GENETIC PROGRAMMING: A NEW PARADIGM IN RAINFALL RUNOFF MODELING 1
By Shie-Yui Liong, T. Gautam, Soon-Thiam Khu, Vladan Babovic, Maarten Keijzer, Nitin Muttil. 2002
113 citations recorded by the source corpus.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/dbf433c0a82057a5bf9d92e1a284712e9bdcf366
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In Environmental Engineering
In Global and Planetary Change
In Water Science and Technology
Citations in the corpus
- A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series (2009, methodology): “GP model was used to emulate the rainfall-runoff process (Whigam and Crapper, 2001) and was evaluated in terms of root mean square error and correlation coefficient (Liong et al., 2002; Whigam and Crapper, 2001).”
- Forecasting energy consumption using a grey model improved by incorporating genetic programming (2011, background): “GP is also extensively employed in practical applications, such as predicting coastal algal blooms [19], building credit scoring models [2,3] and modeling the rainfall-runoff process [20].”
- Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir (2014, background): “GP has become popular in hydrologic forecasting and has been successfully applied (Sheta and Mahmoud 2001; Liong et al. 2002; Aytek and Alp 2008; Kisi and Shiri 2011).”