Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China

The Science of The Total Environment, Год журнала: 2024, Номер 951, С. 175407 - 175407

Опубликована: Авг. 9, 2024

Язык: Английский

---

A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102500 - 102500

Опубликована: Янв. 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.

Язык: Английский

---

Metaheuristic approaches for prediction of water quality indices with relief algorithm-based feature selection

Ecological Informatics, Год журнала: 2023, Номер 75, С. 102122 - 102122

Опубликована: Май 9, 2023

Язык: Английский

---

Water Quality Index Assessment of River Ganga at Haridwar Stretch Using Multivariate Statistical Technique

Molecular Biotechnology, Год журнала: 2023, Номер unknown

Опубликована: Сен. 20, 2023

Язык: Английский

---

Sensitivity analysis-driven machine learning approach for groundwater quality prediction: Insights from integrating ENTROPY and CRITIC methods

Groundwater for Sustainable Development, Год журнала: 2024, Номер 26, С. 101309 - 101309

Опубликована: Авг. 1, 2024

Язык: Английский

---

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

The Science of The Total Environment, Год журнала: 2025, Номер 967, С. 178789 - 178789

Опубликована: Фев. 14, 2025

Язык: Английский

---

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

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Март 31, 2025

Язык: Английский

---

Research on a multiparameter water quality prediction method based on a hybrid model

Ecological Informatics, Год журнала: 2023, Номер 76, С. 102125 - 102125

Опубликована: Май 16, 2023

Язык: Английский

---

Data-driven reference evapotranspiration (ET0) estimation: a comparative study of regression and machine learning techniques

Environment Development and Sustainability, Год журнала: 2023, Номер 26(5), С. 12679 - 12706

Опубликована: Окт. 13, 2023

Язык: Английский

---

Enhancing the prediction of irrigation demand for open field vegetable crops in Germany through neural networks, transfer learning, and ensemble models

Agricultural Water Management, Год журнала: 2025, Номер 312, С. 109402 - 109402

Опубликована: Март 18, 2025

Язык: Английский

---