Neural network and genetic programming for modelling coastal algal blooms

By Nitin Muttil, Kwok-Wing Chau. 2006

364 citations recorded by the source corpus.

Subfields: Environmental Engineering, Oceanography, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/aef63b5e4d2eca0235471b8c59513b2b5f11d530

How researchers see this paper

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

In Environmental Engineering

1. Kwok-Wing Chau
2. C. Wu
3. Riccardo Taormina
4. Rajandrea Sethi
5. Yan Li
6. Ozgur Kisi
7. Jan Adamowski
8. Hossein Bonakdari

In Oceanography

1. C. Wu
2. Kwok-Wing Chau

In Water Science and Technology

1. C. Wu
2. Kwok-Wing Chau
3. Yan Li
4. Riccardo Taormina
5. Rajandrea Sethi
6. Ozgur Kisi
7. Jan Adamowski
8. Xinqiu Zhao

Citations in the corpus

• Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon (2012, background): “…for time series modelling in many hydrological contexts such as rainfallrunoff (Dawson and Wilby, 1998; Hsu et al., 1995), river flow (Cheng et al., 2005; Joorabchi et al., 2007), flood forecasting (Chau et al., 2005), and water quality modelling (Muttil and Chau, 2006; May and Sivakumar, 2009).”
• A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series (2009, neutral): “The ANNs have been shown to give useful results in many fields of hydrology and water resources research (Campolo et al., 2003; Chau, 2006; Muttil and Chau, 2006).”
• Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques (2013, background): “Thus, for “data rich, theory poor” instances, GP may offer advantages over other techniques since GP can self-modify, through the genetic loop, a population of function trees in order to finally generate an “optimal” and physically interpretable model (Muttil and Chau 2006).”
• Methods to improve neural network performance in daily flows prediction (2009, background): “The issue of lagged predictions in the ANN model has been 48 mentioned by some researchers (Dawson and Wilby, 1999; Jian and Srinivasulu, 2004; de 49 Vos and Rientjes, 2005; Muttil and Chau, 2006).”
• Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques (2009, neutral): “In addition, the issue of lagged predictions in the ANN model has been mentioned by some researchers [Dawson and Wilby, 1999; Jain and Srinivasulu, 2004; de Vos and Rientjes, 2005; Muttil and Chau, 2006 ].”
• Prediction of rainfall time series using modular soft computingmethods (2013, background): “In addition, the issue of lagged predictions in the ANN model was mentioned by some researchers (Dawson and Wilby, 2001; Jain and Srinivasulu, 2004; De Vos and Rientjes, 2005; Muttil and Chau, 2006).”