Assessing the feasibility of using Machine learning algorithms to determine reservoir water quality based on a reduced set of predictors
The present study analyses the possibility of assessing water quality using the water quality index (WQI) through the application of four different machine learning algorithms (ML): neural network models (NNM), random forest (RF), k-nearest neighbor (KNN), and linear regression (LR). Water quality w...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25004868 |
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| author | Natalia Walczak Zbigniew Walczak |
| author_facet | Natalia Walczak Zbigniew Walczak |
| author_sort | Natalia Walczak |
| collection | DOAJ |
| description | The present study analyses the possibility of assessing water quality using the water quality index (WQI) through the application of four different machine learning algorithms (ML): neural network models (NNM), random forest (RF), k-nearest neighbor (KNN), and linear regression (LR). Water quality was determined based on 5 indicators: P, COD, BOD5, N total, and total suspended solids TS. The possibility of predicting water quality (WQI index) based on the reduced number of predictors was then analyzed. It was estimated which indicators have the most significant impact on WQI values. The performance of models using different algorithms, as well as those trained on full and reduced data sets, was compared. The models demonstrate high performance in WQI prediction, achieving an R2 of 0.999 (for NNM and LR) for the entire dataset, 0.988 (KNN) for the dataset using only three types of predictors, and 0.941 for the dataset using only two predictors (RF). The construction and training of ML models for reduced sets and types of predictors will enable early water quality estimation based on only a few selected parameters. The implementation of ML algorithms will enable more effective water quality management and significantly improve the precision of predictions for critical water parameters. |
| format | Article |
| id | doaj-art-a98d58b7e0c74a38aaf2fab2aad37d96 |
| institution | DOAJ |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-a98d58b7e0c74a38aaf2fab2aad37d962025-08-20T03:12:32ZengElsevierEcological Indicators1470-160X2025-06-0117511355610.1016/j.ecolind.2025.113556Assessing the feasibility of using Machine learning algorithms to determine reservoir water quality based on a reduced set of predictorsNatalia Walczak0Zbigniew Walczak1Department of Hydraulic and Sanitary Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 28, 60-637 Poznan, Poland; Corresponding author.Department of Construction and Geoengineering, Poznan University of Life Sciences, ul. Wojska Polskiego 28, 60-637 Poznan, PolandThe present study analyses the possibility of assessing water quality using the water quality index (WQI) through the application of four different machine learning algorithms (ML): neural network models (NNM), random forest (RF), k-nearest neighbor (KNN), and linear regression (LR). Water quality was determined based on 5 indicators: P, COD, BOD5, N total, and total suspended solids TS. The possibility of predicting water quality (WQI index) based on the reduced number of predictors was then analyzed. It was estimated which indicators have the most significant impact on WQI values. The performance of models using different algorithms, as well as those trained on full and reduced data sets, was compared. The models demonstrate high performance in WQI prediction, achieving an R2 of 0.999 (for NNM and LR) for the entire dataset, 0.988 (KNN) for the dataset using only three types of predictors, and 0.941 for the dataset using only two predictors (RF). The construction and training of ML models for reduced sets and types of predictors will enable early water quality estimation based on only a few selected parameters. The implementation of ML algorithms will enable more effective water quality management and significantly improve the precision of predictions for critical water parameters.http://www.sciencedirect.com/science/article/pii/S1470160X25004868Reservoir water qualityWQIMachine learning MLPCA |
| spellingShingle | Natalia Walczak Zbigniew Walczak Assessing the feasibility of using Machine learning algorithms to determine reservoir water quality based on a reduced set of predictors Ecological Indicators Reservoir water quality WQI Machine learning ML PCA |
| title | Assessing the feasibility of using Machine learning algorithms to determine reservoir water quality based on a reduced set of predictors |
| title_full | Assessing the feasibility of using Machine learning algorithms to determine reservoir water quality based on a reduced set of predictors |
| title_fullStr | Assessing the feasibility of using Machine learning algorithms to determine reservoir water quality based on a reduced set of predictors |
| title_full_unstemmed | Assessing the feasibility of using Machine learning algorithms to determine reservoir water quality based on a reduced set of predictors |
| title_short | Assessing the feasibility of using Machine learning algorithms to determine reservoir water quality based on a reduced set of predictors |
| title_sort | assessing the feasibility of using machine learning algorithms to determine reservoir water quality based on a reduced set of predictors |
| topic | Reservoir water quality WQI Machine learning ML PCA |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25004868 |
| work_keys_str_mv | AT nataliawalczak assessingthefeasibilityofusingmachinelearningalgorithmstodeterminereservoirwaterqualitybasedonareducedsetofpredictors AT zbigniewwalczak assessingthefeasibilityofusingmachinelearningalgorithmstodeterminereservoirwaterqualitybasedonareducedsetofpredictors |