ArticleEnvironmental science and pollution research international2020
Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index.
Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- Prediction of water quality in Jordanian dams using data mining algorithms.Water science and technology : a journal of the International Association on Water Pollution Research · 2025Article
- Making waves: Knowledge and data fusion in urban water modelling.Water research X · 2024Article
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- Machine learning predictive insight of water pollution and groundwater quality in the Eastern Province of Saudi Arabia.Scientific reports · 2024Article
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- Suspended sediment load prediction using sparrow search algorithm-based support vector machine model.Scientific reports · 2024Article
- Advances in machine learning and IoT for water quality monitoring: A comprehensive review.Heliyon · 2024Review
- Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique.Scientific reports · 2024Article
- Predicting sea levels using ML algorithms in selected locations along coastal Malaysia.Heliyon · 2023Article
- Application of hybrid improved temporal convolution network model in time series prediction of river water quality.Scientific reports · 2023Article
- Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan.Scientific reports · 2022Article
- Assessment of performance indicators for different sizes of water supply systems in Minas Gerais, Brazil.Environmental monitoring and assessment · 2021Article
- Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction.Environmental science and pollution research international · 2021Article
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9 authors.
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Abstract
In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH
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