Comparative Evaluation of Methods and Techniques for Assessing Water Quality

Comparative Evaluation of Methods and Techniques for Assessing Water Quality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Evaluation of Methods and Techniques for Assessing Water Quality Jitendra Pandey, Anjum Zameer Bhat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7453617/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Assessing water high-quality is critical for making sure human fitness and environmental sustainability. This observe gives a complete comparative evaluation of commonly used techniques for water great assessment, together with bodily and chemical analyses, biological tracking, and remote sensing techniques. Each technique is severely tested to spotlight its strengths and limitations in shooting the complexity of water great parameters. Physical and chemical analyses offer precise measurements of parameters which include pH, dissolved oxygen, and nutrient ranges, but they will fail to deal with the dynamic interactions inside aquatic ecosystems. Biological tracking, which employs indicator species and network analyses, presents holistic insights into ecosystem health however may be time-extensive and might lack specificity in figuring out pollutants. Remote sensing permits efficient monitoring of huge water bodies, imparting spatial perspectives on water pleasant; however, it may be constrained in detecting positive parameters and is liable to atmospheric interferences. The observe similarly evaluates the forms of records produced by way of each method, that specialize in elements together with accuracy, price-effectiveness, and time performance. Drawing on statistics from numerous water sources, such as rivers, lakes, and wells, it assesses the applicability of every technique to special tracking objectives. Additionally, the studies explores recent improvements in water exceptional evaluation technology, such as biosensors, automated systems, and advanced statistics analytics, which promise real-time monitoring, improved accuracy, and more efficiency. This comparative analysis provides actionable insights for water control agencies, stakeholders, and researchers, helping the choice of suitable methods for particular water fine evaluation desires. By expertise the skills and barriers of each method, this paintings contributes to the enhancement of water nice assessment methodologies and supports the improvement of effective strategies for water useful resource management and environmental conservation. WQI Data Analytics Potability Machine Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Access to clean and safe water is a basic human right, as well as an important part of environmental sustainability. Assuring water quality requires sound monitoring and assessment to identify possible contaminants, pollutants, and general ecosystem health. While traditional approaches, such as physical and chemical analyses, give valuable insights into specific parameters, modern water quality assessment demands more integrated and subtle methodologies. The present study presents a critical review of major methodologies adopted in the assessment of water quality, such as physical, chemical, and biological techniques. Physical analysis entails parameters such as temperature, turbidity, and pH, which provide an insight into the physical characteristics of the water body. Chemical analysis entails the determination of the dissolved constituents in water, namely oxygen levels, conductivity, and nutrient levels. Biological analysis utilizes the presence and diversity of aquatic organisms to indicate the general view of ecosystem health. New sensor technologies, miniaturization of analytical equipment, and development of novel techniques like bioassays and DNA-based techniques are currently transforming water quality monitoring. Such approaches could lead to faster, more accurate, and integrated measurement that can make up for many deficiencies in the use of traditional approaches. With these developments, consensus has not been reached on the most effective and efficient methods of water quality evaluation. This study will, therefore, fill this gap by comparing the various techniques developed to show their strengths and weaknesses. Data from rivers, lakes, and groundwater analyzed in presenting applicability of each method to various monitoring objectives. The outcome of the present study would be of immense worth to the authorities of water management, policymakers, and researchers in the field of water resource management. This would help to explore the advantages and disadvantages of alternative methods for making decisions and building better schemes that develop efficient water quality monitoring programs. Moreover, the study contributes to the ongoing methods development on water quality assessment, with the intent of protection of human health and conservation of aquatic ecosystems. 2 Problem statement In a generation in which access to clean water is essential for both human fitness and environmental sustainability, the precise evaluation of water nice has never been more critical. Despite advancements in analytical techniques and generation, a fundamental venture persists: the absence of a universally recognized 'gold preferred' for evaluating water quality. This loss of consensus poses massive demanding situations for researchers, water control authorities, and stakeholders. Prime difficulty lies within the inherent variability of water fine evaluation methodologies. Techniques starting from conventional body and chemical analyses to progressive organic monitoring and faraway sensing frequently yield disparate consequences, complicating efforts to derive consistent and comprehensive reviews of water sources. This inconsistency can lead to misinterpretations, in the long run impeding powerful selection-making in water useful resource management. Moreover, the complexities of those methodologies, coupled with restrained understanding of their respective strengths and boundaries, similarly prevent the selection of the maximum suitable method for precise tracking objectives. This information gap dangers inefficient useful resource allocation and inaccuracies in water best assessments, probably jeopardizing public health and environmental protection. To address those demanding situations, this study undertakes a rigorous comparative analysis of broadly hired water first-class assessment strategies. By systematically comparing their benefits, obstacles, and applicability to numerous water assets and tracking desires, this looks at pursuits to provide essential insights and realistic steering for knowledgeable selection-making in water great control. The findings of this study aspire to contribute to the improvement of extra standardized and reliable water high-quality assessment practices. By improving the precision and effectiveness of monitoring efforts, this analysis helps the broader purpose of making sure safe and sustainable water resources for gift and destiny generations. 3 Literature Review Sánchez-Quiles et al. (2018) conducted an intensive study in a Mediterranean coastal environment on water quality, considering traditional methods such as chemical and microscopic organism analysis in comparison with the latest advanced techniques of DNA-based methods. Indeed, the results proved to be so captivating; it reflected that such advanced approaches lead to higher precision in defining the real status of the aquatic environment. The present study points to the possibility that emerging technologies could serve as game-changers for a whole paradigm shift in the process of water quality assessment. Sharma et al. (2019) aimed to analyze the levels of pollution in one of the Indian rivers using a combination of a few classical approaches-such as simple chemical tests-along with advanced DNA-based techniques. Their findings have illustrated the unprecedented sensitivity of these novel techniques, which can reveal even trace levels of pollutants that may remain undetectable with traditional approaches. This work is illustrative of the power of integration of advanced scientific tools for the realization of precise and detailed knowledge about water quality. Chen et al. (2020): Chen et al. studied the water quality in a tropical watershed through a multi-proxy approach, ranging from chemical analyses to biological monitoring and real-time sensor technologies. The authors indicated that a multiple approach is necessary for integrated understanding of the quality of water. By integrating different methods, akin to consulting various specialists, the research showcased how each tool contributes unique insights. This study emphasizes the critical role of methodological diversity in achieving comprehensive water quality assessments. 4 Methodology This study will adopt a multi-faceted approach to evaluate and compare traditional and advanced water quality assessment methods. The research is structured into the following key stages: 1. Water Sample Collection Water samples will be collected from a wide range of sources, including rivers, lakes, and groundwater wells. Sampling shall be done at frequent intervals throughout the year to show seasonal changes and environmental impacts. This ensures a representative dataset reflecting the dynamic nature of water quality. 2. Water Sample Analysis The collected samples will be analyzed by various conventional and modern techniques: Traditional Methods: Physical Analysis: Temperature, conductivity, and TDS measurement to evaluate some of the basic physical parameters. Chemical Analysis: Determination of pH, DO, turbidity, and nutrient levels will help in assessing the chemical composition and possible contamination of water. Biological Analysis: Assessment of indicator organisms and biodiversity indices provides information on ecological health and the impact of pollution. Advanced Methods: DNA-Based Approaches : qPCR and metagenomics for the identification and quantification of microbial communities provide complementary information on the potential pathogens and ecosystem functioning. Bioassays: Toxicity and bioaccumulation tests shall be performed to assess the effect of pollutants on aquatic organisms. 3. Data Analysis An integrative analytical approach will be performed to compare the performances of different assessment methods: t-tests and ANOVA are performed to point out significant differences between traditional and advanced methods. Multivariate Analysis : By principal component analysis or factor analysis, identify the key factors driving water quality variations and define the correlations among parameters. Cluster Analysis : Grouping of water samples by their quality characteristics to assist in the identification of pollution sources and areas of poor quality. Geographic Information Systems : Spatial mapping of the water quality data will yield geographical patterns, trends, and hotspots of potential pollution. 4. Machine Learning Applications The machine learning techniques for enhancing predictive capabilities and finding complex patterns will be applied as follows: Supervised Learning : The regression analysis, decision trees, and Random Forest algorithms will be implemented on a set of input variables for predicting the water quality parameters. Unsupervised Learning: K-means clustering and hierarchical clustering will group the water samples based on similar characteristics. Deep Learning: ANNs and CNNs will pick up complex trends in large datasets comprising sensor data and imagery. Natural Language Processing: Sentiment analysis and topic modeling for extracting insight from unstructured text data represented by public complaints, social media posts, and news articles related to water quality. OBJECTIVE AND CONTRIBUTION This will provide a proper and realistic comparison of various water quality assessment methodologies. Indeed, this research work, by comparing conventional and state-of-the-art techniques using advanced analytical tools, will underpin informed decision-making in water resources management and help in elaborating more efficient policies of environmental protection. Water Quality Indicators and Data Acquisition Procedures This research work embraced a holistic set of water quality indicators backed by a strict procedure for data collection to enable strong comparability among the various methods of assessment. The adopted indicators range from physical to chemical and biological parameters, while sampling and purification methods ensure valid and accurate data. Water Quality Indicators Physical Parameters Temperature (°C): Thermal condition and possible thermal pollution. Turbidity (NTU): Reflects water clarity, which is modified by suspended particles and indicates sediment or pollutant loads. Total Dissolved Solids (TDS) (mg/L): The concentration of dissolved inorganic and organic substances in water, reflecting its mineral content. Total Suspended Solids (TSS) (mg/L): The amount of solid particles suspended in water affects turbidity and can impact aquatic ecosystems. Chemical Parameters pH: Shows the acidity or alkalinity, very important for the suitability of water and for life in water. Dissolved Oxygen (DO) (mg/L): The amount of oxygen available for aquatic respiration, thus being a major indicator of water quality. Conductivity (µS/cm): Shows the ability of water to conduct electricity, which is affected by dissolved ions, salinity, or other pollutants. Nitrate (mg/L): Indicates nitrogen levels, usually from agricultural runoff or wastewater contamination. Chloride (mg/L): Indicates salinity, industrial waste, or road salt contamination. Sample Collection Techniques The following sampling techniques shall be used to ensure that the data collected is representative: Grab Sampling: This is a single sample taken at a specific place and time with the intention of having a snapshot of the quality of the water. Samples will be collected in sterile containers and preserved according to standard protocols. Composite Sampling: Several samples are taken over time at one location and combined to represent average water quality conditions over a period. Automated sampling shall be done through the use of automated equipment at fixed intervals to make monitoring continuous, and time-series data could be generated to show trends. Selection of a sampling technique shall be informed by research objectives, water body characteristics, and available resources. Data Cleaning Approaches The following cleaning approaches will be used to make the dataset valid and consistent: Data Validation: Missing values, outliers, and inconsistencies with the standard ranges will undergo strenuous checks. Data Standardization: The measurements will be uniformly transformed into a unit of measure or normalized to compare metrics and scales. Data Deduplication: Entries found duplicate shall be removed in order to avoid bias. Data Imputation: Missing values will be estimated by choosing proper statistical methods. These are selected based on the pattern of the data and potential effects on the results of the data analysis. This strong basis of metrics selection, data collection, and purification forms a very sound basis for the comparative analysis of the methods applied in water quality assessment. Integrating diversity into the parameters with rigorous data handling, this study aims at delivering actionable insights to advance the monitoring and management of water quality. 5 Data Set The trained dataset has around 2000 records and testing dataset has around 4000 records. Nine different parameters have been used to assess the potability aspect for a given sample. Above graphs show the distribution curve of various parameters. Data has been divided into maximum ten ranges so as to understand the spread. Above graphs show the impact of parameters on water potability. 6 Results and Discussion Three different methods have been compared in this study, of which XGBoost Classifier with an accuracy of 78.54% gives a great performance. Table 1 WQI Classifier Weightage Position Name Weight 1 Sulfate 39.67% 2 Solids 33.93% 3 Hardness 7.47% 4 Chloramines 5.88% 5 Organic_carbon 3.54% 6 Conductivity 3.53% 7 Trihalomethanes 3.43% 8 Turbidity 2.55% Decision Tree Figure 2 Shows how the algorithm works to decide the predicted outcome. Top 3 layers have been shown here to depict the tree model. This tree considers all parameters and works on calculating the potability rate depending on the WQI criteria as set by various regulatory agencies Confusion Matrix Explore false positives and false negatives with this chart. The confusion matrix shows that 457 rows were correctly predicted as potable, whereas 469 rows of data were correctly predicted as unpotable. Hence from the above confusion matrix we conclude that selected algorithm satisfies with the accuracy of 78.54% which is normally treated as moderately successful. Figure 4 shows how the actual vs. predicted values stack up. And the accuracy rate is almost 74.57% which supports the model used in the research. Another model LightGBM Classifier with an accuracy of 63.25%. This is also a good performance. Table 2 WQI Parameters Weightage Position Name Weight 1 Solids 54.94% 2 Sulfate 13.66% 3 Hardness 10.63% 4 Trihalomethanes 7.00% 5 Organic_carbon 5.15% 6 Conductivity 4.01% 7 Chloramines 2.87% 8 Turbidity 1.74% Another model Random Forest Classifier with an accuracy of 61.42%. This is also a good performance. Table 3 WQI Parameters Weightage Position Name Weight 1 Chloramines 14.65% 2 Trihalomethanes 13.61% 3 Hardness 13.49% 4 Sulfate 13.37% 5 Organic_carbon 12.53% 6 Solids 10.93% 7 Conductivity 10.80% 8 Turbidity 10.64% So out of the three models which were tested, XGBoost Classifier algorithm was found to be more accurate and reliable. This is a comprehensive list of model’s technical metrics using XGBoost Classifier algorithm. Table 4 Summary of best performance Driver Value Training Accuracy 80.90% Validation Accuracy 79.33% Testing Accuracy 78.54% F-Score 0.7854 Precision 0.7872 Recall 0.7867 Area Under Curve 0.8702 Number of Iterations 26046 Testing / Training Split 80 / 20 There were 26046 iterations done by the models and the data was split into 4:1 for testing and training data. Algorithm tested the dataset with various values and found that Highest likelihood for Potability to be 1 and Probability = 100.00%, the parameters will have the following values. Table 5 Average values of WQI parameters Driver Value Chloramines 4.278 Conductivity 690.369 Hardness 81.081 Organic_carbon 15.976 Solids 51857.73 Sulfate 143.908 Trihalomethanes 22.874 So this research suggests that the XGBoost Classifier algorithm can be useful in predicting the water potability when working with the given set of parameters. Although various other factors may also be responsible for the same. 7 Critical Analysis The critical analysis of the study "Assessing Water Quality: A Comparative Analysis of Methods and Techniques" involves evaluation on aspects of strengths, shortcomings, implications, and areas that can potentially lead to further research. The paper presents the wide-ranging outlook of various methodologies and techniques that are in use for water quality assessment-both traditional ones involving physical, chemical, and biological analysis, and advanced methods such as DNA-based and bioassays. Thus, the approach given above regarding the multifunctional view to water quality can allow substantial evaluations of many advantages and/or shortcomings in all methods of water assessment. Meanwhile, adding machine learning analysis methodologies provides new insights that can potentially boost the outcomes in terms of efficiency and accuracy for any water quality analysis. Admittedly, such limitations exist regarding some weaknesses within this research. Using machine learning methods is mentioned as part of analysis; however, further specification related to concretely used algorithms and their parametric setting is absent. Also, while the procedures for data cleansing are briefly mentioned, they are not fully elaborated, which may compromise the reproducibility of the study and make its results less applicable by other researchers or practitioners. Further, a critical look at the water quality metrics used-things like their justification and limitations-could help to add weight and strength to the methodology. 8 Scope for future work While these limitations have been identified, the current research nonetheless constitutes a valuable contribution to the field of water quality management and offers insights relevant to both researchers and practitioners. For that matter, the present comparative analysis constitutes a practical guide for the choice of techniques that best suit particular monitoring objectives and resource constraints. Besides, the integration of machine learning in this study presents potential for enhanced evaluation of water quality due to increased accuracy, efficiency, and predictive capability. These limitations would be overcome if the future research effort engaged in a more detailed description of machine learning algorithms and methods adopted for data purification. A deeper analysis, with respect to sensitivity, selectivity, and possible interferences for each of the water quality metrics used, will add value to the methodological strength of a similar study. Also, extending the scope of research to involve more types of water bodies and various conditions of the environment would provide broader generalizations of findings that are more applicable. 9 Conclusion This paper presents an overall assessment of water quality evaluation techniques, considering both conventional and advanced methods. The results demonstrate how method selection should meet the purpose for which the investigation is being carried out, along with those characteristics that differentiate one water body from another. Significantly, it has been indicated in this research that integrated approaches using different valuation methods are desirable to arrive at a more comprehensive realistic assessment of water quality. The study has shown the enormous potential of machine learning techniques in the analysis of water quality data and increasing the accuracy of estimates. It also emphasizes that machine learning should complement and not replace traditional methods to ensure a comprehensive and nuanced assessment. The research also underlines the important role that strict data cleansing procedures play in maintaining data quality and reliability. It also emphasizes effective data imputation techniques in respect to missing values as an indispensable part of a good data management system. Declarations Funding Declaration – No external funding was received for this research. Ethics, Consent to Participate, and Consent to Publish declarations: Not Applicable. Author Contribution JP worked on the literature review and analysis part, AB worked on the methodology testing and conclusion. References APHA, AWWA, WEF (2012) Standard Methods for the Examination of Water and Wastewater, 22nd edition. American Public Health Association, American Water Works Association, and Water Environment Federation, Washington, D.C. Bai, Y., Li, X., & Liu, Y. (2018). 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1","display":"","copyAsset":false,"role":"figure","size":96912,"visible":true,"origin":"","legend":"\u003cp\u003eML Techniques (Source -https://miro.medium.com)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7453617/v1/964f0fa973e5599d7583a862.png"},{"id":93050711,"identity":"e4af166c-ce57-41c8-bfcf-4844a966858f","added_by":"auto","created_at":"2025-10-08 14:09:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":179798,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution curve for various parameters\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7453617/v1/7ebe2b56bbc19edc155a6155.png"},{"id":93050712,"identity":"2ac3ef07-b23c-473a-979c-d5c8283dfa1b","added_by":"auto","created_at":"2025-10-08 14:09:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226858,"visible":true,"origin":"","legend":"\u003cp\u003eImpact on potability graph comparison of all parameters.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7453617/v1/6d120290f91c95756688db22.png"},{"id":93050715,"identity":"a440715d-87c7-4bb1-b381-5f930499ba38","added_by":"auto","created_at":"2025-10-08 14:09:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40784,"visible":true,"origin":"","legend":"\u003cp\u003eDecision Tree\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7453617/v1/84eae1e3aa61d7df44f29246.png"},{"id":93050732,"identity":"1739267a-1985-4fa2-b5e8-a6ccd91b766e","added_by":"auto","created_at":"2025-10-08 14:09:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39270,"visible":true,"origin":"","legend":"\u003cp\u003eConfusion Matrix\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7453617/v1/4bf7fcb5b29f0c87f54c795d.png"},{"id":93050713,"identity":"19fc2bce-3900-4941-8840-be9470ac9a67","added_by":"auto","created_at":"2025-10-08 14:09:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":15506,"visible":true,"origin":"","legend":"\u003cp\u003eActual vs. Predicted\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7453617/v1/dfbb0b6e3d0482ef04347cda.png"},{"id":94184748,"identity":"397f34b4-08da-4a46-b467-29fa0b10e5b7","added_by":"auto","created_at":"2025-10-23 10:24:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1309246,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7453617/v1/1ecf1fdc-1547-44ba-8a35-8b2e3f11387d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Evaluation of Methods and Techniques for Assessing Water Quality","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccess to clean and safe water is a basic human right, as well as an important part of environmental sustainability. Assuring water quality requires sound monitoring and assessment to identify possible contaminants, pollutants, and general ecosystem health. While traditional approaches, such as physical and chemical analyses, give valuable insights into specific parameters, modern water quality assessment demands more integrated and subtle methodologies.\u003c/p\u003e\n\u003cp\u003eThe present study presents a critical review of major methodologies adopted in the assessment of water quality, such as physical, chemical, and biological techniques. Physical analysis entails parameters such as temperature, turbidity, and pH, which provide an insight into the physical characteristics of the water body. Chemical analysis entails the determination of the dissolved constituents in water, namely oxygen levels, conductivity, and nutrient levels. Biological analysis utilizes the presence and diversity of aquatic organisms to indicate the general view of ecosystem health.\u003c/p\u003e\n\u003cp\u003eNew sensor technologies, miniaturization of analytical equipment, and development of novel techniques like bioassays and DNA-based techniques are currently transforming water quality monitoring. Such approaches could lead to faster, more accurate, and integrated measurement that can make up for many deficiencies in the use of traditional approaches.\u003c/p\u003e\n\u003cp\u003eWith these developments, consensus has not been reached on the most effective and efficient methods of water quality evaluation. This study will, therefore, fill this gap by comparing the various techniques developed to show their strengths and weaknesses. Data from rivers, lakes, and groundwater analyzed in presenting applicability of each method to various monitoring objectives.\u003c/p\u003e\n\u003cp\u003eThe outcome of the present study would be of immense worth to the authorities of water management, policymakers, and researchers in the field of water resource management. This would help to explore the advantages and disadvantages of alternative methods for making decisions and building better schemes that develop efficient water quality monitoring programs. Moreover, the study contributes to the ongoing methods development on water quality assessment, with the intent of protection of human health and conservation of aquatic ecosystems.\u003c/p\u003e"},{"header":"2 Problem statement","content":"\u003cp\u003eIn a generation in which access to clean water is essential for both human fitness and environmental sustainability, the precise evaluation of water nice has never been more critical. Despite advancements in analytical techniques and generation, a fundamental venture persists: the absence of a universally recognized \u0026apos;gold preferred\u0026apos; for evaluating water quality. This loss of consensus poses massive demanding situations for researchers, water control authorities, and stakeholders.\u003c/p\u003e\n\u003cp\u003ePrime difficulty lies within the inherent variability of water fine evaluation methodologies. Techniques starting from conventional body and chemical analyses to progressive organic monitoring and faraway sensing frequently yield disparate consequences, complicating efforts to derive consistent and comprehensive reviews of water sources. This inconsistency can lead to misinterpretations, in the long run impeding powerful selection-making in water useful resource management.\u003c/p\u003e\n\u003cp\u003eMoreover, the complexities of those methodologies, coupled with restrained understanding of their respective strengths and boundaries, similarly prevent the selection of the maximum suitable method for precise tracking objectives. This information gap dangers inefficient useful resource allocation and inaccuracies in water best assessments, probably jeopardizing public health and environmental protection.\u003c/p\u003e\n\u003cp\u003eTo address those demanding situations, this study undertakes a rigorous comparative analysis of broadly hired water first-class assessment strategies. By systematically comparing their benefits, obstacles, and applicability to numerous water assets and tracking desires, this looks at pursuits to provide essential insights and realistic steering for knowledgeable selection-making in water great control.\u003c/p\u003e\n\u003cp\u003eThe findings of this study aspire to contribute to the improvement of extra standardized and reliable water high-quality assessment practices. By improving the precision and effectiveness of monitoring efforts, this analysis helps the broader purpose of making sure safe and sustainable water resources for gift and destiny generations.\u003c/p\u003e"},{"header":"3 Literature Review","content":"\u003cp\u003eS\u0026aacute;nchez-Quiles et al. (2018) conducted an intensive study in a Mediterranean coastal environment on water quality, considering traditional methods such as chemical and microscopic organism analysis in comparison with the latest advanced techniques of DNA-based methods. Indeed, the results proved to be so captivating; it reflected that such advanced approaches lead to higher precision in defining the real status of the aquatic environment. The present study points to the possibility that emerging technologies could serve as game-changers for a whole paradigm shift in the process of water quality assessment.\u003c/p\u003e\n\u003cp\u003eSharma et al. (2019) aimed to analyze the levels of pollution in one of the Indian rivers using a combination of a few classical approaches-such as simple chemical tests-along with advanced DNA-based techniques. Their findings have illustrated the unprecedented sensitivity of these novel techniques, which can reveal even trace levels of pollutants that may remain undetectable with traditional approaches. This work is illustrative of the power of integration of advanced scientific tools for the realization of precise and detailed knowledge about water quality.\u003c/p\u003e\n\u003cp\u003eChen et al. (2020): Chen et al. studied the water quality in a tropical watershed through a multi-proxy approach, ranging from chemical analyses to biological monitoring and real-time sensor technologies. The authors indicated that a multiple approach is necessary for integrated understanding of the quality of water. By integrating different methods, akin to consulting various specialists, the research showcased how each tool contributes unique insights. This study emphasizes the critical role of methodological diversity in achieving comprehensive water quality assessments.\u003c/p\u003e"},{"header":"4 Methodology","content":"\u003cp\u003eThis study will adopt a multi-faceted approach to evaluate and compare traditional and advanced water quality assessment methods. The research is structured into the following key stages:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Water Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWater samples will be collected from a wide range of sources, including rivers, lakes, and groundwater wells. Sampling shall be done at frequent intervals throughout the year to show seasonal changes and environmental impacts. This ensures a representative dataset reflecting the dynamic nature of water quality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Water Sample Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe collected samples will be analyzed by various conventional and modern techniques:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTraditional Methods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical Analysis:\u003c/strong\u003e Temperature, conductivity, and TDS measurement to evaluate some of the basic physical parameters. Chemical Analysis: Determination of pH, DO, turbidity, and nutrient levels will help in assessing the chemical composition and possible contamination of water. Biological Analysis: Assessment of indicator organisms and biodiversity indices provides information on ecological health and the impact of pollution. Advanced Methods:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA-Based Approaches\u003c/strong\u003e: qPCR and metagenomics for the identification and quantification of microbial communities provide complementary information on the potential pathogens and ecosystem functioning. Bioassays: Toxicity and bioaccumulation tests shall be performed to assess the effect of pollutants on aquatic organisms. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Data Analysis An integrative analytical approach will be performed to compare the performances of different assessment methods:\u003c/p\u003e\n\u003cp\u003et-tests and ANOVA are performed to point out significant differences between traditional and advanced methods. \u003cstrong\u003eMultivariate Analysis\u003c/strong\u003e: By principal component analysis or factor analysis, identify the key factors driving water quality variations and define the correlations among parameters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster Analysis\u003c/strong\u003e: Grouping of water samples by their quality characteristics to assist in the identification of pollution sources and areas of poor quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeographic Information Systems\u003c/strong\u003e: Spatial mapping of the water quality data will yield geographical patterns, trends, and hotspots of potential pollution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Machine Learning Applications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe machine learning techniques for enhancing predictive capabilities and finding complex patterns will be applied as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupervised Learning\u003c/strong\u003e: The regression analysis, decision trees, and Random Forest algorithms will be implemented on a set of input variables for predicting the water quality parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnsupervised Learning:\u003c/strong\u003e K-means clustering and hierarchical clustering will group the water samples based on similar characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeep Learning:\u003c/strong\u003e ANNs and CNNs will pick up complex trends in large datasets comprising sensor data and imagery. Natural Language Processing: Sentiment analysis and topic modeling for extracting insight from unstructured text data represented by public complaints, social media posts, and news articles related to water quality. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBJECTIVE AND CONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis will provide a proper and realistic comparison of various water quality assessment methodologies. Indeed, this research work, by comparing conventional and state-of-the-art techniques using advanced analytical tools, will underpin informed decision-making in water resources management and help in elaborating more efficient policies of environmental protection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWater Quality Indicators and Data Acquisition Procedures\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research work embraced a holistic set of water quality indicators backed by a strict procedure for data collection to enable strong comparability among the various methods of assessment. The adopted indicators range from physical to chemical and biological parameters, while sampling and purification methods ensure valid and accurate data.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWater Quality Indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemperature (\u0026deg;C):\u0026nbsp;\u003c/strong\u003eThermal condition and possible thermal pollution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTurbidity (NTU):\u0026nbsp;\u003c/strong\u003eReflects water clarity, which is modified by suspended particles and indicates sediment or pollutant loads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTotal Dissolved Solids (TDS) (mg/L):\u003c/strong\u003e The concentration of dissolved inorganic and organic substances in water, reflecting its mineral content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTotal Suspended Solids (TSS) (mg/L):\u0026nbsp;\u003c/strong\u003eThe amount of solid particles suspended in water affects turbidity and can impact aquatic ecosystems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChemical Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003epH:\u0026nbsp;\u003c/strong\u003eShows the acidity or alkalinity, very important for the suitability of water and for life in water.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDissolved Oxygen (DO) (mg/L):\u0026nbsp;\u003c/strong\u003eThe amount of oxygen available for aquatic respiration, thus being a major indicator of water quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConductivity (\u0026micro;S/cm):\u0026nbsp;\u003c/strong\u003eShows the ability of water to conduct electricity, which is affected by dissolved ions, salinity, or other pollutants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNitrate (mg/L):\u0026nbsp;\u003c/strong\u003eIndicates nitrogen levels, usually from agricultural runoff or wastewater contamination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChloride (mg/L):\u0026nbsp;\u003c/strong\u003eIndicates salinity, industrial waste, or road salt contamination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Collection Techniques\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following sampling techniques shall be used to ensure that the data collected is representative:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrab Sampling:\u0026nbsp;\u003c/strong\u003eThis is a single sample taken at a specific place and time with the intention of having a snapshot of the quality of the water. Samples will be collected in sterile containers and preserved according to standard protocols.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComposite Sampling:\u0026nbsp;\u003c/strong\u003eSeveral samples are taken over time at one location and combined to represent average water quality conditions over a period.\u003c/p\u003e\n\u003cp\u003eAutomated sampling shall be done through the use of automated equipment at fixed intervals to make monitoring continuous, and time-series data could be generated to show trends.\u003c/p\u003e\n\u003cp\u003eSelection of a sampling technique shall be informed by research objectives, water body characteristics, and available resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Cleaning Approaches\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following cleaning approaches will be used to make the dataset valid and consistent:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Validation:\u0026nbsp;\u003c/strong\u003eMissing values, outliers, and inconsistencies with the standard ranges will undergo strenuous checks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Standardization:\u0026nbsp;\u003c/strong\u003eThe measurements will be uniformly transformed into a unit of measure or normalized to compare metrics and scales.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Deduplication:\u003c/strong\u003eEntries found duplicate shall be removed in order to avoid bias.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Imputation:\u0026nbsp;\u003c/strong\u003eMissing values will be estimated by choosing proper statistical methods. These are selected based on the pattern of the data and potential effects on the results of the data analysis.\u003c/p\u003e\n\u003cp\u003eThis strong basis of metrics selection, data collection, and purification forms a very sound basis for the comparative analysis of the methods applied in water quality assessment. Integrating diversity into the parameters with rigorous data handling, this study aims at delivering actionable insights to advance the monitoring and management of water quality.\u003c/p\u003e"},{"header":"5 Data Set","content":"\u003cp\u003eThe trained dataset has around 2000 records and testing dataset has around 4000 records. Nine different parameters have been used to assess the potability aspect for a given sample.\u003c/p\u003e\n\u003cp\u003eAbove graphs show the distribution curve of various parameters. Data has been divided into maximum ten ranges so as to understand the spread.\u003c/p\u003e\n\u003cp\u003eAbove graphs show the impact of parameters on water potability.\u003c/p\u003e"},{"header":"6 Results and Discussion","content":"\u003cp\u003eThree different methods have been compared in this study, of which XGBoost Classifier with an accuracy of 78.54% gives a great performance.\u003c/p\u003e\n\u003cp\u003eTable 1 WQI Classifier Weightage\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"226\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5507%;\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.3392%;\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.1101%;\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5507%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.3392%;\"\u003e\n \u003cp\u003eSulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.1101%;\"\u003e\n \u003cp\u003e39.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5507%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.3392%;\"\u003e\n \u003cp\u003eSolids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.1101%;\"\u003e\n \u003cp\u003e33.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5507%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.3392%;\"\u003e\n \u003cp\u003eHardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.1101%;\"\u003e\n \u003cp\u003e7.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5507%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.3392%;\"\u003e\n \u003cp\u003eChloramines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.1101%;\"\u003e\n \u003cp\u003e5.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5507%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.3392%;\"\u003e\n \u003cp\u003eOrganic_carbon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.1101%;\"\u003e\n \u003cp\u003e3.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5507%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.3392%;\"\u003e\n \u003cp\u003eConductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.1101%;\"\u003e\n \u003cp\u003e3.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5507%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.3392%;\"\u003e\n \u003cp\u003eTrihalomethanes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.1101%;\"\u003e\n \u003cp\u003e3.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5507%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.3392%;\"\u003e\n \u003cp\u003eTurbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.1101%;\"\u003e\n \u003cp\u003e2.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDecision Tree\u003c/p\u003e\n\u003cp\u003eFigure 2 Shows how the algorithm works to decide the predicted outcome. Top 3 layers have been shown here to depict the tree model. This tree considers all parameters and works on calculating the potability rate depending on the WQI criteria as set by various regulatory agencies\u003c/p\u003e\n\u003cp\u003eConfusion Matrix\u003c/p\u003e\n\u003cp\u003eExplore false positives and false negatives with this chart.\u003c/p\u003e\n\u003cp\u003eThe confusion matrix shows that 457 rows were correctly predicted as potable, whereas 469 rows of data were correctly predicted as unpotable. Hence from the above confusion matrix we conclude that selected algorithm satisfies with the accuracy of 78.54% which is normally treated as moderately successful.\u003c/p\u003e\n\u003cp\u003eFigure 4 shows how the actual vs. predicted values stack up. And the accuracy rate is almost 74.57% which supports the model used in the research.\u003c/p\u003e\n\u003cp\u003eAnother model LightGBM Classifier with an accuracy of 63.25%. This is also a good performance.\u003c/p\u003e\n\u003cp\u003eTable 2 WQI Parameters Weightage\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"285\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eSolids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e54.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eSulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e13.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eHardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e10.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eTrihalomethanes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e7.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eOrganic_carbon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e5.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eConductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e4.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eChloramines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e2.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eTurbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e1.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Another model Random Forest Classifier with an accuracy of 61.42%. This is also a good performance.\u003c/p\u003e\n\u003cp\u003eTable 3 WQI Parameters Weightage\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"285\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eChloramines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e14.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eTrihalomethanes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e13.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eHardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e13.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eSulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e13.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eOrganic_carbon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e12.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eSolids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e10.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eConductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e10.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.1608%;\"\u003e\n \u003cp\u003eTurbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.4196%;\"\u003e\n \u003cp\u003e10.64%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;So out of the three models which were tested, XGBoost Classifier algorithm was found to be more accurate and reliable. This is a comprehensive list of model\u0026rsquo;s technical metrics using XGBoost Classifier algorithm.\u003c/p\u003e\n\u003cp\u003eTable 4 Summary of best performance\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71.2121%;\"\u003e\n \u003cp\u003eDriver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7879%;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71.2121%;\"\u003e\n \u003cp\u003eTraining Accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7879%;\"\u003e\n \u003cp\u003e80.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71.2121%;\"\u003e\n \u003cp\u003eValidation Accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7879%;\"\u003e\n \u003cp\u003e79.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71.2121%;\"\u003e\n \u003cp\u003eTesting Accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7879%;\"\u003e\n \u003cp\u003e78.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71.2121%;\"\u003e\n \u003cp\u003eF-Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7879%;\"\u003e\n \u003cp\u003e0.7854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71.2121%;\"\u003e\n \u003cp\u003ePrecision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7879%;\"\u003e\n \u003cp\u003e0.7872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71.2121%;\"\u003e\n \u003cp\u003eRecall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7879%;\"\u003e\n \u003cp\u003e0.7867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71.2121%;\"\u003e\n \u003cp\u003eArea Under Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7879%;\"\u003e\n \u003cp\u003e0.8702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71.2121%;\"\u003e\n \u003cp\u003eNumber of Iterations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7879%;\"\u003e\n \u003cp\u003e26046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71.2121%;\"\u003e\n \u003cp\u003eTesting / Training Split\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7879%;\"\u003e\n \u003cp\u003e80 / 20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThere were 26046 iterations done by the models and the data was split into 4:1 for testing and training data. Algorithm tested the dataset with various values and found that Highest likelihood for Potability to be 1 and Probability = 100.00%, the parameters will have the following values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 Average values of WQI parameters\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.0164%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDriver\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.9836%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.0164%;\"\u003e\n \u003cp\u003eChloramines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.9836%;\"\u003e\n \u003cp\u003e4.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.0164%;\"\u003e\n \u003cp\u003eConductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.9836%;\"\u003e\n \u003cp\u003e690.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.0164%;\"\u003e\n \u003cp\u003eHardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.9836%;\"\u003e\n \u003cp\u003e81.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.0164%;\"\u003e\n \u003cp\u003eOrganic_carbon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.9836%;\"\u003e\n \u003cp\u003e15.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.0164%;\"\u003e\n \u003cp\u003eSolids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.9836%;\"\u003e\n \u003cp\u003e51857.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.0164%;\"\u003e\n \u003cp\u003eSulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.9836%;\"\u003e\n \u003cp\u003e143.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.0164%;\"\u003e\n \u003cp\u003eTrihalomethanes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.9836%;\"\u003e\n \u003cp\u003e22.874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSo this research suggests that the XGBoost Classifier algorithm can be useful in predicting the water potability when working with the given set of parameters. Although various other factors may also be responsible for the same.\u003c/p\u003e"},{"header":"7 Critical Analysis","content":"\u003cp\u003eThe critical analysis of the study \u0026quot;Assessing Water Quality: A Comparative Analysis of Methods and Techniques\u0026quot; involves evaluation on aspects of strengths, shortcomings, implications, and areas that can potentially lead to further research.\u003c/p\u003e\n\u003cp\u003eThe paper presents the wide-ranging outlook of various methodologies and techniques that are in use for water quality assessment-both traditional ones involving physical, chemical, and biological analysis, and advanced methods such as DNA-based and bioassays. Thus, the approach given above regarding the multifunctional view to water quality can allow substantial evaluations of many advantages and/or shortcomings in all methods of water assessment. Meanwhile, adding machine learning analysis methodologies provides new insights that can potentially boost the outcomes in terms of efficiency and accuracy for any water quality analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdmittedly, such limitations exist regarding some weaknesses within this research. Using machine learning methods is mentioned as part of analysis; however, further specification related to concretely used algorithms and their parametric setting is absent. Also, while the procedures for data cleansing are briefly mentioned, they are not fully elaborated, which may compromise the reproducibility of the study and make its results less applicable by other researchers or practitioners. Further, a critical look at the water quality metrics used-things like their justification and limitations-could help to add weight and strength to the methodology.\u003c/p\u003e"},{"header":"8 Scope for future work","content":"\u003cp\u003eWhile these limitations have been identified, the current research nonetheless constitutes a valuable contribution to the field of water quality management and offers insights relevant to both researchers and practitioners. For that matter, the present comparative analysis constitutes a practical guide for the choice of techniques that best suit particular monitoring objectives and resource constraints. Besides, the integration of machine learning in this study presents potential for enhanced evaluation of water quality due to increased accuracy, efficiency, and predictive capability.\u003c/p\u003e\n\u003cp\u003eThese limitations would be overcome if the future research effort engaged in a more detailed description of machine learning algorithms and methods adopted for data purification. A deeper analysis, with respect to sensitivity, selectivity, and possible interferences for each of the water quality metrics used, will add value to the methodological strength of a similar study. Also, extending the scope of research to involve more types of water bodies and various conditions of the environment would provide broader generalizations of findings that are more applicable.\u003c/p\u003e"},{"header":"9 Conclusion","content":"\u003cp\u003eThis paper presents an overall assessment of water quality evaluation techniques, considering both conventional and advanced methods. The results demonstrate how method selection should meet the purpose for which the investigation is being carried out, along with those characteristics that differentiate one water body from another. Significantly, it has been indicated in this research that integrated approaches using different valuation methods are desirable to arrive at a more comprehensive realistic assessment of water quality.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study has shown the enormous potential of machine learning techniques in the analysis of water quality data and increasing the accuracy of estimates. It also emphasizes that machine learning should complement and not replace traditional methods to ensure a comprehensive and nuanced assessment. The research also underlines the important role that strict data cleansing procedures play in maintaining data quality and reliability. It also emphasizes effective data imputation techniques in respect to missing values as an indispensable part of a good data management system.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Declaration \u0026ndash;\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNo external funding was received for this research.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;Ethics, Consent to Participate, and Consent to Publish declarations:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJP worked on the literature review and analysis part, AB worked on the methodology testing and conclusion.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAPHA, AWWA, WEF (2012) Standard Methods for the Examination of Water and Wastewater, 22nd edition. 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Elsevier, Amsterdam.\u003c/li\u003e\n \u003cli\u003eUnited States Environmental Protection Agency (EPA). (2019). Water Analysis Interpretation. Retrieved from https://www.epa.gov/sites/production/files/2019-12/documents/water-analysis-interpretation.pdf\u003c/li\u003e\n \u003cli\u003eS. Verma, J. Pandey and A. Panthakkan, \u0026quot;Data Analytics and Knowledge Management in a learning environment for Higher Education,\u0026quot; 2022 5th International Conference on Signal Processing and Information Security (ICSPIS), Dubai, United Arab Emirates, 2022, pp. 61-64, doi: 10.1109/ICSPIS57063.2022.10002568.\u003c/li\u003e\n \u003cli\u003eUnited Nations Environment Programme (UNEP) (2020) Water Quality Monitoring and Assessment. Retrieved from https://www.unep.org/water-for-environment-and-development/water-quality-monitoring-and-assessment\u003c/li\u003e\n \u003cli\u003eWang, Y., Li, X., \u0026amp; Zhang, J. (2020). Advancements in water quality analysis: A review. Journal of Water and Health, 18(1), 1-11.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"WQI, Data Analytics, Potability, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-7453617/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7453617/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Assessing water high-quality is critical for making sure human fitness and environmental sustainability. This observe gives a complete comparative evaluation of commonly used techniques for water great assessment, together with bodily and chemical analyses, biological tracking, and remote sensing techniques. Each technique is severely tested to spotlight its strengths and limitations in shooting the complexity of water great parameters. Physical and chemical analyses offer precise measurements of parameters which include pH, dissolved oxygen, and nutrient ranges, but they will fail to deal with the dynamic interactions inside aquatic ecosystems. Biological tracking, which employs indicator species and network analyses, presents holistic insights into ecosystem health however may be time-extensive and might lack specificity in figuring out pollutants. Remote sensing permits efficient monitoring of huge water bodies, imparting spatial perspectives on water pleasant; however, it may be constrained in detecting positive parameters and is liable to atmospheric interferences. The observe similarly evaluates the forms of records produced by way of each method, that specialize in elements together with accuracy, price-effectiveness, and time performance. Drawing on statistics from numerous water sources, such as rivers, lakes, and wells, it assesses the applicability of every technique to special tracking objectives. Additionally, the studies explores recent improvements in water exceptional evaluation technology, such as biosensors, automated systems, and advanced statistics analytics, which promise real-time monitoring, improved accuracy, and more efficiency. This comparative analysis provides actionable insights for water control agencies, stakeholders, and researchers, helping the choice of suitable methods for particular water fine evaluation desires. By expertise the skills and barriers of each method, this paintings contributes to the enhancement of water nice assessment methodologies and supports the improvement of effective strategies for water useful resource management and environmental conservation.","manuscriptTitle":"Comparative Evaluation of Methods and Techniques for Assessing Water Quality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 14:08:53","doi":"10.21203/rs.3.rs-7453617/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dae2b153-8e7c-4fee-a8c2-cd83fb97e52f","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-23T10:23:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 14:08:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7453617","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7453617","identity":"rs-7453617","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}