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, these two issues constitute the essential basis of all wavelet analyses. Although there are a multitude of relevant studies (Sang 2012;Nourani et al 2014;Shoaib et al 2014), these issues have not been completely resolved, and there is no universal method for the choice of mother wavelet and temporal scale yet.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Hydrological Time Series Forecasting

et al. 2016

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These days wavelet analysis is becoming popular for hydrological time series simulation and forecasting. There are, however, a set of key issues influencing the wavelet-aided data preprocessing and modeling practice that need further discussion. This article discusses four key issues related to wavelet analysis: discrepant use of continuous and discrete wavelet methods, choice of mother wavelet, choice of temporal scale, and uncertainty evaluation in wavelet-aided forecasting. The article concludes with a personal reflection on solving the four issues for improving and supplementing relevant wavelet studies, especially wavelet-based artificial intelligence modeling.

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Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review

Cited by 635 publications

References 146 publications

A new approach to flow simulation using hybrid models

A new approach to flow simulation using hybrid models

Discussion on the Choice of Decomposition Level for Wavelet Based Hydrological Time Series Modeling

Wavelet-Based Hydrological Time Series Forecasting

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