Deep learning for water quality

Nature Water, Journal Year: 2024, Volume and Issue: 2(3), P. 228 - 241

Published: March 12, 2024

Language: Английский

---

Performance of hydrogel immobilized bioreactors combined with different iron ore wastes for denitrification and removal of copper and lead: Optimization and possible mechanism

Water Research, Journal Year: 2022, Volume and Issue: 225, P. 119196 - 119196

Published: Oct. 1, 2022

Language: Английский

---

Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods

Entropy, Journal Year: 2023, Volume and Issue: 25(8), P. 1186 - 1186

Published: Aug. 9, 2023

In the context of escalating global environmental concerns, importance preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, monitoring prediction quality have emerged as vital tasks in achieving these objectives. However, ensuring accuracy dependability proven to be challenging endeavor. To address this issue, study proposes comprehensive weight-based approach that combines entropy weighting with Pearson correlation coefficient select crucial features prediction. This effectively considers both feature information content, avoiding excessive reliance on single criterion for selection. Through utilization approach, evaluation contribution was achieved, thereby minimizing subjective bias uncertainty. By striking balance among various factors, stronger greater content can selected, leading improved robustness feature-selection process. Furthermore, explored several machine learning models prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM). SVM exhibited commendable performance predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities high accuracy. MLP demonstrated its strength nonlinear modeling performed well multiple parameters. Conversely, RF XGBoost relatively inferior contrast, LSTM model, recurrent neural network specialized processing time series data, exceptional abilities It captured dynamic patterns present offering stable accurate predictions

Language: Английский

---

Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges

Chemosphere, Journal Year: 2024, Volume and Issue: 353, P. 141474 - 141474

Published: Feb. 19, 2024

Language: Английский

---

Enhanced Insights into Effluent Prediction in Wastewater Treatment Plants: Comprehensive Deep Learning Model Explanation Based on SHAP

ACS ES&T Water, Journal Year: 2024, Volume and Issue: 4(4), P. 1904 - 1915

Published: April 2, 2024

Models are increasingly being utilized to improve the understanding and operation of wastewater treatment plants (WWTPs) in face escalating water resource challenges. Abundant operational data provide extensive opportunities for development machine learning (ML) deep (DL) models. However, coupling time lag among features exacerbate black-box nature such models, hindering their application WWTPs. In this study, we construct a DL model using long short-term memory (LSTM) algorithm capable accurately predicting effluent quality full-scale WWTP with finely tuned hyperparameters rationally chosen input features. Comprehensive explanation based on Shapley additive explanations (SHAP) is implemented clarify contributions multivariate series (MTS) inputs predicted results terms feature dimensions. The LSTM models exhibit excellent accuracy (R2 0.96, 0.95, 0.76 MAPE 5.49, 7.17, 13.37%, respectively) chemical oxygen demand (COD), total phosphorus (TP), nitrogen (TN) better than other baseline ML SHAP quantify what most important when they exert influence how impact results. analysis from temporal dimension further explains characteristics process justifies introduction MTS. Compared correlation without engineering, selection method by significantly enhances predictive accuracy. combinations adjusted values, strong interactions significant output identified. This novel attempt both explainability MTS prediction work shows potential applying WWTPs performance.

Language: Английский

---

Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 471, P. 134392 - 134392

Published: April 23, 2024

Language: Английский

---

Surface water quality prediction in the lower Thoubal river watershed, India: A hyper-tuned machine learning approach and DNN-based sensitivity analysis

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(3), P. 112915 - 112915

Published: May 3, 2024

Language: Английский

---

Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment.

TrAC Trends in Analytical Chemistry, Journal Year: 2024, Volume and Issue: unknown, P. 117980 - 117980

Published: Sept. 1, 2024

Language: Английский

---

Bioaccessibility of arsenic, lead, and cadmium in contaminated mining/smelting soils: Assessment, modeling, and application for soil environment criteria derivation

Journal of Hazardous Materials, Journal Year: 2022, Volume and Issue: 443, P. 130321 - 130321

Published: Nov. 2, 2022

Language: Английский

---

Novel production prediction model of gasoline production processes for energy saving and economic increasing based on AM-GRU integrating the UMAP algorithm

Energy, Journal Year: 2022, Volume and Issue: 262, P. 125536 - 125536

Published: Sept. 22, 2022

Language: Английский

---