Deep Learning for Water Quality Classification in Water Distribution Networks

Shahra, Essa Q.; Wu, Wenyan; Basurra, Shadi; Rizou, Stamatia

doi:10.1007/978-3-030-80568-5_13

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…Finally, the extracted features generated by the final deep learning network layer will be recognised as the output by a classifier. Deep learning networks consider the layers closest to the input data to be lower levels, while the others are higher layers [28,31].…”

Section: Deep Learning In Edge Computingmentioning

confidence: 99%

Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System

Shahra,

Wu,

Basurra

et al. 2024

Water

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Ensuring consistent high water quality is paramount in water management planning. This paper addresses this objective by proposing an intelligent edge-cloud framework for water quality monitoring within the water distribution system (WDS). Various scenarios—cloud computing, edge computing, and hybrid edge-cloud computing—are applied to identify the most effective platform for the proposed framework. The first scenario brings the analysis closer to the data generation point (at the edge). The second and third scenarios combine both edge and cloud platforms for optimised performance. In the third scenario, sensor data are directly sent to the cloud for analysis. The proposed framework is rigorously tested across these scenarios. The results reveal that edge computing (scenario 1) outperforms cloud computing in terms of latency, throughput, and packet delivery ratio obtaining 20.33 ms, 148 Kb/s, and 97.47%, respectively. Notably, collaboration between the edge and cloud enhances the accuracy of classification models with an accuracy of up to 94.43%, this improvement was achieved while maintaining the energy consumption rate at the lowest value. In conclusion, our study demonstrates the effectiveness of the proposed intelligent edge-cloud framework in optimising water quality monitoring, and the superior performance of edge computing, coupled with collaborative edge-cloud strategies, underscores the practical viability of this approach.

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Section: Deep Learning In Edge Computingmentioning

confidence: 99%

Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System

Shahra,

Wu,

Basurra

et al. 2024

Water

Self Cite

View full text Add to dashboard Cite

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“…NARNET and LSTM achieved almost the same accuracy but a slightly different regression coefficient (RLSTM= 94.21%, NARNET = 96.17%), and from machine learning models, SVM achieved an accuracy of 97.01%. Shahra et al [12] proposed a deep learning-based system for water-quality classification for water distribution networks. The study aims to achieve high accuracy and keep low time for computation.…”

Section: Related Workmentioning

confidence: 99%

Water-Quality Prediction Based on H2O AutoML and Explainable AI Techniques

Madni

Umer

Ishaq

et al. 2023

Water

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Rapid expansion of the world’s population has negatively impacted the environment, notably water quality. As a result, water-quality prediction has arisen as a hot issue during the last decade. Existing techniques fall short in terms of good accuracy. Furthermore, presently, the dataset available for analysis contains missing values; these missing values have a significant effect on the performance of the classifiers. An automated system for water-quality prediction that deals with the missing values efficiently and achieves good accuracy for water-quality prediction is proposed in this study. To handle the accuracy problem, this study makes use of the stacked ensemble H2O AutoML model; to handle the missing values, this study makes use of the KNN imputer. Moreover, the performance of the proposed system is compared to that of seven machine learning algorithms. Experiments are performed in two scenarios: removing missing values and using the KNN imputer. The contribution of each feature regarding prediction is explained using SHAP (SHapley Additive exPlanations). Results reveal that the proposed stacked model outperforms other models with 97% accuracy, 96% precision, 99% recall, and 98% F1-score for water-quality prediction.

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“…According to the study, the kernel function employed for the SVM algorithm made the results more convincing than other models. For water distribution systems, a DL-based architecture was developed by Shahra et al [12]. At this point, the work attained a higher accuracy with lower computation.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Water Quality Using SoftMax-ELM Optimized Using Adaptive Crow-Search Algorithm

Chakravarthy,

Bharanidharan,

Venkatesan

et al. 2023

IEEE Access

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Water is a predominant source in the survival and development of all human lives. On top of all, predicting water quality is a significant one since water is essential in regulating our human body. In recent days, the advent of machine learning techniques has been supporting a lot in water quality prediction. Accordingly, Adaptive Crow Search Optimized SoftMax-Extreme Learning Machine (AdCSO-sELM) is proposed to improve the ELM performance by making the flight length adaptively with respect to the iterations. Here, the research novelty lies in making the CSOA parameters as a dynamic one which in turn provides promising ELM performance. Finally, the proposed AdCSO-sELM provides a superior accuracy of 96.54% for classifying water potability using the Kaggle dataset.INDEX TERMS crow search algorithm, extreme learning machine, neural network, optimization, water quality.

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Deep Learning for Water Quality Classification in Water Distribution Networks

Cited by 8 publications

References 19 publications

Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System

Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System

Water-Quality Prediction Based on H2O AutoML and Explainable AI Techniques

Prediction of Water Quality Using SoftMax-ELM Optimized Using Adaptive Crow-Search Algorithm

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