Machine learning algorithms for efficient water quality prediction

Azrour, Mourade; Mabrouki, Jamal; Fattah, Ghizlane; Guezzaz, Azedine; Aziz, Faissal

doi:10.1007/s40808-021-01266-6

Cited by 87 publications

(32 citation statements)

References 39 publications

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“…Mourad Azrour et al [26] investigated the efficiency of machine learning algorithms for evaluating WQI prediction value based on four water features: pH, temperature, turbidity, and coliforms. The experimental results have proven the efficiency of used regression algorithms in predicting WQI.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognizing Safe Drinking Water and Predicting Water Quality Index using Machine Learning Framework

Torky¹,

Bakhiet²,

Bakrey³

et al. 2023

IJACSA

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Water quality monitoring, analysis, and prediction have emerged as important challenges in several uses of water in our life. Recent water quality problems have raised the need for artificial intelligence (AI) models for analyzing water quality, classifying water samples, and predicting water quality index (WQI). In this paper, a machine-learning framework has been proposed for classify drinking water samples (safe/unsafe) and predicting water quality index. The classification tier of the proposed framework consists of nine machine-learning models, which have been applied, tested, validated, and compared for classifying drinking water samples into two classes (safe/unsafe) based on a benchmark dataset. The regression tier consists of six regression models that have been applied to the same dataset for predicting WQI. The experimental results clarified good classification results for the nine models with average accuracy, of 94.7%. However, the obtained results showed the superiority of Random Forest (RF), and Light Gradient Boosting Machine (Light GBM) models in recognizing safe drinking water samples regarding training and testing accuracy compared to the other models in the proposed framework. Moreover, the regression analysis results proved the superiority of LGBM regression, and Extra Trees Regression models in predicting WQI according to training, testing accuracy, 0.99%, and 0.95%, respectively.Moreover, the mean absolute error (MAE) results proved that the same models achieved less error rate, 10% than other applied regression models. These findings have significant implications for the understanding of how novel deep learning models can be developed for predicting water quality, which is suitable for other environmental and industrial purposes.

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Section: Literature Reviewmentioning

confidence: 99%

Recognizing Safe Drinking Water and Predicting Water Quality Index using Machine Learning Framework

Torky¹,

Bakhiet²,

Bakrey³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…An ANN is made up of a series of connected nodes or units, known as artificial neurons that approximately resemble naturally occurring brain neurons [10], [22]- [27]. The proposed model comprises an input layer, an output layer and hidden layers, as shown in Figure 3 and 4.…”

Section: Figure 3 Ann Graphical Representationmentioning

confidence: 99%

APP innovation to Control Projects Risks Management during Crises

Waheeb

2022

EAI Endorsed Trans IoT

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The goal of this study is to build an application that can be used in difficult cases and sudden circumstances during the pandemic and post-disaster state, which can be the development of digital risk management and mitigating the difficult impact of the epidemic through the improvement of IT and IoT that can be fine by finding initial solutions and make the world like a digital city that could be managed by the network. We provide this study to gain an overview of reasons for delayed and exceeded costs in a select of thirty Iraqi case projects by controlling the time and cost. The drivers of delay have been investigated in multiple countries/contexts. however, there is little country data available under the conditions that have characterized Iraq over the previous 10-20 years.

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“…Recently, this technique has been utilized in different branches of research. For example, research on predicting water quality has revealed that the ML algorithm could be more effective in evaluating water quality than other traditional methods (Aldhyani et al, 2020;Azrour et al, 2021;Babbar and Babbar, 2017;Haghiabi et al, 2018a;Mohammed et al, 2018;Prakash et al, 2018;Solanki et al, 2015;Xiong et al, 2020). Several studies have effectively used machine learning approaches to predict WQI (Ahmad et al, 2017;Bui et al, 2020;Grbčić et al, 2021;Hassan et al, 2021;Kadam et al, 2019;Kouadri et al, 2021;Leong et al, 2019;Venkata Vara Prasad et al, 2020;Wang et al, 2017).…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%