Advancing Water Quality Assessment and Monitoring with a Robust Stacked Ensemble Method DOI

M. Sreedhar Babu,

S. Sreelakshmi,

S. S. Vinod Chandra

и другие.

Water Resources Management, Год журнала: 2024, Номер unknown

Опубликована: Дек. 30, 2024

Язык: Английский

Unraveling the water quality-ecosystem nexus using Kalman filter-driven models and feature analysis under uncertainty DOI
Mojtaba Poursaeid

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133092 - 133092

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

2

The application of Simulated Annealing Algorithm, Firefly Algorithm, Invasive Weed Optimization, and Shuffled Frog Leaping Algorithm for prediction of Water Quality Index DOI Creative Commons

Feridon Ghadimi,

Saeed Zolfaghari Moghaddam

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Фев. 10, 2025

Abstract Groundwater is a vital resource for drinking water, agriculture, and industry worldwide. Effective groundwater quality management crucial safeguarding public health ensuring ecological sustainability. Hydrogeochemical data modeling widely utilized to predict using various approaches. The method proposed in this study leverages an intelligent model combined with chemical compositions. Sampling was conducted from 175 agricultural wells the Arak Plain. By utilizing hydrogeochemical performing correlation sensitivity analyses, key compositions were identified: Ca²⁺, Cl⁻, EC, HCO₃⁻, K⁺, Mg²⁺, Na⁺, pH, SO₄²⁻, TDS, NO₃⁻.The predicted Water Quality Index (WQI) values composition artificial neural network (ANN) model. of served as model’s input, while WQI treated output. To enhance ANN's accuracy, several optimization algorithms used, including: Simulated Annealing Algorithm (SAA), Firefly (FA), Invasive Weed Optimization (IWO), Shuffled Frog Leaping (SFLA).The comparison results indicated that ANN-SAA outperformed other models. R² MSE predicting training data: = 0.8275, 0.0303 test 0.7357, 0.0371.These demonstrate provides reliable accurate index values, offering valuable tool assessment management.

Язык: Английский

Процитировано

0

A Novel Approach for Water Resources Management to Accurate River Flow Prediction: Utilization of Linear Programming and Weighted Ensemble Learning DOI
Mojtaba Poursaeid

Water Resources Management, Год журнала: 2025, Номер unknown

Опубликована: Май 7, 2025

Язык: Английский

Процитировано

0

Improving long-term water quality forecasting with limited data using hidden pattern extraction and explainable ensemble learning DOI
Mehdi Mohammadi Ghaleni,

Mansour Moradi,

Mahnoosh Moghaddasi

и другие.

Journal of Water Process Engineering, Год журнала: 2025, Номер 75, С. 107946 - 107946

Опубликована: Май 19, 2025

Язык: Английский

Процитировано

0

The Application of Artificial Intelligence in Predicting the Effect of Gravel and Vegetation Cover on the Urban Runoff Volume Using Experimental Data DOI
Hamidreza Ghazvinian, Hojat Karami

Water Resources Management, Год журнала: 2025, Номер unknown

Опубликована: Май 22, 2025

Язык: Английский

Процитировано

0

A new framework for water quality instabilities simulation by artificial intelligence approaches and optimization algorithms under uncertainty DOI
Mojtaba Poursaeid

Environment Development and Sustainability, Год журнала: 2025, Номер unknown

Опубликована: Май 24, 2025

Язык: Английский

Процитировано

0

A comprehensive study of water quality in the Western Black Sea: Implementation of prospective human health risk assessment in Kastamonu, Turkey DOI
Arife Şimşek, Fikret Ustaoğlu, Ekrem Mutlu

и другие.

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(6)

Опубликована: Май 30, 2025

Язык: Английский

Процитировано

0

Advancing Water Quality Assessment and Monitoring with a Robust Stacked Ensemble Method DOI

M. Sreedhar Babu,

S. Sreelakshmi,

S. S. Vinod Chandra

и другие.

Water Resources Management, Год журнала: 2024, Номер unknown

Опубликована: Дек. 30, 2024

Язык: Английский

Процитировано

1