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Mogane LK, Masebe T, Msagati TAM, Ncube E. A comprehensive review of water quality indices for lotic and lentic ecosystems. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:926. [PMID: 37420028 PMCID: PMC10329065 DOI: 10.1007/s10661-023-11512-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/10/2023] [Indexed: 07/09/2023]
Abstract
Freshwater resources play a pivotal role in sustaining life and meeting various domestic, agricultural, economic, and industrial demands. As such, there is a significant need to monitor the water quality of these resources. Water quality index (WQI) models have gradually gained popularity since their maiden introduction in the 1960s for evaluating and classifying the water quality of aquatic ecosystems. WQIs transform complex water quality data into a single dimensionless number to enable accessible communication of the water quality status of water resource ecosystems. To screen relevant articles, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to include or exclude articles. A total of 17 peer-reviewed articles were used in the final paper synthesis. Among the reviewed WQIs, only the Canadian Council for Ministers of the Environment (CCME) index, Irish water quality index (IEWQI) and Hahn index were used to assess both lotic and lentic ecosystems. Furthermore, the CCME index is the only exception from rigidity because it does not specify parameters to select. Except for the West-Java WQI and the IEWQI, none of the reviewed WQI performed sensitivity and uncertainty analysis to improve the acceptability and reliability of the WQI. It has been proven that all stages of WQI development have a level of uncertainty which can be determined using statistical and machine learning tools. Extreme gradient boosting (XGB) has been reported as an effective machine learning tool to deal with uncertainties during parameter selection, the establishment of parameter weights, and determining accurate classification schemes. Considering the IEWQI model architecture and its effectiveness in coastal and transitional waters, this review recommends that future research in lotic or lentic ecosystems focus on addressing the underlying uncertainty issues associated with the WQI model in addition to the use of machine learning techniques to improve the predictive accuracy and robustness and increase the domain of application.
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Affiliation(s)
- Lazarus Katlego Mogane
- College of Agriculture & Environmental Sciences, Department of Life and Consumer Sciences, University of South Africa, Roodepoort, Gauteng, South Africa.
| | - Tracy Masebe
- College of Agriculture & Environmental Sciences, Department of Life and Consumer Sciences, University of South Africa, Roodepoort, Gauteng, South Africa
| | - Titus A M Msagati
- College of Science, Engineering & Technology, Institute for Nanotechnology & Water Sustainability, University of South Africa, Roodepoort, Gauteng, South Africa
| | - Esper Ncube
- School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Tshwane, Gauteng, South Africa
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Georgescu PL, Moldovanu S, Iticescu C, Calmuc M, Calmuc V, Topa C, Moraru L. Assessing and forecasting water quality in the Danube River by using neural network approaches. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 879:162998. [PMID: 36966845 DOI: 10.1016/j.scitotenv.2023.162998] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/01/2023] [Accepted: 03/18/2023] [Indexed: 05/17/2023]
Abstract
The health and quality of the Danube River ecosystems is strongly affected by the nutrients loads (N and P), degree of contamination with hazardous substances or with oxygen depleting substances, microbiological contamination and changes in river flow patterns and sediment transport regimes. Water quality index (WQI) is an important dynamic attribute in the characterization of the Danube River ecosystems health and quality. The WQ index scores do not reflect the actual condition of water quality. We proposed a new forecast scheme for water quality based on the following qualitative classes very good (0-25), good (26-50), poor (51-75), very poor (76-100) and extremely polluted/non-potable (>100). Water quality forecasting by using Artificial Intelligence (AI) is a meaningful method of protecting public health because of its possibility to provide early warning regarding harmful water pollutants. The main objective of the present study is to forecast the WQI time series data based on water physical, chemical and flow status parameters and associated WQ index scores. The Cascade-forward network (CFN) models, along with the Radial Basis Function Network (RBF) as a benchmark model, were developed using data from 2011 to 2017 and WQI forecasts were produced for the period 2018-2019 at all sites. The nineteen input water quality features represent the initial dataset. Moreover, the Random Forest (RF) algorithm refines the initial dataset by selecting eight features considered the most relevant. Both datasets are employed for constructing the predictive models. According to the results of appraisal, the CFN models produced better outcomes (MSE = 0.083/0,319 and R-value 0.940/0.911 in quarter I/quarter IV) than the RBF models. In addition, results show that both the CFN and RBF models could be effective for predicting time series data for water quality when the eight most relevant features are used as input variables. Also, the CFNs provide the most accurate short-term forecasting curves which reproduce the WQI for the first and fourth quarters (the cold season). The second and third quarters presented a slightly lower accuracy. The reported results clearly demonstrate that CFNs successfully forecast the short-term WQI as they may learn historic patterns and determine the nonlinear relationships between the input and output variables.
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Affiliation(s)
- Puiu-Lucian Georgescu
- Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008, Romania; REXDAN Research Infrastructure, "Dunarea de Jos" University of Galati, 98 George Cosbuc Street, 800385 Galati, Romania
| | - Simona Moldovanu
- Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008 Galati, Romania; The Modelling & Simulation Laboratory SMlab, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008 Galati, Romania
| | - Catalina Iticescu
- Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008, Romania; REXDAN Research Infrastructure, "Dunarea de Jos" University of Galati, 98 George Cosbuc Street, 800385 Galati, Romania
| | - Madalina Calmuc
- Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008, Romania; REXDAN Research Infrastructure, "Dunarea de Jos" University of Galati, 98 George Cosbuc Street, 800385 Galati, Romania
| | - Valentina Calmuc
- Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008, Romania; REXDAN Research Infrastructure, "Dunarea de Jos" University of Galati, 98 George Cosbuc Street, 800385 Galati, Romania
| | - Catalina Topa
- Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008, Romania; REXDAN Research Infrastructure, "Dunarea de Jos" University of Galati, 98 George Cosbuc Street, 800385 Galati, Romania
| | - Luminita Moraru
- Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008, Romania; The Modelling & Simulation Laboratory SMlab, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008 Galati, Romania.
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Gorgan-Mohammadi F, Rajaee T, Zounemat-Kermani M. Investigating machine learning models in predicting lake water quality parameters as a 3-year moving average. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:63839-63863. [PMID: 37059948 DOI: 10.1007/s11356-023-26830-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 04/03/2023] [Indexed: 04/16/2023]
Abstract
Lake water quality plays a vital role in the lake ecosystem, including biotic (for living creatures, such as plants, animals, and micro-organisms) and abiotic interactions. In this research, various types of machine learning (ML) methodologies, such as classification and regression tree (CART), chi-squared automatic interaction detector (CHAID), C5 tree, quick, unbiased, and efficient statistical tree (QUEST), along with multilayer perceptron (MLP) neural network, and radial basis function (RBF) neural network, are employed to predict the concentration of water quality parameters (P, EC, TDS, pH, DO, NH3, SO4, and θ). Lake Erie is situated at the international border of the USA and Canada. The C5 tree and QUEST tree are used to classify data and predict the number of groups, while the other methods are used to predict the concentration of water quality parameters in the form of a 3-year moving average. The greater matching between the observed and predicted data of dissolved oxygen (NSE = 0.978, bias = 0.126) shows that the CART decision tree has higher accuracy in correctly detecting the concentration of this parameter. The C5 tree could identify 33 groups correctly out of 36 total groups, which shows better accuracy for the C5 tree in classifying the data for this parameter.
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Affiliation(s)
| | - Taher Rajaee
- Department of Civil Engineering, University of Qom, Qom, Iran
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A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions. ENVIRONMENTS 2022. [DOI: 10.3390/environments9070085] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.
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Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing. HYDROLOGY 2022. [DOI: 10.3390/hydrology9070115] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models.
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