OPTIMIZATION OF NEURAL NETWORK CLASSIFIERS BY LEVERAGING THE SEQUENTIAL FEATURE ENGINEERING FOR ROBUST WATER QUALITY PREDICTION SYSTEM
Rapid population growth increases water demand, intensifying extraction from wells and rivers. The Water Quality Index (WQI) assesses water suitability for drinking based on multiple parameters. Accurate assessment of pollution in water is imperative for effective management of water quality. The pr...
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2025-03-01
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| Series: | Proceedings on Engineering Sciences |
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| Online Access: | https://pesjournal.net/journal/v7-n1/33.pdf |
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| author | Maheswara Rao V V R Silpa N Kranthi Addanki Shiva Shankar Reddy Ramachandra Rao Kurada Pachipala Yellamma |
| author_facet | Maheswara Rao V V R Silpa N Kranthi Addanki Shiva Shankar Reddy Ramachandra Rao Kurada Pachipala Yellamma |
| author_sort | Maheswara Rao V V R |
| collection | DOAJ |
| description | Rapid population growth increases water demand, intensifying extraction from wells and rivers. The Water Quality Index (WQI) assesses water suitability for drinking based on multiple parameters. Accurate assessment of pollution in water is imperative for effective management of water quality. The present research on the Neural Network-based Robust Water Quality Prediction System (NN-RWQPS) exploits the capabilities of neural networks and advances in feature engineering, positioning it at the forefront of WQI. Venturing into the new world of predictive modelling armed with four different neural network classifiers: Wide, Bilayer, Trilayer, and an Optimized Neural Network. Further the study harness the power of feature selection, deploying four distinct methods. A champion feature selection method is scientifically validated for each neural network, and then the neural networks are fine-tuned by training them across a range of feature dimensions, unveiling an empirically supported set of optimal features. Study advances water quality prediction using neural networks and feature engineering. |
| format | Article |
| id | doaj-art-46d1d1f45e26475ea3ebd22ebd52e60b |
| institution | DOAJ |
| issn | 2620-2832 2683-4111 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj-art-46d1d1f45e26475ea3ebd22ebd52e60b2025-08-20T02:58:36ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112025-03-017128529410.24874/PES07.01B.008OPTIMIZATION OF NEURAL NETWORK CLASSIFIERS BY LEVERAGING THE SEQUENTIAL FEATURE ENGINEERING FOR ROBUST WATER QUALITY PREDICTION SYSTEMMaheswara Rao V V R 0https://orcid.org/0000-0002-0503-7211Silpa N 1https://orcid.org/0000-0003-3411-0358Kranthi Addanki 2https://orcid.org/0000-0003-4921-8783Shiva Shankar Reddy 3https://orcid.org/0000-0001-5439-0348Ramachandra Rao Kurada 4https://orcid.org/0000-0002-7014-8313Pachipala Yellamma 5https://orcid.org/0000-0002-6291-7418Shri Vishnu Engineering College for Women, Bhimavaram, India Shri Vishnu Engineering College for Women, Bhimavaram, India James Cook University, James Cook Drive, AustraliaS.R.K.R. Engineering College, Bhimavaram, India Shri Vishnu Engineering College for Women, Bhimavaram, India Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India Rapid population growth increases water demand, intensifying extraction from wells and rivers. The Water Quality Index (WQI) assesses water suitability for drinking based on multiple parameters. Accurate assessment of pollution in water is imperative for effective management of water quality. The present research on the Neural Network-based Robust Water Quality Prediction System (NN-RWQPS) exploits the capabilities of neural networks and advances in feature engineering, positioning it at the forefront of WQI. Venturing into the new world of predictive modelling armed with four different neural network classifiers: Wide, Bilayer, Trilayer, and an Optimized Neural Network. Further the study harness the power of feature selection, deploying four distinct methods. A champion feature selection method is scientifically validated for each neural network, and then the neural networks are fine-tuned by training them across a range of feature dimensions, unveiling an empirically supported set of optimal features. Study advances water quality prediction using neural networks and feature engineering.https://pesjournal.net/journal/v7-n1/33.pdfwater quality predictionneural networksfeature engineeringdata analyticspredictive modelling |
| spellingShingle | Maheswara Rao V V R Silpa N Kranthi Addanki Shiva Shankar Reddy Ramachandra Rao Kurada Pachipala Yellamma OPTIMIZATION OF NEURAL NETWORK CLASSIFIERS BY LEVERAGING THE SEQUENTIAL FEATURE ENGINEERING FOR ROBUST WATER QUALITY PREDICTION SYSTEM Proceedings on Engineering Sciences water quality prediction neural networks feature engineering data analytics predictive modelling |
| title | OPTIMIZATION OF NEURAL NETWORK CLASSIFIERS BY LEVERAGING THE SEQUENTIAL FEATURE ENGINEERING FOR ROBUST WATER QUALITY PREDICTION SYSTEM |
| title_full | OPTIMIZATION OF NEURAL NETWORK CLASSIFIERS BY LEVERAGING THE SEQUENTIAL FEATURE ENGINEERING FOR ROBUST WATER QUALITY PREDICTION SYSTEM |
| title_fullStr | OPTIMIZATION OF NEURAL NETWORK CLASSIFIERS BY LEVERAGING THE SEQUENTIAL FEATURE ENGINEERING FOR ROBUST WATER QUALITY PREDICTION SYSTEM |
| title_full_unstemmed | OPTIMIZATION OF NEURAL NETWORK CLASSIFIERS BY LEVERAGING THE SEQUENTIAL FEATURE ENGINEERING FOR ROBUST WATER QUALITY PREDICTION SYSTEM |
| title_short | OPTIMIZATION OF NEURAL NETWORK CLASSIFIERS BY LEVERAGING THE SEQUENTIAL FEATURE ENGINEERING FOR ROBUST WATER QUALITY PREDICTION SYSTEM |
| title_sort | optimization of neural network classifiers by leveraging the sequential feature engineering for robust water quality prediction system |
| topic | water quality prediction neural networks feature engineering data analytics predictive modelling |
| url | https://pesjournal.net/journal/v7-n1/33.pdf |
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