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

и другие.

Heliyon, Год журнала: 2024, Номер 10(13), С. e33695 - e33695

Опубликована: Июнь 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.

Язык: 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

и другие.

Environmental Monitoring and Assessment, Год журнала: 2023, Номер 195(4)

Опубликована: Март 8, 2023

Язык: Английский

Процитировано

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

и другие.

Water, Год журнала: 2023, Номер 15(12), С. 2244 - 2244

Опубликована: Июнь 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.

Язык: Английский

Процитировано

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

и другие.

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 58, С. 102227 - 102227

Опубликована: Фев. 17, 2025

Язык: Английский

Процитировано

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

и другие.

Environmental Pollution, Год журнала: 2022, Номер 318, С. 120870 - 120870

Опубликована: Дек. 13, 2022

Язык: Английский

Процитировано

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

Computation, Год журнала: 2023, Номер 11(2), С. 16 - 16

Опубликована: Янв. 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).

Язык: Английский

Процитировано

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

Ecological Indicators, Год журнала: 2023, Номер 150, С. 110202 - 110202

Опубликована: Апрель 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.

Язык: Английский

Процитировано

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

и другие.

Environmental Pollution, Год журнала: 2023, Номер 335, С. 122358 - 122358

Опубликована: Авг. 9, 2023

Язык: Английский

Процитировано

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

и другие.

Journal of Environmental Management, Год журнала: 2023, Номер 344, С. 118398 - 118398

Опубликована: Июнь 15, 2023

Язык: Английский

Процитировано

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

и другие.

Water, Год журнала: 2023, Номер 15(19), С. 3512 - 3512

Опубликована: Окт. 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.,

Язык: Английский

Процитировано

27
Water Quality Index Assessment of River Ganga at Haridwar Stretch Using Multivariate Statistical Technique DOI
Abdul Gani, Shray Pathak, Athar Hussain

и другие.

Molecular Biotechnology, Год журнала: 2023, Номер unknown

Опубликована: Сен. 20, 2023

Язык: Английский

Процитировано

25