Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniques
AquaMeld, a novel method for reusing agricultural and industrial wastewater in rivers and lakes, is presented in this article. Water shortage and environmental sustainability are major problems, making wastewater treatment a responsibility. Customizing solutions for varied stakeholders and environme...
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PeerJ Inc.
2024-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2488.pdf |
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| author | J. Priskilla Angel Rani C. Yesubai Rubavathi |
| author_facet | J. Priskilla Angel Rani C. Yesubai Rubavathi |
| author_sort | J. Priskilla Angel Rani |
| collection | DOAJ |
| description | AquaMeld, a novel method for reusing agricultural and industrial wastewater in rivers and lakes, is presented in this article. Water shortage and environmental sustainability are major problems, making wastewater treatment a responsibility. Customizing solutions for varied stakeholders and environmental conditions using standard methods is challenging. This study uses AquaMeld and Multi-Layer Perceptron with Recurrent Neural Network (MLP-RNN) algorithms to create a complete recommendation system. AquaMeld uses MLP-RNN to evaluate complicated wastewater, environmental, and pH data. AquaMeld analyses real-time data to recommend wastewater reuse systems. This design can adapt to changing scenarios and user demands, helping ideas grow. This technique does not assume data follows a distribution, which may reduce the model’s predictive effectiveness. Instead, it forecasts aquatic quality using RNN-MLP. The main motivation is combining the two models into the MLP-RNN to improve prediction accuracy. RNN handles sequential data better, whereas MLP handles complex nonlinear relationships better. MLP-RNN projections are the most accurate. This shows how effectively the model handles complicated, time- and place-dependent water quality data. If other environmental data analysis projects have similar limits, MLP-RNN may work. AquaMeld has several benefits over traditional methods. The MLP-RNN architecture uses deep learning to assess complicated aquatic ecosystem interactions, enabling more proactive and accurate decision-making is the most accurate with a 98% success rate. AquaMeld is flexible and eco-friendly since it may be used for many agricultural and industrial operations. AquaMeld helps stakeholders make better, faster water resource management choices. Models and field studies in agricultural and industrial contexts examine AquaMeld’s efficacy. This strategy enhances environmental sustainability, resource exploitation, and wastewater reuse over previous ones. According to the results, AquaMeld might transform wastewater treatment. River and lake-dependent companies and agriculture may now use water resource management methods that are less destructive. |
| format | Article |
| id | doaj-art-d68ea8e3b68d4de4a07bb306d82b185a |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-d68ea8e3b68d4de4a07bb306d82b185a2025-08-20T02:49:16ZengPeerJ Inc.PeerJ Computer Science2376-59922024-11-0110e248810.7717/peerj-cs.2488Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniquesJ. Priskilla Angel Rani0C. Yesubai Rubavathi1Department of Computer Science and Engineering, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Saveetha Engineering College, Thandalam, Tamil Nadu, IndiaAquaMeld, a novel method for reusing agricultural and industrial wastewater in rivers and lakes, is presented in this article. Water shortage and environmental sustainability are major problems, making wastewater treatment a responsibility. Customizing solutions for varied stakeholders and environmental conditions using standard methods is challenging. This study uses AquaMeld and Multi-Layer Perceptron with Recurrent Neural Network (MLP-RNN) algorithms to create a complete recommendation system. AquaMeld uses MLP-RNN to evaluate complicated wastewater, environmental, and pH data. AquaMeld analyses real-time data to recommend wastewater reuse systems. This design can adapt to changing scenarios and user demands, helping ideas grow. This technique does not assume data follows a distribution, which may reduce the model’s predictive effectiveness. Instead, it forecasts aquatic quality using RNN-MLP. The main motivation is combining the two models into the MLP-RNN to improve prediction accuracy. RNN handles sequential data better, whereas MLP handles complex nonlinear relationships better. MLP-RNN projections are the most accurate. This shows how effectively the model handles complicated, time- and place-dependent water quality data. If other environmental data analysis projects have similar limits, MLP-RNN may work. AquaMeld has several benefits over traditional methods. The MLP-RNN architecture uses deep learning to assess complicated aquatic ecosystem interactions, enabling more proactive and accurate decision-making is the most accurate with a 98% success rate. AquaMeld is flexible and eco-friendly since it may be used for many agricultural and industrial operations. AquaMeld helps stakeholders make better, faster water resource management choices. Models and field studies in agricultural and industrial contexts examine AquaMeld’s efficacy. This strategy enhances environmental sustainability, resource exploitation, and wastewater reuse over previous ones. According to the results, AquaMeld might transform wastewater treatment. River and lake-dependent companies and agriculture may now use water resource management methods that are less destructive.https://peerj.com/articles/cs-2488.pdfAquaMeldMulti-layer perceptronRecurrent neural networkWastewater recommendationRiver and lake ecosystems |
| spellingShingle | J. Priskilla Angel Rani C. Yesubai Rubavathi Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniques PeerJ Computer Science AquaMeld Multi-layer perceptron Recurrent neural network Wastewater recommendation River and lake ecosystems |
| title | Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniques |
| title_full | Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniques |
| title_fullStr | Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniques |
| title_full_unstemmed | Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniques |
| title_short | Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniques |
| title_sort | enhancing river and lake wastewater reuse recommendation in industrial and agricultural using aquameld techniques |
| topic | AquaMeld Multi-layer perceptron Recurrent neural network Wastewater recommendation River and lake ecosystems |
| url | https://peerj.com/articles/cs-2488.pdf |
| work_keys_str_mv | AT jpriskillaangelrani enhancingriverandlakewastewaterreuserecommendationinindustrialandagriculturalusingaquameldtechniques AT cyesubairubavathi enhancingriverandlakewastewaterreuserecommendationinindustrialandagriculturalusingaquameldtechniques |