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. 2022 Sep 28:2022:7104752.
doi: 10.1155/2022/7104752. eCollection 2022.

Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms

Charles Onyutha  1
Affiliations

Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms

Charles Onyutha. J Environ Public Health. .

Abstract

Applicability of statistical models in predicting chlorine decay remains minimally explored. This study predicted residual chlorine using six deep learning and nine machine learning techniques. Suitability of multimodel ensembles (MMEs) including arithmetic mean of all the models (Ens1), average of the best three performing models (Ens2), and weighted mean of outputs from all the 15 models was investigated. A total of nine "goodness-of-fit" measures (such as distance correlation (r d) and Taylor skill score) were used to rank the models. The two best deep learning methods were the nonlinear autoregressive model with exogenous input (NARX) (r d = 0.51) and feedforward backpropagation (FFB) (r d = 0.61). The two best machine learning algorithms included random forests (RF) (r d = 0.64) and Gaussian process regression (GPR) (r d = 0.59). Eventually, Ens2 was obtained using RF, FFB, and GPR. Ens2 performed better than Ens1 and Ens3. The amount of variance explained by individual models and MMEs was over the ranges of 13-66% and 51-74%, respectively. Ens2 explained 74% of the total variance in observed residual chlorine. Remarkably, the appropriateness of the MMEs depends on the approach for combining model outputs, and the number of models considered. This study demonstrated the acceptability of statistical MMEs in predicting chlorine residual concentration in drinking water.

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Conflict of interest statement

The author declares no conflict of interest and no competing financial interests.

Figures

Figure 1
Figure 1
Values of “goodness-of-fit” metrics for (a) training, (b) validation, and (c) entire data.
Figure 2
Figure 2
Error analysis based on (a) histogram and (b) boxplots.
Figure 3
Figure 3
Observed versus modelled RCC based on (a)–(f) deep learning and (g)–(o) machine learning methods.
Figure 4
Figure 4
Performance of models considering (a) machine learning, (b) deep learning, and (c) combination of both machine learning and deep learning methods.
Figure 5
Figure 5
Overall performance of models.
Figure 6
Figure 6
Observed versus modelled RCC for ensemble model.
See this image and copyright information in PMC

References

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