Ensemble machine learning using hydrometeorological information to improve modeling of quality parameter of raw water supplying treatment plants DOI Creative Commons
Christian Ortiz-Lopez,

Christian Bouchard,

Manuel J. Rodríguez

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 362, P. 121378 - 121378

Published: June 1, 2024

Source and raw water quality may deteriorate due to rainfall river flow events that occur in watersheds. The effects on are normally detected drinking treatment plants (DWTPs) with a time-lag after these the Early warning systems (EWSs) DWTPs require models high accuracy order anticipate changes parameters. Ensemble machine learning (EML) techniques have recently been used for modeling improve decrease variance outcomes. We three decision-tree-based EML (random forest [RF], gradient boosting [GB], eXtreme Gradient Boosting [XGB]) predict two critical parameters DWTPs, Turbidity UV absorbance (UV254), using time series as predictors. When turbidity, (rRF−Tu2=0.87, rGB−Tu2=0.80 rXGB−Tu2=0.81) showed very good performance metrics. For UV254, (rRF−UV2=0.89, rGB−UV2=0.85 rXGB−UV2=0.88) again Results from this study suggest approaches could be EWSs of enhance decision-making DWTPs.

Language: Английский

A novel machine learning-based framework for the water quality parameters prediction using hybrid long short-term memory and locally weighted scatterplot smoothing methods DOI Creative Commons
Ana Dodig, Elisa Ricci, Goran Kvaščev

et al.

Journal of Hydroinformatics, Journal Year: 2024, Volume and Issue: 26(5), P. 1059 - 1079

Published: April 12, 2024

ABSTRACT Water quality prediction is crucial for effective river stream management. Dissolved oxygen, conductivity and chemical oxygen demand are vital parameters water quality. Development of machine learning (ML) deep (DL) methods made them widely used in this domain. Sophisticated DL techniques, especially long short-term memory (LSTM) networks, required accurate, real-time multistep prediction. LSTM networks predicting due to their ability handle long-term dependencies sequential data. We propose a novel hybrid approach combining with data smoothing method. The Sava at the Jamena hydrological station serves as case study. Our workflow uses alongside LOcally WEighted Scatterplot Smoothing (LOWESS) technique filtering. For comparison, Support Vector Regressor (SVR) baseline Performance evaluated using Root Mean Squared Error (RMSE) Coefficient Determination R2 metrics. Results demonstrate that outperforms method, an up 0.9998 RMSE 0.0230 on test set dissolved oxygen. Over 5-day period, our achieves 0.9912 0.1610 confirming it reliable method

Language: Английский

Citations

9
A water quality prediction approach for the Downstream and Delta of Dongjiang River Basin under the joint effects of water intakes, pollution sources, and climate change DOI

Yaping Huang,

Yanpeng Cai, Yanhu He

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 640, P. 131686 - 131686

Published: July 16, 2024

Language: Английский

Citations

3
Interpretable Ai-Enhanced Reliable River Water Quality Prediction with Multi Remote Sensing Data Sources: Insights from Meteorological & Spatial-Temporal Variables DOI
Salma Imtiaz,

Mitra Nasr Azadani,

Nasrin Alamdari

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Ensemble machine learning using hydrometeorological information to improve modeling of quality parameter of raw water supplying treatment plants DOI Creative Commons
Christian Ortiz-Lopez,

Christian Bouchard,

Manuel J. Rodríguez

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 362, P. 121378 - 121378

Published: June 1, 2024

Source and raw water quality may deteriorate due to rainfall river flow events that occur in watersheds. The effects on are normally detected drinking treatment plants (DWTPs) with a time-lag after these the Early warning systems (EWSs) DWTPs require models high accuracy order anticipate changes parameters. Ensemble machine learning (EML) techniques have recently been used for modeling improve decrease variance outcomes. We three decision-tree-based EML (random forest [RF], gradient boosting [GB], eXtreme Gradient Boosting [XGB]) predict two critical parameters DWTPs, Turbidity UV absorbance (UV254), using time series as predictors. When turbidity, (rRF−Tu2=0.87, rGB−Tu2=0.80 rXGB−Tu2=0.81) showed very good performance metrics. For UV254, (rRF−UV2=0.89, rGB−UV2=0.85 rXGB−UV2=0.88) again Results from this study suggest approaches could be EWSs of enhance decision-making DWTPs.

Language: Английский

Citations

0