River Water Quality Prediction in Malaysia Based on Extra Tree Regression Model Coupled with Linear Discriminant Analysis (LDA)

2021 ◽  
pp. 1491-1496
Author(s):  
Danny Hartanto Djarum ◽  
Zainal Ahmad ◽  
Jie Zhang
Keyword(s):  
Water Quality ◽  
Discriminant Analysis ◽  
Regression Model ◽  
Linear Discriminant Analysis ◽  
River Water ◽  
River Water Quality ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Linear Discriminant ◽  
Tree Regression Model

CALHIDRA 3.0 – New software application for river water quality prediction based on RWQM1

2011 ◽  
Vol 26 (7) ◽  
pp. 973-979 ◽  
Author(s):  
C.M. Cardona ◽  
C. Martin ◽  
A. Salterain ◽  
A. Castro ◽  
D. San Martín ◽  
...  
Keyword(s):  
Water Quality ◽  
River Water ◽  
River Water Quality ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Software Application

A hybrid regression model for water quality prediction

OPSEARCH ◽  
2019 ◽  
Vol 56 (4) ◽  
pp. 1167-1178 ◽  
Author(s):  
Tanujit Chakraborty ◽  
Ashis Kumar Chakraborty ◽  
Zubia Mansoor
Keyword(s):  
Water Quality ◽  
Regression Model ◽  
Quality Prediction ◽  
Water Quality Prediction

scholarly journals Universally consistent hybrid regression model for water quality prediction

2018 ◽  
Author(s):  
TANUJIT CHAKRABORTY ◽  
Zubia Mansoor ◽  
Ashis Kumar Chakraborty
Keyword(s):  
Water Quality ◽  
Regression Model ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Paper Machine ◽  
Quality Prediction ◽  
Boiler Water ◽  
Produce Water ◽  
Water Quality Prediction

In this work, we propose a hybrid regression model to solve a specific problem faced by a modern papermanufacturing company. Boiler inlet water quality is a major concern for the company since it helps toproduce power and steam for the paper machine. If water treatment plant can not produce water of desiredquality as specified by the boiler, then it results in poor health of the boiler water tube and consequentlyaffects the quality of the paper. Variation in inlet water quality of the boiler is due to several crucial processparameters. We build a hybrid regression model for boiler water quality prediction based on decision treesand artificial neural networks. This model can be useful for manufacturing process quality improvementfor the paper company. We have proved the desired statistical consistency of the hybrid model to showits robustness and universal use. The primary advantage of the model is its natural interpretability andexcellent performance when compared with other state-of-the-art.

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