Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds.

By Jan Adamowski, K. Sun. 2010

414 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/266e6d0d7266831256dd4f841979900e86b9c2eb

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In Electrical and Electronic Engineering

In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Monthly runoff forecasting based on LSTM–ALO model (2018, methodology): “Data-driven methods are fundamentally black-box methods, which are convenient as they can be applied easily without considering the internal physical mechanism of watershed system (Adamowski and Sun 2010).”
Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models (2014, background): “, 1999), to evaluate rainfall-runoff models (Lane, 2007), to forecast river flow (Adamowski, 2008; Adamowski and Sun, 2010), to forecast groundwater levels (Adamowski and Chan, 2011), to forecast urban water demand (Adamowski et al.”
Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression (2021, background): “This happens because WT decomposes complex time series by catching information on different levels, which results in a more effective structure for machine learning methods (Adamowski & Sun, 2010; Nourani et al., 2009; Zhou et al., 2020).”
A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework (2018, background): “However, data-driven models are rapidly developed and widely used because of their simplicity, minimal data requirements, and ease of real-time implementation (Zhang et al. 2015; Adamowski and Sun 2010; Sahay and Srivastava 2014).”
Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting (2014, methodology): “Therefore, it may be an ideal tool for watersheds where other climatological and hydrogeological data are limited, and where it is more important to provide precise forecasts than understand physical catchment processes (Adamowski, 2008; Adamowski and Sun, 2010).”
Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (2018, methodology): “Adamowski and Sun (2010) employed a hybrid WT-NN method for flow prediction in terms of single and ternary days.”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, background): “Some studies have used all of their wavelet sub-series (Adamowski and Sun 2010; Nourani et al. 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…”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, background): “Adamowski and Sun (2010) coupled DWT and ANN for flow forecasting in non-perennial rivers in semi-arid watersheds.”
Multi-models for SPI drought forecasting in the north of Haihe River Basin, China (2017, methodology): “Multilayer perceptron has been applied widely in drought forecasting because of their simplicity (Mishra and Desai 2006; Bacanli et al. 2008; Kim and Valdes 2003; Adamowski and Sun 2010).”
Wavelet Genetic Algorithm-Support Vector Regression (Wavelet GA-SVR) for Monthly Flow Forecasting (2015, background): “The original times series is passed through high-pass and low-pass filters, and detailed coefficients and approximation series are obtained (Adamowski and Sun 2010).”
Wavelet Bootstrap Multiple Linear Regression Based Hybrid Modeling for Daily River Discharge Forecasting (2014, background): “…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 Sehgal 2013; Sahay and Srivastava 2014; Sehgal et al. 2014).”