Forecasting daily streamflow using hybrid ANN models

By Wen Wang, P. Gelder, J. Vrijling, Jun Ma. 2006

283 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/1bd1a99a2bc5412b3028dd36b4eed38c4f04d33e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Kwok-Wing Chau
2. V. Smakhtin
3. C. Fan
4. Jianzhou Wang
5. C. Wu
6. Y. P. Li
7. Yan Li
8. Zhen-hai Guo

In Environmental Engineering

1. Kwok-Wing Chau
2. C. Wu
3. C. Fan
4. V. Smakhtin
5. Jianzhou Wang
6. Y. P. Li
7. Yan Li
8. Guohe Huang

In Water Science and Technology

1. C. Wu
2. Kwok-Wing Chau
3. C. Fan
4. Guohe Huang
5. Yan Li
6. Y. P. Li
7. V. Smakhtin
8. Yurui Fan

Citations in the corpus

• A case study on a hybrid wind speed forecasting method using BP neural network (2011, background): “Although there are many similar forecasting approaches in which neural networks have been built on deseasonalised time series data, they have mainly been used to predict aggregate retail sales [8], temperature [7], streamflow [27], total production value and soft drink [25] and total new privately o”
• Drought forecasting using artificial neural networks and time series of drought indices (2007, methodology): “Wang et al. (2006) used ANN to forecast daily streamflow from streamflow records alone, without employing exogenous variables of runoff generating process such as rainfall.”
• Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques (2009, methodology): “However, some doubts have been raised in terms of the existence of chaos in hydrologic data [Ghilardi and Rosso, 1990; Koutsoyiannis and Pachakis, 1996; Pasternack, 1999; Schertzer et al., 2002; Wang et al., 2006a].”
• Monthly runoff forecasting based on LSTM–ALO model (2018, methodology): “Wang et al. (2006) selected hybrid ANNs as univariate time series models to forecast runoff series.”
• Prediction of rainfall time series using modular soft computingmethods (2013, methodology): “The soft split means the dataset can be overlapped and the overall forecasting output is the weightedaverage of each local model (Shrestha and Solomatine, 2006; Zhang and Govindaraju, 2000; Wang et al., 2006; Wu et al., 2008).”
• A stepwise-cluster forecasting approach for monthly streamflows based on climate teleconnections (2015, background): “Generally, these models can be categorized as processdriven methods and data-driven methods (Wang et al. 2006; Dehghani et al. 2014).”
• Methods to improve neural network performance in daily flows prediction (2009, methodology): “, 1993) is another commonly used method to identify model inputs, which is for the perspective of dynamics reconstruction of a system (Wang et al., 2006).”
• Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques (2010, methodology): “…the hard splitting and the final forecasting output is explicitly from only 81  one of local models (See and Openshaw, 2000; Hu et al., 2001; Solomatine and Xue, 2004; 82  Sivapragasam and Liong, 2005; Jain and Srinivasulu, 2006; Wang et al., 2006; Corzo and 83  Solomatine, 2007; Lin and Wu, 2009).”