Machine Learning-driven Optimization of Water Quality Index: A Synergistic ENTROPY-CRITIC Approach Using Spatio-Temporal Data DOI
Imran Khan,

Rashid Umar

Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 8(4), P. 1453 - 1475

Published: Oct. 24, 2024

Language: Английский

Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China DOI
Zhiyuan Yao, Zhaocai Wang,

Jinghan Huang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175407 - 175407

Published: Aug. 9, 2024

Language: Английский

Citations

24
A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India DOI Creative Commons
Nagalapalli Satish, Jagadeesh Anmala,

K. Rajitha

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102500 - 102500

Published: Jan. 28, 2024

The importance of water quality models has increased as their inputs are critical to the development risk assessment framework for environmental management and monitoring rivers. However, with advent a plethora recent advances in ML algorithms better predictions possible. This study proposes causal effect model by considering climatological such temperature precipitation along geospatial information related agricultural land use factor (ALUF), forest (FLUF), grassland usage (GLUF), shrub (SLUF), urban (ULUF). All these factors included input data, whereas four Stream Water Quality parameters (SWQPs) Electrical Conductivity (EC), Biochemical Oxygen Demand (BOD), Nitrate, Dissolved (DO) from 2019 2021 taken outputs predict Godavari River Basin quality. In preliminary investigation, out SWQPs, nitrate's coefficient variation (CV) is high, revealing close association climate practices across sampling stations. authors' earlier study, using single-layer Feed-Forward Neural Network (FFNN) showed improved performance predicting cause linked metrics. To achieve prediction, stacked ANN meta-model nine conventional machine learning (ML) models, including Extreme Gradient Boosting (XGB), Extra Trees (ET), Bagging (BG), Random Forest (RF), AdaBoost or Adaptive (ADB), Decision Tree (DT), Highest (HGB), Light Method (LGBM), (GB), were compared this study. According study's findings, outperformed stand-alone FFNN same dataset superior predictive capabilities terms accuracy forecasting variable interest. For instance, during testing, determination (R2) (BOD) 0.72 0.87. Furthermore, Artificial (ANN) meta that was reinforced (ET) base performed than individual (from R2 = 0.87 0.91 BOD testing). By new framework, effort hyperparameter tuning can be minimized.

Language: Английский

Citations

18
Metaheuristic approaches for prediction of water quality indices with relief algorithm-based feature selection DOI
Nand Lal Kushwaha, Jitendra Rajput, Truptimayee Suna

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 75, P. 102122 - 102122

Published: May 9, 2023

Language: Английский

Citations

31
Water Quality Index Assessment of River Ganga at Haridwar Stretch Using Multivariate Statistical Technique DOI
Abdul Gani, Shray Pathak, Athar Hussain

et al.

Molecular Biotechnology, Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 20, 2023

Language: Английский

Citations

25
Sensitivity analysis-driven machine learning approach for groundwater quality prediction: Insights from integrating ENTROPY and CRITIC methods DOI
Imran Khan, Md. Ayaz

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 26, P. 101309 - 101309

Published: Aug. 1, 2024

Language: Английский

Citations

9
Geochemical and isotopic studies of the Douda-Damerjogue aquifer (Republic of Djibouti): Origin of high nitrate and fluoride, spatial distribution, associated health risk assessment and prediction of water quality using machine learning DOI
M.O. Awaleh, Tiziano Boschetti,

Christelle Marlin

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 967, P. 178789 - 178789

Published: Feb. 14, 2025

Language: Английский

Citations

1
Applying the water quality indices, geographical information system, and advanced decision-making techniques to assess the suitability of surface water for drinking purposes in Brahmani River Basin (BRB), Odisha DOI
Abhijeet Das

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

Language: Английский

Citations

1
Research on a multiparameter water quality prediction method based on a hybrid model DOI
Zhiqiang Zheng, Hao Ding, Zhi Weng

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 76, P. 102125 - 102125

Published: May 16, 2023

Language: Английский

Citations

16
Data-driven reference evapotranspiration (ET0) estimation: a comparative study of regression and machine learning techniques DOI
Jitendra Rajput, Man Singh,

Khajanchi Lal

et al.

Environment Development and Sustainability, Journal Year: 2023, Volume and Issue: 26(5), P. 12679 - 12706

Published: Oct. 13, 2023

Language: Английский

Citations

13
An Optimized Approach for Predicting Water Quality Features and A Performance evaluation for Mapping Surface Water Potential Zones Based on Discriminant Analysis (DA), Geographical Information System (GIS) and Machine Learning (ML) Models in Baitarani River Basin, Odisha DOI Creative Commons

Abhijeet Das

Desalination and Water Treatment, Journal Year: 2025, Volume and Issue: 321, P. 101039 - 101039

Published: Jan. 1, 2025

Language: Английский

Citations

0