Extremely randomized tree: a new machines learning method for predicting coagulant dosage in drinking water treatment plant

Heddam, Salim

doi:10.1016/b978-0-12-820644-7.00013-x

Cited by 9 publications

(4 citation statements)

References 45 publications

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“…The regions cover many parts of the world and methods include ML and neural network methods. Salim-Heddam's 16,26,44 experimental research regarded coagulation dosage prediction coagulant dosage rate in Boudouaou DWPT, Algeria. It used TUR, PH, DO, CON, and temperature as input variable features and RF, MLR, ANFIS, and RBFNN methods to conduct experimental research, respectively.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

“…3,10,11 With the rapid development of ML algorithms, a favourable tool for precise dosing control is provided. [12][13][14][15] Heddam 16 proposed an extremely randomized tree, random forest (RF) and multiple linear regression (MLR) for predicting coagulant dosage in the Boudouaou drinking water treatment plant (DWPT). Wang 17 combined a genetic algorithm-based and particle swarm optimization technique with regression model analyses implemented to optimize the coagulation dosage.…”

Section: Introductionmentioning

confidence: 99%

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Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management

Fang

Zang

Zhai

et al. 2023

Environ. Sci.: Water Res. Technol.

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Section: Comparison With Existing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management

Fang

Zang

Zhai

et al. 2023

Environ. Sci.: Water Res. Technol.

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“…Both the ERT and RF models were highly accurate in both training and validation stages. The results indicate that the ERT model is the best choice for predicting coagulant dosage in drinking water treatment plants and has the potential to improve operational efficiency and effectiveness in water treatment processes [7]. In summary, these studies demonstrate the effectiveness of using artificial neural network and random forest for predicting optimal dosage of coagulant.…”

Section: Introductionmentioning

confidence: 74%

Prediction of Optimum Dosage of Coagulant in Water Treatment Plant: A Comparative Study between Artificial Neural Network and Random Forest

Nitin T. Sawalkar,

Sagar W. Jadhav,

Alpita A. Pawar

2024

Int Res J Adv Engg Hub

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Raw water, sourced directly from natural water bodies, is unsuitable for direct consumption due to the presence of various impurities. Therefore, it undergoes treatment at a Water Treatment Plant (WTP) before being supplied to the public. Preliminary treatment involves the removal of floating matter, through screening, while heavier particles settle out by gravity, fine particles remain in suspension, causing turbidity. Effective removal of these suspended particles requires coagulation to form flocs and facilitate the settling. Determining the optimal coagulant dosage is crucial, as both underdoing and overdosing of coagulant can lead to ineffective treatment and increased costs. Conventionally optimum dosage of coagulant is determined by performing jar test. This study focuses on predicting the optimum coagulant dosage using two soft computing techniques: Artificial Neural Network (ANN) and Random Forest (RF). The Input parameters for model development include turbidity, pH, temperature, and alkalinity of raw water from the Parvati Water Treatment Plant, Pune. In this study Four models were developed, namely Model A (Turbidity), Model B (pH, Alkalinity, Temperature, Turbidity), Model C (pH, Alkalinity, Temperature), and Model D (Alkalinity and Turbidity). These models were trained using ANN and RF. Predictions of optimum coagulant doses were made for the testing dataset, and model accuracy was evaluated using Scatter plots, Root Mean Squared Error (RMSE) and Coefficient of Correlation (R). Results indicate that RMSE values of ANN Models are comparatively lower than RF. Comparing among Models A, B, C, and D, Model B and Model D exhibit better performance, with lower RMSE values.

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“…In recent years, machine learning has also been adapted for water science studies, with many publications focusing on the applicability of algorithms for modelling water quality in different environments [513][514][515][516][517][518][519][520] and water treatment [521][522][523][524][525][526]. Machine learning methods have also been successfully applied in the identification, tracking, and removal of pollutants [527][528][529][530][531][532][533].…”

Section: Machine Learning Paradigmmentioning

confidence: 99%

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

Samborska-Goik,

Pogrzeba

2024

Applied Sciences

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The pollution of groundwater and soil by hydrocarbons is a significant and growing global problem. Efforts to mitigate and minimise pollution risks are often based on modelling. Modelling-based solutions for prediction and control play a critical role in preserving dwindling water resources and facilitating remediation. The objectives of this article are to: (i) to provide a concise overview of the mechanisms that influence the migration of hydrocarbons in groundwater and to improve the understanding of the processes that affect contamination levels, (ii) to compile the most commonly used models to simulate the migration and fate of hydrocarbons in the subsurface; and (iii) to evaluate these solutions in terms of their functionality, limitations, and requirements. The aim of this article is to enable potential users to make an informed decision regarding the modelling approaches (deterministic, stochastic, and hybrid) and to match their expectations with the characteristics of the models. The review of 11 1D screening models, 18 deterministic models, 7 stochastic tools, and machine learning experiments aimed at modelling hydrocarbon migration in the subsurface should provide a solid basis for understanding the capabilities of each method and their potential applications.

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Extremely randomized tree: a new machines learning method for predicting coagulant dosage in drinking water treatment plant

Cited by 9 publications

References 45 publications

Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management

Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management

Prediction of Optimum Dosage of Coagulant in Water Treatment Plant: A Comparative Study between Artificial Neural Network and Random Forest

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

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