NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS

By Tae‐Woong Kim, Juan B. Valdes. 2003

455 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/8c71370067992aee5a7f2906c8dc65b2f3162967

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In Electrical and Electronic Engineering

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Multi-models for SPI drought forecasting in the north of Haihe River Basin, China (2017, methodology): “The hidden layer contains the neuron-like processing unit that connected with the input and output layers (Kim and Valdes 2003) is formed by: y0k ¼ f0 Xm j¼1 xkj fn Xn i¼1 xjixiðtsÞ þ ðxj0Þ ! þ xk0 " # ð14Þ where n = ‘‘the number of input variables’’; m = ‘‘the number of hidden neurons’’; xi(t) =…”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “Kisi and Cimen (2012) used a joint wavelet–SVM model for the prediction of daily precipitation and found that the hybrid method could increase the forecasting accuracy of one-day-ahead precipitation better than single SVM and ANN models.”
Predicting Water Level Fluctuations in Lake Michigan-Huron Using Wavelet-Expert System Methods (2014, background): “Many applications on wavelet combination models have been studied in the literature (Kim and Valdes 2003; Kucuk et al. 2009; Webster and Hoyos 2004; Nourani et al. 2009; Ozger 2010; Shiri and Kisi 2010; Shiri et al. 2011).”
Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models (2014, methodology): “Furthermore, a study by Kim and Valdes (2003) found the ‘a trous’ wavelet to be more effective for forecasting studies than the Morlet or Daubechies wavelets.”
Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (2018, background): “Kim and Valdés (2003) employed neural networks and dyadic-wavelet transforms in order to predict the droughts in the Conchos River Basin in Mexico City.”
A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation (2009, background): “Kim and Valdes (2003) proposed a conjunction model based on dyadic wavelet transforms and ANNs to forecast droughts for Conches river basin in Mexico; they used ANN to forecast sub-signals from wavelet decomposition and also to reconstruct the main signal from the forecasted sub-signals.”
Wavelet Bootstrap Multiple Linear Regression Based Hybrid Modeling for Daily River Discharge Forecasting (2014, methodology): “The wavelet based hybrid models have been successfully employed in many hydrology and water resources studies (Wang and Ding 2003; Kim and Valdes 2003; Anctil and Tape 2004; Partal and Cigizoglu 2008; Zhou et al. 2008; Adamowski and Sun 2010; Adamowski et al. 2012; Kisi and Shiri 2012; Sahay and…”
Soil Water Simulation and Predication Using Stochastic Models Based on LS-SVM for Red Soil Region of China (2011, methodology): “Kim and Valdes (2003) used wavelet multi-scale decomposed signals as inputs of neural network models and Ben Mabrouk et al. (2008) make a wavelet decomposition to analyzed time series, which can provide the models with”
Hydrological Drought Class Transition Using SPI and SRI Time Series by Loglinear Regression (2015, background): “…areal extent and severity of various drought episodes (Palmer 1967) and to investigate the spatial and temporal drought characteristics (Soule 1993) as well as to explore the periodic behavior of droughts (Rao and Padmanabhan 1984), and drought forecasting (Kim and Valdes 2003; Özger et al. 2009).”
Wavelet Analysis-Support Vector Machine Coupled Models for Monthly Rainfall Forecasting in Arid Regions (2015, background): “Wavelet analysis can also provide useful decompositions of original time series, so the wavelet-transformed data aids to the ability of a forecasting model to capture useful information at different resolution levels (Kim and Valdes 2003).”