Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques

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

414 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/9679af2bee04b7b7f74bc12479a3e7d639154b3c

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Reservoir flood control operation based on chaotic particle swarm optimization algorithm (2014, methodology): “With the development of the fusion of water conservancy engineering and computing science by artificial intelligence technology and intelligent optimization method [3–7], many scholars have begun to introduce various optimal algorithms into develop the study on the flood control operation [8–10].”
Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran (2018, methodology): “Wu et al. (2009) applied the methods of autoregressive (integrated moving) average (ARIMA), k-nearest neighbors (KNN), ANN, and SVR to streamflow prediction in China over the period 1974 to 2003, finding that the SVR method has the highest precision (with RMSE values of 376.6, 561.5, 299.7, and…”
Maximum entropy-Gumbel-Hougaard copula method for simulation of monthly streamflow in Xiangxi river, China (2015, neutral): “Wu et al. (2009) developed a crisp distributed support vectors regression (CDSVR) model for monthly streamflow forecasts.”
A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework (2018, background): “If the parameter is too small or too large, an under-fitting or over-fitting phenomenon can occur (Wu et al. 2009; Adamowski et al. 2012; Huang et al. 2014; Mahmoodi et al. 2014), which can increase the training error and decrease the generalization ability.”
Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020, methodology): “…auto regressive (AR) model, auto regressive moving average (ARMA) model, and auto regressive integrated moving average (ARIMA) model are linear models that have received wide application in hydrological time-series forecasting (Wu et al. 2009; Wu and Chau 2010; Valipour et al. 2013; Valipour 2015).”
Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir (2014, background): “Current applications of SSA in hydrology only selected the filtered series as model inputs without considering noise series (Sivapragasam et al. 2001; Marques et al. 2006; Wu et al. 2009).”
A hybrid support vector regression framework for streamflow forecast (2019, methodology): “Artificial Neural Network (ANN) is a widely used method for long-term simulation and forecast (Aksoy and Dahamsheh, 2009; Moeeni and Bonakdari, 2016; Wu and Chau, 2010; Wu et al., 2009).”