Water quality ensemble prediction model for the urban water reservoir based on the hybrid long short-term memory (LSTM) network analysis DOI Creative Commons
Kai He, Yu Liu, Jinlong Yuan

et al.

AQUA - Water Infrastructure Ecosystems and Society, Journal Year: 2024, Volume and Issue: 73(8), P. 1621 - 1642

Published: July 15, 2024

ABSTRACT The water quality of drinking reservoirs directly impacts the supply safety for urban residents. This study focuses on Da Jing Shan Reservoir, a crucial source Zhuhai City and Macau Special Administrative Region. aim is to establish prediction model reservoirs, which can serve as vital reference plants when formulating their plans. In this research, after smoothing data using Hodrick-Prescott filter, we utilized long short-term memory (LSTM) network create Reservoir. Simulation calculations reveal that model's fitting degree consistently above 60%. Specifically, accuracy pH, dissolved oxygen (DO), biochemical demand (BOD) in aligns with actual results by more than 70%, effectively simulating reservoir's changes. Moreover, parameters such DO, BOD, total phosphorus, relative forecasting error LSTM less 10%, confirming validity. offer an essential predicting

Language: Английский

Machine learning models for predicting water quality index: optimization and performance analysis for El Moghra, Egypt DOI Creative Commons
Mohamed Kamel Elshaarawy, Mohamed Galal Eltarabily

Water Science & Technology Water Supply, Journal Year: 2024, Volume and Issue: 24(9), P. 3269 - 3294

Published: Aug. 16, 2024

ABSTRACT Assessing groundwater quality is vital for irrigation, but financial constraints in developing countries often result infrequent sampling. This study comprehensively analyzes the of El Moghra aquifer Egypt's arid Western Desert, its suitability irrigation uses. Detailed hydrochemical analysis and advanced machine learning (ML) techniques, including geographic information systems, were employed to enhance spatial predictive accuracy. Various ML models, such as random forest, adaptive boosting, extreme gradient boosting (XGBoost), optimized using Bayesian optimization predict water index (IWQI) accurately. The evaluation incorporated visual quantitative methods, alongside ranking analysis, validate model effectiveness. Shapley Additive exPlanations feature importance a graphical user interface (GUI) developed based on best model. results indicated that generally suitable with XGBoost showing performance, achieving root mean square error 5.602 determination coefficient (R²) 0.872. Sodium concentration was identified most significant factor affecting IWQI. GUI facilitates easy prediction IWQI, aiding agricultural management resource allocation within region.

Language: Английский

Citations

21
Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques DOI
Mustafa El-Rawy, Mohamed Wahba, Heba Fathi

et al.

Marine Pollution Bulletin, Journal Year: 2024, Volume and Issue: 205, P. 116645 - 116645

Published: June 25, 2024

Language: Английский

Citations

19
Machine learning and GIS based groundwater quality prediction for agricultural practices - A case study form Arjunanadi River basin of South India DOI

Mohan Raj,

D. Karunanidhi,

N. Subba Rao

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 229, P. 109932 - 109932

Published: Jan. 16, 2025

Language: Английский

Citations

6
Comparative Assessment of Machine Learning Models for Groundwater Quality Prediction Using Various Parameters DOI
Majid Niazkar, Reza Piraei, Mohammad Reza Goodarzi

et al.

Environmental Processes, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 11, 2025

Language: Английский

Citations

4
Information and Analytical System Monitoring and Assessment of the Water Bodies State in the Mineral Resources Complex DOI Creative Commons
Olga Afanaseva,

Mikhail Afanasyev,

Semyon Neyrus

et al.

Inventions, Journal Year: 2024, Volume and Issue: 9(6), P. 115 - 115

Published: Nov. 12, 2024

Currently, one of the most pressing global issues is ensuring that human activities have access to water resources meet essential quality standards. This challenge addressed by implementing a series organizational and technical measures aimed at preserving ecology basins reducing level harmful industrial emissions other pollutants in aquatic environment. To guarantee necessary resources, monitoring conducted based on selected parameters using various methods means control. From these results, suitable are formulated applied maintain quality. Various scientific works extensively discuss different approaches management compliance with specified requirements. Modern strategies for developing systems leverage capabilities information collect, process, store, transmit information, enabling resolution geographically distributed bodies real time. paper proposes an approach employs mathematical identify significant factors determining assess their interrelations priori ranking, multivariate correlation regression analysis, integral quantitative assessment. A hardware software solution development unified integrated analytical system proposed. enables continuous assessment set key parameters, addressing range critical tasks. provides detailed description product, presents demonstration real-world data, discusses anticipated benefits such system.

Language: Английский

Citations

11
Improving groundwater quality predictions in semi-arid regions using ensemble learning models DOI

Mojtaba Mahmoudi,

Amin Mahdavi‐Meymand, Ammar Aldallal

et al.

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

Published: Jan. 4, 2025

Language: Английский

Citations

1
Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution DOI Open Access
Selma Toumi, Sabrina Lekmine, Nabil Touzout

et al.

Water, Journal Year: 2024, Volume and Issue: 16(23), P. 3380 - 3380

Published: Nov. 24, 2024

This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance accuracy, speed, accessibility monitoring. Data collected from various samples in Algeria were analyzed determine key such as conductivity, turbidity, pH, total dissolved solids (TDS). These measurements integrated into deep neural networks (DNNs) predict indices sodium adsorption ratio (SAR), magnesium hazard (MH), percentage (SP), Kelley’s (KR), potential salinity (PS), exchangeable (ESP), well Water Quality Index (WQI) Irrigation (IWQI). DNNs model, optimized through selection activation functions hidden layers, demonstrated high precision, with a correlation coefficient (R) 0.9994 low root mean square error (RMSE) 0.0020. AI-driven methodology significantly reduces reliance traditional laboratory analyses, offering real-time assessments that are adaptable local conditions environmentally sustainable. provides practical solution resource managers, particularly resource-limited regions, efficiently monitor make informed decisions public health agricultural applications.

Language: Английский

Citations

7
Suitability of Water Quality for Irrigation Purposes Using GIS‐Based Irrigation Water Quality Index DOI
Mehmet Şi̇mşi̇r, Sayiter Yıldız, Can Bülent Karakuş

et al.

Irrigation and Drainage, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

ABSTRACT In this study, surface water quality was assessed on the basis of irrigation indices and index (IWQI) via GIS. The study carried out analyses samples collected in August (dry) November (wet) 2023 from 12 designated points along Yıldız River Sivas. sodium adsorption ratio (SAR), Kelly (KI), percentage (Na%), permeability (PI), residual carbonate (RSC), magnesium hazard (MH) IWQI were calculated to determine classification quality. Additionally, Ca 2+ , Cl − Fe K + HCO 3 Mg Mn, Na pH SO 4 2− conducted samples. spatial distributions parameters mapped GIS, assessment performed according US Salinity Diagram standards. values ranged 401 61 during rainy season 42 67 dry season. season, two classified as ‘poor (MR: moderate restriction, IWQI: 55–70)’ nine ‘very poor (HR: high 40–55)’. three restriction)’ restriction)’. According Diagram, majority both seasons fell into categories C3S1 (high‐salinity hazard–low‐sodium hazard) C2S1 (medium‐salinity hazard), respectively. results highlight effectiveness these methodologies evaluating quality, assisting development informed management strategies for sustainable resource use agricultural environments. has proven be a good tool assessing area managing can help decision makers manage resources more effectively agriculture.

Language: Английский

Citations

0
Integration of positive matrix factorization and water quality models for pollution source identification and water quality enhancement in rivers DOI Creative Commons
Semin Kim

Applied Water Science, Journal Year: 2025, Volume and Issue: 15(3)

Published: Feb. 18, 2025

Language: Английский

Citations

0
Hydrogeochemical processes regulating groundwater quality and its suitability for drinking purposes in the recent alluvial plain, Blue Nile Region, Sudan DOI

Yousif Hassan Mohamed Salh,

Chunli Su, Javed Iqbal

et al.

Environmental Geochemistry and Health, Journal Year: 2025, Volume and Issue: 47(4)

Published: March 5, 2025

Language: Английский

Citations

0