Wavelet and neuro-fuzzy conjunction model for precipitation forecasting

By T. Partal, Ozgur Kisi. 2007

297 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/533c71f25cd1b7d9c8f7f9b2a65642d72fe4c5ba

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Citations in the corpus

Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models (2014, background): “The wavelet transform is a mathematical tool that provides a time–frequency representation of a signal in the time domain (Partal and Kisi, 2007).”
Methods to improve neural network performance in daily flows prediction (2009, methodology): “75 The applications of WMRA to precipitation and discharge were presented in the work of 76 Partal and Kişi (2007) and Partal and Cigizoglu (2008) respectively, where the WMRA was 77 applied to each model input variable.”
Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (2018, background): “Partal and Kişi (2007) developed a hybrid wavelet-neuro-fuzzy model in order to estimate the daily rainfall of three stations in Turkey.”
Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada (2012, methodology): “Wavelet transform analysis, developed during the last three decades, is often a more effective tool than the Fourier transform (FT) in studying nonstationary time series [ Partal and Kisi , 2007].”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, result): “2009; Wang and Ding 2003) whereas others have removed the db1 sub-series and added the remaining series, considering the former series as noise due to low correlation with their original data (Partal and Kişi 2007; Rajaee et al. 2010; Kisi and Cimen 2011).”
Wavelet Analysis-Support Vector Machine Coupled Models for Monthly Rainfall Forecasting in Arid Regions (2015, methodology): “Partal and Kişi (2007) applied WA-ANFIS model for precipitation forecast in Turkey.”
Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques (2010, methodology): “…Principal component analysis (PCA), wavelet analysis (WA), and 101 singular spectrum analysis (SSA), have been employed in hydrology field by researchers 102 (Sivapragasam et al., 2001; Marques et al., 2006; Hu et al., 2007; Partal and Kişi, 2007; 103 Sivapragasam et al., 2007;Wu et al., 2009).”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “A number of recent hydrological studies have implemented wavelet analysis (e.g., Adamowski and Sun, 2010; Kim and Valdes, 2003; Kisi, 2009a,b, 2010; Nourani et al., 2009a,b, 2011; Maheswaran and Khosa, 2012a; Partal and Kisi, 2007; Sang, 2012; Tiwari and Chatterjee, 2010; Zhou et al., 2008).”
Prediction of rainfall time series using modular soft computingmethods (2013, methodology): “ Hence techniques such as singular spectrum 10 analysis (SSA) were recently introduced to hydrology field by some researchers (Marques et 11 al., 2006; Partal and Kişi, 2007; Sivapragasam et al., 2001).”
Can deep learning beat numerical weather prediction? (2021, background): “First attempts at using such concepts in ML weather applications [95–98] were limited to simple NNs and limited complexity datasets.”
Assessment of drought trend and variability in India using wavelet transform (2020, methodology): “In the field of hydro-climatology, WT is used in time-series forecasting, cluster and trend analysis (Partal and Kişi 2007, Adamowski et al. 2009, Araghi et al. 2015, Joshi et al. 2016).”