Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{ABBA:2020:JH,

• author = "S. I. Abba and Sinan Jasim Hadi and Saad Sh. Sammen and Sinan Q. Salih and R. A. Abdulkadir and Quoc Bao Pham and Zaher Mundher Yaseen",
• title = "Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination",
• journal = "Journal of Hydrology",
• volume = "587",
• pages = "124974",
• year = "2020",
• ISSN = "0022-1694",
• DOI = "doi:10.1016/j.jhydrol.2020.124974",
• URL = "http://www.sciencedirect.com/science/article/pii/S0022169420304340",
• keywords = "genetic algorithms, genetic programming, Water quality index, Watershed management, Extreme Gradient Boosting, Extreme Learning Machine, Kinta River",
• abstract = "Anthropogenic activities affect the water bodies and result in a drastic reduction of river water quality (WQ). The development of a reliable intelligent model for evaluating the suitability of water remains a challenging task facing hydro-environmental engineers. The current study is investigated the applicability of Extreme Gradient Boosting (XGB) and Genetic Programming (GP) in obtaining feature importance, and then abstracted input variables were imposed into the predictive model (the Extreme Learning Machine (ELM)) for the prediction of water quality index (WQI). The stand-alone modeling schema is compared with the proposed hybrid models where the optimum variables are supplied into the GP, XGB, linear regression (LR), stepwise linear regression (SWLR) and ELM models. The WQ data is obtained from the Department of Environment (DoE) (Malaysia), and results are evaluated in terms of determination coefficient (R2) and root mean square error (RMSE). The results demonstrated that the hybrid GPELM and XGBELM models outperformed the standalone GP, XGB, and ELM models for the prediction of WQI at Kinta River basin. A comparison of the hybrid models showed that the predictive skill of GPELM (RMSE = 3.441 training and RMSE = 3.484 testing) over XGBELM improving the accuracy by decreasing the values of RMSE by 5percent and 9percent for training and testing, respectively with regards to XGBELM (RMSE = 3.606 training and RMSE = 3.816 testing). Although regressions are often proposed as reference models (LR and SWLR), when combined with computational intelligence, they still provide satisfactory results in this study. The proposed hybrid GPELM and XGBELM models have improved the prediction accuracy with minimum number of input variables and can therefore serve as reliable predictive tools for WQI at Kinta River basin",

}

Genetic Programming entries for S I Abba Sinan Jasim Hadi Saad Sh Sammen Sinan Q Salih Rabiu A Abdulkadir Quoc Bao Pham Zaher Mundher Yaseen

Citations