Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China DOI Creative Commons

Jing Xu,

Yuming Mo,

Senlin Zhu

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(13), P. e33695 - e33695

Published: June 28, 2024

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous parameters, making sample collection and laboratory analysis time-consuming costly. This study aimed to identify key parameters the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water from 2020 2023 were collected including nine biophysical chemical indicators in seventeen rivers Yancheng Nantong, two coastal cities Jiangsu Province, China, adjacent Yellow Sea. Linear regression seven machine learning (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) Stochastic (SGB)) developed predict WQI different groups input variables based on correlation analysis. results indicated improved 2022 but deteriorated 2023, with inland stations exhibiting better conditions than ones, particularly terms turbidity nutrients. environment was comparatively Nantong Yancheng, mean values approximately 55.3–72.0 56.4–67.3, respectively. classifications "Good" "Medium" accounted 80 % records, no instances "Excellent" 2 classified as "Bad". performance all models, except SOM, addition variables, achieving R2 higher 0.99 such SVM, RF, XGB, SGB. RF XGB total phosphorus (TP), ammonia nitrogen (AN), dissolved oxygen (DO) (R2 = 0.98 0.91 training testing phase) predicting values, TP AN (accuracy 85 %) grades. "Low" grades highest at 90 %, followed by level 70 %. model contribute efficient evaluation identifying facilitating effective management basins.

Language: Английский

Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques DOI
Md Ataul Gani, Abdul Majed Sajib, Md. Abubakkor Siddik

et al.

Environmental Monitoring and Assessment, Journal Year: 2023, Volume and Issue: 195(4)

Published: March 8, 2023

Language: Английский

Citations

56
Water Quality Evaluation and Prediction Using Irrigation Indices, Artificial Neural Networks, and Partial Least Square Regression Models for the Nile River, Egypt DOI Open Access
Mohamed Gad, Ali Saleh, Hend Hussein

et al.

Water, Journal Year: 2023, Volume and Issue: 15(12), P. 2244 - 2244

Published: June 15, 2023

Water quality is identically important as quantity in terms of meeting basic human needs. Therefore, evaluating the surface-water and associated hydrochemical characteristics essential for managing water resources arid semi-arid environments. present research was conducted to evaluate predict agricultural purposes across Nile River, Egypt. For that, several irrigation indices (IWQIs) were used, along with an artificial neural network (ANN), partial least square regression (PLSR) models, geographic information system (GIS) tools. The physicochemical parameters, such T °C, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, NO3−, measured at 51 locations. As a result, ions contents following: Ca2+ > Na+ Mg2+ K+ HCO3− Cl− SO42− NO3− reflecting Ca-HCO3 mixed Ca-Mg-Cl-SO4 types. index (IWQI), sodium adsorption ratio (SAR), percentage (Na%), soluble (SSP), permeability (PI), magnesium hazard (MH) had mean values 92.30, 1.01, 35.85, 31.75, 72.30, 43.95, respectively. instance, IWQI readings revealed that approximately 98% samples inside no restriction category, while 2% fell within low area irrigation. ANN-IWQI-6 model’s six indices, R2 0.999 calibration (Cal.) 0.945 validation (Val.) datasets, are crucial predicting IWQI. rest models behaved admirably SAR, Na%, SSP, PI, MR Cal. Val. 0.999. findings ANN PLSR effective methods assist decision plans. To summarize, integrating features, WQIs, ANN, PLSR, GIS tools suitability offers complete image sustainable development.

Language: Английский

Citations

45
Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis DOI Creative Commons
Zhan Xie,

Weiting Liu,

Si Chen

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 58, P. 102227 - 102227

Published: Feb. 17, 2025

Language: Английский

Citations

2
Prediction of long-term water quality using machine learning enhanced by Bayesian optimisation DOI
Tao Yan, Annan Zhou, Shui‐Long Shen

et al.

Environmental Pollution, Journal Year: 2022, Volume and Issue: 318, P. 120870 - 120870

Published: Dec. 13, 2022

Language: Английский

Citations

53
Efficient Data-Driven Machine Learning Models for Water Quality Prediction DOI Creative Commons
Ηλίας Δρίτσας, Μαρία Τρίγκα

Computation, Journal Year: 2023, Volume and Issue: 11(2), P. 16 - 16

Published: Jan. 18, 2023

Water is a valuable, necessary and unfortunately rare commodity in both developing developed countries all over the world. It undoubtedly most important natural resource on planet constitutes an essential nutrient for human health. Geo-environmental pollution can be caused by many different types of waste, such as municipal solid, industrial, agricultural (e.g., pesticides fertilisers), medical, etc., making water unsuitable use any living being. Therefore, finding efficient methods to automate checking suitability great importance. In context this research work, we leveraged supervised learning approach order design accurate possible predictive models from labelled training dataset identification suitability, either consumption or other uses. We assume set physiochemical microbiological parameters input features that help represent water’s status determine its class (namely safe nonsafe). From methodological perspective, problem treated binary classification task, machine models’ performance (such Naive Bayes–NB, Logistic Regression–LR, k Nearest Neighbours–kNN, tree-based classifiers ensemble techniques) evaluated with without application balancing (i.e., nonuse Synthetic Minority Oversampling Technique–SMOTE), comparing them terms Accuracy, Recall, Precision Area Under Curve (AUC). our demonstration, results show Stacking model after SMOTE 10-fold cross-validation outperforms others Accuracy Recall 98.1%, 100% AUC equal 99.9%. conclusion, article, framework presented support researchers’ efforts toward quality prediction using (ML).

Language: Английский

Citations

36
A hybrid approach based on Monte Carlo simulation-VIKOR method for water quality assessment DOI Creative Commons
Xi Yang, Zhihe Chen

Ecological Indicators, Journal Year: 2023, Volume and Issue: 150, P. 110202 - 110202

Published: April 3, 2023

Under the dual influence of global climate change and human activities, river water environment is facing more serious problems challenges. Assessing quality great significance for promoting regional sustainable development. Currently, traditional assessment methods usually do not consider uncertainty data in collection process, which limits application these methods. In order to overcome above shortcomings, this study constructed a method by integrating Monte Carlo (MC), CRITIC VIKOR methods, applied it assess Songhua River tributary. Results indicate that: (1) The two sampling points area level III, consistent with actual situation; (2) This can caused error improve credibility evaluation results; (3) Total nitrogen (TN), potassium permanganate index (PPI) ammonia (NH3-N) are factors related results. When decision coefficient mechanism λ taken [0.1–0.5], outcomes line real water. addition, we recommend that distribution profile generated based on measured should obey probability density curve decreases from middle tail both sides. findings paper provide scientific basis makers carry out restoration management.

Language: Английский

Citations

31
Advances and applications of machine learning and deep learning in environmental ecology and health DOI
Shixuan Cui, Yuchen Gao, Yizhou Huang

et al.

Environmental Pollution, Journal Year: 2023, Volume and Issue: 335, P. 122358 - 122358

Published: Aug. 9, 2023

Language: Английский

Citations

31
Water quality pollution assessment and source apportionment of lake wetlands: A case study of Xianghai Lake in the Northeast China Plain DOI
Jin Gao, Guangyi Deng, Haibo Jiang

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 344, P. 118398 - 118398

Published: June 15, 2023

Language: Английский

Citations

28
Application of the Weighted Arithmetic Water Quality Index in Assessing Groundwater Quality: A Case Study of the South Gujarat Region DOI Open Access

Divya D. Patel,

Darshan Mehta, Hazi Mohammad Azamathulla

et al.

Water, Journal Year: 2023, Volume and Issue: 15(19), P. 3512 - 3512

Published: Oct. 8, 2023

Groundwater is a natural resource used for drinking, agriculture, and industry, apart from surface water. Its quality should be assessed regularly, the condition of water resources maintained accordingly. The most common analytical method describing assessing general Water Quality Index (WQI). This study aims to assess South Gujarat Region’s groundwater using WQI. Various physicochemical parameters like pH, turbidity, total dissolved solids, hardness, calcium, magnesium, chloride, sulphate, nitrate, fluorides, alkalinity are considered present study. data period 2018 2022 same. Weighted Arithmetic Technique evaluate these data. For checking potability within acceptable limit, Indian Standard Drinking Specification code (IS: 10050-2012) adopted. According mentioned above, few wells’ has been found higher than WQI value. It also observed that four wells were unsuitable drinking purposes in 2018. noted if value above 51, it harmful human health; therefore, requires some kind processing before use. will beneficial policymakers identifying providing details about form specific value, i.e.,

Language: Английский

Citations

27
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