Journal ArticleDOI
Estimation and forecasting of daily suspended sediment data using wavelet–neural networks
Turgay Partal,H. Kerem Cigizoglu +1 more
TLDR
In this paper, a combined wavelet-ANN method was proposed to estimate and predict the suspended sediment load in rivers by using measured data were decomposed into wavelet components via discrete wavelet transform, and the new wavelet series was used as input for the ANN model.About:
This article is published in Journal of Hydrology.The article was published on 2008-09-05. It has received 197 citations till now. The article focuses on the topics: Sediment transport & Wavelet.read more
Citations
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Journal ArticleDOI
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review
TL;DR: The present review focuses on defining hybrid modeling, the advantages of such combined models, as well as the history and potential future of their application in hydrology to predict important processes of the hydrologic cycle.
Journal ArticleDOI
A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling
TL;DR: In this paper, the wavelet analysis was linked to the ANN concept for modeling Ligvanchai watershed rainfall-runoff process at Tabriz, Iran, and the obtained results show the proposed model can predict both short and long term runoff discharges because of using multi-scale time series of rainfall and runoff data as the ANN input layer.
Journal ArticleDOI
Survey of computational intelligence as basis to big flood management: challenges, research directions and future work
Farnaz Fotovatikhah,Manuel Herrera,Shahaboddin Shamshirband,Kwok Wing Chau,Sina Ardabili,Md. Jalil Piran +5 more
TL;DR: This paper aims to present a comprehensive survey about the application of CI-based methods in FMSs and identifies and introduces the most promising approaches nowadays with respect to the accuracy and error rate for flood debris forecasting and management.
Journal ArticleDOI
Methods to improve neural network performance in daily flows prediction
TL;DR: Three data-preprocessing techniques, moving average (MA), singular spectrum analysis (SSA), and wavelet multi-resolution analysis (WMRA), were coupled with artificial neural network (ANN) to improve the estimate of daily flows and the ANN-MA model performed best and eradicated the lag effect.
Journal ArticleDOI
Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process
TL;DR: Two hybrid AI-based models which are reliable in capturing the periodicity features of the process are introduced for watershed rainfall–runoff modeling and show that the second model is relatively more appropriate because it uses the multi-scale time series of rainfall and runoff data in the ANFIS input layer.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Journal ArticleDOI
A Practical Guide to Wavelet Analysis.
TL;DR: In this article, a step-by-step guide to wavelet analysis is given, with examples taken from time series of the El Nino-Southern Oscillation (ENSO).
Journal ArticleDOI
The wavelet transform, time-frequency localization and signal analysis
TL;DR: Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied and the notion of time-frequency localization is made precise, within this framework, by two localization theorems.
Journal ArticleDOI
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Holger R. Maier,Graeme C. Dandy +1 more
TL;DR: The steps that should be followed in the development of artificial neural network models are outlined, including the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation.
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR)
TL;DR: In this paper, an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information is proposed for both the Japanese Islands and the Florida peninsula using GOES-8 and NEXRAD data.
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