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Lookup NU author(s): Dr Jie ZhangORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
The purpose of the work is to investigate the use of decision tree (DT) enhanced by bootstrap aggregates (Bag) and least-squares boosting (Lsboost) in modeling the organic matter of water according to its physicochemical parameters. An entire database of 500 samples of 21 physicochemical parameters, including organic matter, was used to build the DT, DT_Bag, and DT_Lsboost models. Training data (364 data points) is resampled using a bootstrap technique to form different training data sets to train different models. The models built were validated by a data set of 91 samples. The predicted outputs obtained from the developed DT models are then combined by simple averaging. On the other hand, the data was also boosted with the Lsboost technical aid to increase the strength of a weak learning algorithm. The model trains the first weak learner with equal weight across all data points in the training set, then trains all other weak learners based on the updated weight aimed at the validation result to minimize the squared error medium. Good agreement between the predicted and experimental organic matter concentrations for the DT_Lsboost model was obtained (the correlation coefficient for the validation dataset was 0.9992), followed by the DT-Bag model with a correlation coefficient of 0.9949. The comparison between DT, DT_Bag, and DT_Lsboost revealed the superiority of the DT_Lsboost model (the mean root of the squared errors for the dataset were 0.1295 for the DT_Lsboost, 0.1664 for the DT_Bag, and 0.5444 for the DT). These results show that Lsboost technology dramatically improved the DT model. This result is also confirmed by the results of tests on models (interpolation data of 45 points). It should also be noted that the Bag technique was also very effective in optimizing the DT model, as the results obtained with this technique were very close to the DT_Lsboost model.
Author(s): Tahraoui H, Amrane A, Belhadj AE, Zhang J
Publication type: Article
Publication status: Published
Journal: Environmental Technology & Innovation
Print publication date: 01/08/2022
Online publication date: 16/02/2022
Acceptance date: 08/02/2022
Date deposited: 08/02/2022
ISSN (print): 2352-1864
ISSN (electronic): 2352-1864
ePrints DOI: 10.57711/abz6-4e42
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