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

By Vahid Nourani, A. H. Baghanam, Jan Adamowski, Ozgur Kisi. 2014

675 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/2005e33abca73dc70eb9d4afd162d6a5e284c071

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In Electrical and Electronic Engineering

In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “…ANNs [66], neuro‐fuzzy [67] adaptive neuro‐fuzzy inference systems (ANFIS) [68], support vector machines (SVM) [69], wavelet neural networks (WNN) [70], and multilayer perceptron (MLP In the following subsections, a brief description and background of these fundamental ML algorithms are presented.”
A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, background): “Previous reviews [20,27,29] about ANNs are more concerned about the water quantity (e.”
A multivariate conditional model for streamflow prediction and spatial precipitation refinement (2015, methodology): “ANFIS combines the advantages of both ANNs and fuzzy systems within a single framework [Nourani et al., 2011]. A detailed description of ANFIS can be found in Jang [1993]. The appropriate ANFIS parameters (e.g., type and number of membership functions) are determined by a trial-and-error method [Nou”
Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting (2015, background): “2011), most of the models assume that the runoff time series is stationary (Li et al. 2013; Nourani et al. 2014; Lafdani et al. 2013).”
New insights into soil temperature time series modeling: linear or nonlinear? (2019, methodology): “The number of decomposition levels for the wavelet transform was calculated as follows (Nourani et al. 2014):”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, background): “However, wavelets are preferred because there is ample research supporting their use in hydrology (Nourani et al. 2009, 2014) and they offer flexibility to the user since one may choose between many different wavelet basis functions.”
Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (2018, methodology): “Nourani et al. (2014) proposed a wavelet neural network (WNN) as a successful approach compared to bootstrap NN and to a standard ANN.”
Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling (2016, background): “…(response variable) and a set of potential drivers (candidate input variables or explanatory variables) that may contain relevant, irrelevant, and/or redundant information (e.g., Campisi et al., 2012; Rathinasamy et al., 2013; Belayneh et al., 2014; Nourani et al., 2014; Belayneh et al., 2016).”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “Similar gray-box approaches have been gaining popularity in various other ﬁelds too (e.g., hydrology [337], material forming [338], bioprocess [339], built environment [340], petroleum [341], reactor design [342] and quantum chemistry [343]).”
Forecasting of Multi-Step Ahead Reference Evapotranspiration Using Wavelet- Gaussian Process Regression Model (2018, methodology): “The following equation was used to select discrete wavelet transform decomposition level (Nourani et al. 2014).”