Prediction and modeling of water quality using deep neural networks

El-shebli, Marwa; Sharrab, Yousef; Al-Fraihat, Dimah

doi:10.1007/s10668-023-03335-5

Cited by 8 publications

(3 citation statements)

References 83 publications

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“…Marwa El-Shebli et al (2023) [13] present in their study a very reliable DNN model developed expressly for water quality assessment, especially in dry and wet seasons. One can extract the information from experimental data by applying advanced techniques of statistical modelling and unsupervised machine learning, including principal component analysis (PCA), factor analysis (FA) and hierarchical cluster analysis (HCA).…”

Section: Literature Surveymentioning

confidence: 99%

“…However, accurately incorporating contextual and spatial factors and capturing temporal relationships present challenges. The CGTFN model, integrating the IoT [10], introduces an innovative perspective to address these difficulties and overcome issues encountered in previous studies [11][12][13][14], including limitations in regional focus, transfer learning challenges [12], and interpretability issues [13]. By combining IoT data, CGFTN, and advanced deep learning techniques, our model presents a solution to remedy the limitations of existing models.…”

Section: Introductionmentioning

confidence: 99%

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An IoT Based Water Quality Classification Framework for Aqua-Ponds through Water and Environmental Variables using CGTFN Model

AREPALLI,

Amgoth

2024

Preprint

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Maintaining water quality in aquatic habitats is critical for the health of aquatic species, particularly for fish. This study pioneers an innovative method to water quality classification, leveraging IoT-driven data acquisition and meticulous data labelling with the Aqua-Enviro Index (AEI) by considering the fish habitats. Existing mechanisms fail to capture complex temporal dynamics and depend largely on large amounts of labelled data, exposing fundamental limits. In response, we describe the Deep learning based Convolutional Gated Recurrent Unit Tempo Fusion Network (CGTFN) model, which represents a considerable development in the evaluation of water quality. The model addresses these restrictions by seamlessly merging Convolutional Neural Networks (CNNs) for spatial pattern recognition and Gated Recurrent Units (GRUs) for temporal interactions. The Tempo Fusion mechanism combines spatial, temporal, and contextual data harmoniously, allowing for more sophisticated classifications by recognizing subtle interdependencies among environmental elements. The pioneering CGTFN model outperforms previous models, achieving 99.71% and 99.81% accuracy on both public-env and real-time-env datasets, respectively, exceeding established models at 98.2%. These remarkable findings highlight CGTFN's disruptive potential in water quality evaluation, bridging the gap between technology and environmental management, with ramifications ranging from aquaculture to resource sustainability.

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An IoT Based Water Quality Classification Framework for Aqua-Ponds through Water and Environmental Variables using CGTFN Model

AREPALLI,

Amgoth

2024

Preprint

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“…Recent research endeavors have proposed approaches that incorporate machine learning techniques to tackle these challenges [58]. However, further exploration is needed to evaluate their impact across metrics and address the limitations inherent in existing clustering approaches [59,60].…”

Section: Optimizing Clustering Approaches In Cloud Environmentsmentioning

confidence: 99%

Optimizing Clustering Approaches in Cloud Environments

Al-Ghuwairi,

Al-Fraihat,

Sharrab

et al. 2023

Int. J. Interact. Mob. Technol.

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This study focuses on the challenge of developing abstract models to differentiate various cloud resources. It explores the advancements in cloud products that offer specialized services to meet specific external needs. The study proposes a new approach to request processing in clusters, improving downtime, load distribution, and overall performance. A comparison of three clustering approaches is conducted: local single cluster, local multiple clusters, and multiple cloud clusters. Performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness are evaluated through experiments with 50 requests. All three approaches achieve a 100% success rate, but processing times vary. The local single cluster has the longest duration, while the local multiple clusters and multiple cloud clusters perform better and offer faster processing, scalability, fault tolerance, and availability. From a cost perspective, the local single cluster and local multiple clusters incur capital and operational expenses, while the multiple cloud clusters follow a pay-as-you-go model. Overall, the local multiple clusters and multiple cloud clusters outperform the local single cluster in terms of performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness. These findings provide valuable insights for selecting appropriate clustering strategies in cloud environments.

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Hydrogeochemical Evaluation and Multivariate Statistical Analysis of Groundwater for Sustainable Groundwater Quality Management in the Industrial Corridor of Ranipet District, Tamil Nadu, India

Krishnamoorthy,

Lakshmanan

2024

Water Air Soil Pollut

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Prediction and modeling of water quality using deep neural networks

Cited by 8 publications

References 83 publications

An IoT Based Water Quality Classification Framework for Aqua-Ponds through Water and Environmental Variables using CGTFN Model

An IoT Based Water Quality Classification Framework for Aqua-Ponds through Water and Environmental Variables using CGTFN Model

Optimizing Clustering Approaches in Cloud Environments

Hydrogeochemical Evaluation and Multivariate Statistical Analysis of Groundwater for Sustainable Groundwater Quality Management in the Industrial Corridor of Ranipet District, Tamil Nadu, India

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