Methods to improve neural network performance in daily flows prediction

By C. Wu, Kwok-Wing Chau, Yan Li. 2009

310 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/ad6b3b1ae9fa7cc865b1a7b8fed123df245d357d

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In Ecology

In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “The wavelet function utilised in this study was adopted from the family of Daubechies mother wavelets (Nourani et al. 2009; Wu et al. 2009) where the DWT process operated as two sets of functions with a high-pass and a low-pass filter.”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “In another study, Wu et al. (2009) explored the efficiency of various methods in improving the ANN performance in daily flow prediction.”
Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques (2010, methodology): “A comparison between WA and SSA in Wu et al. (2009) indicated 116 that SSA performed better than WA.”
Comparison of methods for non-stationary hydrologic frequency analysis: Case study using annual maximum daily precipitation in Taiwan (2017, methodology): “In some studies, ANNs have been combined with external covariate climatic indices (Wu et al., 2009; Chen et al., 2015; Gholami et al., 2015).”
Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. (2010, background): “…be explored in greater detail: (1) lead times greater than 1 day but less than 1 month need to be explored in greater detail since the only study (Wu et al., 2009) that explored lead times greater than 1 day but less than 1 month found that the WA–ANN models did not perform as well as other…”
Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods (2016, unknown): “, the projection pursuit technique [20] and neural networks [21,22]), multivariate statistical techniques [23] (e.”