Criteria Decision Research Articles

Feeling-BIM: A digital model to support maintenance decisions, based on automatic inspection and dwellers’ feelings

Maintenance decisions often extend beyond physical deterioration, with stakeholders' experiences playing crucial roles. Traditionally, building assessments rely on visual inspections, but manual methods have limitations (e.g., time and cost expensive, relying on subjective assessment). “Feeling-BIM” proposes a new methodology whose concept integrates automated facade inspection with residents' sentiments. Incorporating residents' feelings acknowledges the prevalence of subjective criteria in maintenance decisions, with a particular focus on facade stains that influence interventions and building aesthetics. Three buildings with the same archetype and in the same street, in different degradation conditions, are analysed as case study. A sentiment analysis is performed, to evaluate the ability of residents to clearly distinguish between facades in the best condition and those identified by automated inspection as having a higher presence of stains. “Feeling-BIM” empowers decision-makers with a more comprehensive perspective on facades’ condition, ensuring that decisions are well-informed and balanced, considering both objective and subjective factors.

Markov modeling for cost-effectiveness using federated health data network.

To introduce 2 R-packages that facilitate conducting health economics research on OMOP-based data networks, aiming to standardize and improve the reproducibility, transparency, and transferability of health economic models. We developed the software tools and demonstrated their utility by replicating a UK-based heart failure data analysis across 5 different international databases from Estonia, Spain, Serbia, and the United States. We examined treatment trajectories of 47 163 patients. The overall incremental cost-effectiveness ratio (ICER) for telemonitoring relative to standard of care was 57 472 €/QALY. Country-specific ICERs were 60 312 €/QALY in Estonia, 58 096 €/QALY in Spain, 40 372 €/QALY in Serbia, and 90 893 €/QALY in the US, which surpassed the established willingness-to-pay thresholds. Currently, the cost-effectiveness analysis lacks standard tools, is performed in ad-hoc manner, and relies heavily on published information that might not be specific for local circumstances. Published results often exhibit a narrow focus, central to a single site, and provide only partial decision criteria, limiting their generalizability and comprehensive utility. We created 2 R-packages to pioneer cost-effectiveness analysis in OMOP CDM data networks. The first manages state definitions and database interaction, while the second focuses on Markov model learning and profile synthesis. We demonstrated their utility in a multisite heart failure study, comparing telemonitoring and standard care, finding telemonitoring not cost-effective.

Application of bootstrap f2 to dissolution data from biorelevant media and evidence of the conservative nature of bootstrap f2

f2 with or without bootstrapping is the most common method to compare in vitro dissolution profiles, but methods to compare dissolution profiles have become less harmonized. The objective was to compare outcomes from bootstrap f2 and f2 (i.e. not-bootstrapped f2) using a large set of in vitro dissolution data. Non-parametric bootstrapping was performed on the 104 profile comparisons that did not meet the percent coefficient of variation (CV%) criteria to use average dissolution data. Bootstrap f2 was taken as the lower 90 % confidence interval of bootstrapped samples. There was concordance between bootstrap f2 and f2 in 92 of the 104 comparisons (88 %). There were no false positives. However, 12 % were false negative. Inspection of these discordance pairs suggests that bootstrap f2 serves as a conservative approach to assess profile similarity, particularly when a value of 50 is being used as decision criteria.

Using Interpretable Machine Learning for Differential Item Functioning Detection in Psychometric Tests.

This study presents a novel method to investigate test fairness and differential item functioning combining psychometrics and machine learning. Test unfairness manifests itself in systematic and demographically imbalanced influences of confounding constructs on residual variances in psychometric modeling. Our method aims to account for resulting complex relationships between response patterns and demographic attributes. Specifically, it measures the importance of individual test items, and latent ability scores in comparison to a random baseline variable when predicting demographic characteristics. We conducted a simulation study to examine the functionality of our method under various conditions such as linear and complex impact, unfairness and varying number of factors, unfair items, and varying test length. We found that our method detects unfair items as reliably as Mantel-Haenszel statistics or logistic regression analyses but generalizes to multidimensional scales in a straight forward manner. To apply the method, we used random forests to predict migration backgrounds from ability scores and single items of an elementary school reading comprehension test. One item was found to be unfair according to all proposed decision criteria. Further analysis of the item's content provided plausible explanations for this finding. Analysis code is available at: https://osf.io/s57rw/?view_only=47a3564028d64758982730c6d9c6c547.

The Use of Evidence to Design an Essential Package of Health Services in Pakistan: A Review and Analysis of Prioritisation Decisions at Different Stages of the Appraisal Process.

Pakistan embarked on a process of designing an essential package of health services (EPHS) as a pathway towards universal health coverage (UHC). The EPHS design followed an evidence-informed deliberative process; evidence on 170 interventions was introduced along multiple stages of appraisal engaging different stakeholders tasked with prioritising interventions for inclusion. We report on the composition of the package at different stages, analyse trends of prioritised and deprioritised interventions and reflect on the trade-offs made. Quantitative evidence on cost-effectiveness, budget impact, and avoidable burden of disease was presented to stakeholders in stages. We recorded which interventions were prioritised and deprioritised at each stage and carried out three analyses: (1) a review of total number of interventions prioritised at each stage, along with associated costs per capita and disability-adjusted life years (DALYs) averted, to understand changes in affordability and efficiency in the package, (2) an analysis of interventions broken down by decision criteria and intervention characteristics to analyse prioritisation trends across different stages, and (3) a description of the trajectory of interventions broken down by current coverage and cost-effectiveness. Value for money generally increased throughout the process, although not uniformly. Stakeholders largely prioritised interventions with low budget impact and those preventing a high burden of disease. Highly cost-effective interventions were also prioritised, but less consistently throughout the stages of the process. Interventions with high current coverage were overwhelmingly prioritised for inclusion. Evidence-informed deliberative processes can produce actionable and affordable health benefit packages. While cost-effective interventions are generally preferred, other factors play a role and limit efficiency.

Impact and utility of follicular lymphoma GELF criteria in routine care: an Australasian Lymphoma Alliance study.

Follicular lymphoma (FL) treatment initiation is largely determined by tumor burden and symptoms. In the pre-rituximab era, the Group d'Etude des Lymphomes Folliculaires (GELF) developed widely adopted criteria to identify high tumor burden FL patients to harmonize clinical trial populations. The utilization of GELF criteria (GELFc) in routine therapeutic decision- making is poorly described. This multicenter retrospective study evaluated patterns of GELFc at presentation and GELFc utilization in therapeutic decision-making in newly diagnosed, advanced-stage rituximab-era FL. Associations between GELFc, treatment given, and patient survival were analyzed in 300 eligible cases identified between 2002-2019. One hundred and sixty-three (54%) had ≥1 GELFc at diagnosis. The presence or cumulative number of GELFc did not predict progression-free survival in patients undergoing watch-and-wait (W&W) or those receiving systemic treatment. Of interest, in patients with ≥1 GELFc, 16 of 163 (10%) underwent initial W&W (comprising 22% of the W&W cohort). In those receiving systemic therapy +/- radiotherapy, 74 of 215 (34%) met no GELFc. Our data suggest clinicians are using adjunctive measures to make decisions regarding treatment initiation in a significant proportion of patients. By restricting FL clinical trial eligibility only to those meeting GELFc, reported outcomes may not be applicable to a significant proportion of patients treated in routine care settings.

Rush to Charge, Dead to Drive: Application of Deadline Rush Model to Electric Vehicle User's Charging Behavior.

This work aims to estimate the portion of electric vehicle (EV) users who exhibit procrastination-like behavior, almost equivalent to an "empty" battery, before they decide to charge their vehicles. There is a human tendency to procrastinate when a deadline approaches. Human behavior in the presence of deadlines has been studied in different fields to evaluate individuals' performance or organizational efficiency and effectiveness. However, this phenomenon has not been investigated among EV users. This study explores users' procrastination-like behavior among 69 Rhode Island public charging stations' data representing 70,611 charging events. The Deadline Rush Model is incorporated to model frequent users' charging profiles. To conduct a robust estimation, the Bayesian Mixture Model is implemented. With the selection of an informative prior, the Bayesian Mixture Model estimated that almost one-third of frequent users procrastinate charging. The majority of procrastination-like users have small battery sizes. Although procrastination-like users need to charge when they arrive at a location, that might not necessarily be true for a plug-in hybrid; thus, systematically, they can clog the system for other users whose needs are more pressing. Understanding unique and unexplored charging behaviors among EV users is beneficial to EV infrastructure stakeholders in reducing the adoption threshold by providing a reliable and ubiquitous charging network. The findings identify a different kind of demand on the EV infrastructure than previously modeled and can directly influence future decision-making criteria in terms of planning to optimize to accommodate EV drivers with different charging behaviors.

An Agent-Based Modeling Approach for the Diffusion Analysis of Electric Vehicles With Two-Stage Purchase Choice Modeling

Abstract Diffusion research of innovative technologies is crucial for new product positioning and strategic planning in product design. As a versatile system simulation method, agent-based modeling (ABM) has been used in many previous studies on the diffusion analysis of electric vehicles (EVs). In these simulations, modeling consumers' purchase decisions is a significant step. Previous studies often adopt simple rule-based decision criteria in this step, while an accurate purchase decision model can contribute to a more reasonable diffusion analysis of EVs. To fill this gap, this brief presents an agent-based modeling approach for the diffusion analysis of electric vehicles with two-stage choice modeling. The core idea is to separate consumers' decision-making process for purchasing cars into two stages. Consumers first form a small choice set from the whole auto market. Then, they make the final choice from the choice set built in the first stage. In addition, the word-of-mouth (WOM) effect and consumers' social networks are also considered in the ABM, which can further improve the accuracy of the diffusion analysis. A case study using data collected from Shanghai, China, is presented to demonstrate the proposed approach. Our approach outperforms other ablation models as well as traditional statistical models in the prediction accuracy of EV's market share. The influence of factors such as government policy and technological improvement on the diffusion of EVs is also discussed. These insights can assist automakers in improving their product design and enhancing their market competitiveness.

Aspecte teoretice și practice ale aplicării criteriilor de decizie în jocurile contra naturii

This article succinctly outlines certain theoretical aspects concerning decision criteria and their practical application within the realm of game theory. The aim of this article is to enhance and reinforce practical aptitudes in making and justifying decisions in situations characterized by limited information, where one player lacks a clearly defined objective and selects subsequent moves randomly.

Influence of reinforcement and its omission on trial-by-trial changes of response bias in perceptual decision making.

Discrimination performance in perceptual choice tasks is known to reflect both sensory discriminability and nonsensory response bias. In the framework of signal detection theory, these aspects of discrimination performance are quantified through separate measures, sensitivity (d') for sensory discriminability and decision criterion (c) for response bias. However, it is unknown how response bias (i.e., criterion) changes at the single-trial level as a consequence of reinforcement history. We subjected rats to a two-stimulus two-response conditional discrimination task with auditory stimuli and induced response bias through unequal reinforcement probabilities for the two responses. We compared three signal-detection-theory-based criterion learning models with respect to their ability to fit experimentally observed fluctuations of response bias on a trial-by-trial level. These models shift the criterion by a fixed step (1) after each reinforced response or (2) after each nonreinforced response or (3) after both. We find that all three models fail to capture essential aspects of the data. Prompted by the observation that steady-state criterion values conformed well to a behavioral model of signal detection based on the generalized matching law, we constructed a trial-based version of this model and find that it provides a superior account of response bias fluctuations under changing reinforcement contingencies.

Chronic rhinosinusitis with nasal polyposis and biological agents: the ARIA-ITALY Survey.

Background. Chronic rhinosinusitis (CRS) is an inflammatory disease that affects the nasal mucosa and the paranasal sinuses. CRS can be associated by nasal polyposis (CRSwNP phenotype) in up to 30% of patients and it is frequently associated with bronchial asthma. CRSwNP shows predominantly an underlying activation of type 2 inflammatory pathways with the involvement of eosinophils, IgE, interleukin (IL)-4, IL-5 and IL-13. Biological drugs that target these inflammatory cytokines are currently a therapeutic option recognized by guidelines for the treatment of uncontrolled form of the disease. Methods. As part of the activity of the "ARIA-Italy" working group, a panel of 255 Italian Ear, Nose and Throat (ENT) specialists, pneumologists and immuno-allergologists actively participated in this national survey and answered a series of questions geared toward understanding the main criteria for patient characterization and therapeutic decision, highlighting multidisciplinarity, and the implementation of the management of CRSwNP patients, as a part of the precision medicine concept and the appropriate use of the biologicals. Results. Two hundred and fifty-five experts and specialists participated in the survey. Conclusions. The results of this survey obtained from an extensive number of active specialists throughout Italy allow some important concluding remarks to be drawn. The main points of agreement were that multidisciplinary care teams provide many benefits but that, once the team is established, meetings and communication between members must be coordinated. Finally, the dissemination of national disease registries and the continuous updating of guidelines and position papers related to CRSwNP and comorbidities should be encouraged.

Multidisciplinary Decision-Making-ITAlian Consensus After Two Years of Real Practice on the Management of Severe Uncontrolled CRSwNP by Biologics (ITACA Study).

We aimed to reach an Italian multidisciplinary consensus on some crucial aspects of treatment decision making in CRSwNP, following 2years of clinical experience in order to support specialists in the management of CRSwNP in clinical practice. We addressed issues relating to therapeutic decision-making and shared criteria for the treatment choice, as well as appropriate timing and criteria for evaluating treatment response, and highlighted the need for repeated multidisciplinary assessments. A national survey has been conducted recently to understand how rhinology practice has changed in Italy with the advent of biologics and how this affects patients with uncontrolled, severe CRSwNP. Despite the many published consensus documents, practical recommendations, and protocols on the use of biologics in CRSwNP, heterogenous behaviors in practice are still observed mainly conditioned by the novelty of the topic. The consensus procedure followed a modified Delphi approach. The scientific board included 18 otorhinolaryngologists and 8 allergists, who selected the 4 main topics to be addressed and developed overall 20 statements. Consensus on these statements was sought by a larger group of 48 additional experts, through two rounds of voting, the first web-based, the second in presence with discussion and possible refinement of the statements. The statements reaching an average score ≥ 7 at the second voting round were approved. Five statements were proposed for each of the following topics: baseline evaluation of patients eligible for biologic therapy; choice between different therapeutic options; assessment of the response to biologic treatment; multidisciplinary management. At the first voting round, 19 out of the 20 statements reached a mean score ≥ 7. Following the discussion and a few consequent amendments, at the second round of voting all the 20 statements were approved.

Data-driven decision making: Informed healthcare supply chain management

With the evolution and incorporation of artificial intelligence (AI) and machine learning (ML) into so many business models and enterprises, data-driven decision making is assuming an ascendant role among organisational operations. While this work is not intended to negate experiential or intuitive decision making, leaders are encouraged to examine operational decision making through a lens of how experience can be supported by organisationally derived information yielding more effective operational decisions. While experiential learning is beneficial in determining how organisational decisions are being made, data presents why such decisions are made in the first place. More important still, what impact might decisions have on healthcare operations if grounded in ill-informed, ill-defined decision-making processes? Especially within healthcare supply chain operations, decision making should be a collaborative, multidiscipline, multistakeholder process that is best grounded in data-driven approaches. Healthcare supply chain data and analytics can and do provide a valuable framework within which operational decisions should be viewed. Potentially gleaned from this paper will be a perspective relatable to evidence-based analyses permitting the evolution of better-informed leadership decision criteria contributing to tangible organisational resilience, agility and flexibility.

부동산 경매 시 유치권의 대항력 제한 법리에 대한 검토

The retention right holder has the right to refuse delivery to the buyer in the auction procedure. As part of resolving the problem of this situation, the Supreme Court has established the legal principle that liens established after the registration of the auction commencement decision cannot be used against the buyer in the auction process. However, in 2022, the Supreme Court made a ruling that is difficult to explain with these legal principles. This is because the opposing power of the lien was recognized even though the lien was not established at the time of registration of the auction commencement decision due to the postponement of the payment period. However, the 2022 Supreme Court ruling does not provide specific standards, but simply lists various circumstances that do not undermine the stability of the auction procedure even if the lien is recognized in the case. Therefore, it is not possible to know which of them is the decisive factor. In other words, this ruling is significant in that it demonstrates the need for change in the existing Supreme Court jurisprudence, but it has the limitation of failing to present specific standards to resolve this issue. In this paper, the following conclusions were presented regarding the standards for judging the opposing power of liens by comparing previous rulings with the 2022 ruling.
 (1) Limiting the opposing power of liens established by transfer of possession after registration of the auction commencement decision is an appropriate way to protect the interests of enforcing creditor. And this can be explained by the prohibition effect of attachment.
 (2) Even if a claim is established after registration of the auction commencement decision, it is necessary to deny the opposing power of the lien. This can be explained if the effect of attachment is broadly understood.
 (3) The fact that the payment period arrives after the registration of the auction commencement decision cannot be a factor to be considered when determining the adversarial power of the lien.
 (4) Whether or not a lien is listed in the status investigation report cannot be a decisive criterion for determining opposing power. However, in cases where it is necessary to protect the trust of buyers in the auction process who were unaware of the existence of a lien, opposing power must be determined by reviewing the lien holder's fault for the failure to include it in the status investigation report.

Evaluation of Salesperson Performance in the Sales Allowance Decision Support System Using the MARCOS and PIPRECIA Methods

Optimal salesperson performance is the main key to a company's success in achieving sales targets and business growth. A reliable salesperson is not only able to sell products or services, but also has the ability to build strong relationships with customers. The purpose of this study is to assess the performance of salesperson in providing sales allowances based on performance results carried out by applying a combination of MARCOS and PIPRECIA methods, so as to produce a recommendation for the final assessment of salesperson performance that will assist the company in providing sales benefits to salespersons. The combination of Pairwise Relative Criteria Importance Assessment (PIPRECIA) and Measurement of Alternatives and Ranking According to Compromise Solution (MARCOS) forms a powerful holistic approach to decision making. PRCIA facilitates the identification and assessment of the relative weights of each decision criterion, providing a solid foundation for assigning value to the relative importance between criteria. The results of the salesperson performance evaluation ranking above show the final results for rank 1 with a value of 4.3446 obtained by Rini, rank 2 with a value of 3.5369 obtained by Murniasih, rank 3 with a value of 3.1807 obtained by Hana Ferbi.

Adopting immersive technologies in construction training: determining educational decision-making criteria through a Delphi technique

PurposeIn the realm of construction education, the investigation of immersive learning and extended reality (XR) technologies has experienced a surge in recent times. Nevertheless, there remains a notable lack of comprehension surrounding the most efficient ways to integrate these technologies into tailored teaching approaches for vocational construction training. This research study aims to pinpoint the key factors that construction vocational education and training (VET) providers must consider when introducing XR technologies into their training schemes.Design/methodology/approachThis study conducted an in-depth literature review to develop an initial framework to summarise training, technology and institutional factors influencing the educational-technology integration of XR technologies in VET. In addition, this study utilised a Delphi technique, including semi-structured group discussions and two rounds of online follow-up questionnaires, to capture VET experts’ judgements on the importance of decision-making criteria.FindingsThis study has identified the critical factors to be considered in the VET sector when adopting XR technologies. Findings revealed institutional factors were the most important criteria for participants, followed by training and technology factors.Research limitations/implicationsThe current decision-making process focuses on selecting XR technologies rather than evaluating their performance after implementation. Therefore, more research is needed to monitor the implementation of this technology in curricula from a senior management perspective. This will help to understand the cost and value factors related to the value proposition of XR technologies in courses.Practical implicationsTo ensure the success and long-term viability of the technology-curriculum interface, it is important to consider factors such as the availability of technical and educational support, data security and cost-effectiveness. It is also crucial to focus on ease of use and content development that emphasises instruction to create engaging content for learners.Originality/valueThe potential impact of this study is underpinned by two facts: (1) it constitutes the first effort made in the field to comprehensively elicit VET expert judgements in relation to XR technologies, and (2) it offers decision-making criteria that are at play in seeking to take advantage of high-cost technologies that are rapidly evolving. While there is no simple checklist for XR implementation, this study takes a step further to identify significant factors influencing XR integration in vocational construction training.

MADM-based network selection and handover management in heterogeneous network: A comprehensive comparative analysis

As radio access technologies, processing speeds, and multimode interfaces of low-powered portable devices continue to advance, the future of wireless communication is envisioned to offer pervasive network coverage, high data rates, and a wide spectrum of services while maintaining high mobility. High data rates, wide range of services, huge connectivity, capacity, and good geographic coverage are being provided by the ultra-dense deployment of small base stations (BSs) in heterogeneous wireless networks (HWN). But dense deployment of small BSs, high mobility, network heterogeneity, imbalanced traffic, and dynamic user preferences lead to frequent handover. Network overhead, excessive energy consumption, and a decrease in service quality and user satisfaction can be due to frequent handover. So, handover management is one of the crucial challenges in the implementation of 5G and beyond in HWNs for ensuring seamless connectivity, energy efficiency, and the required quality of services and experiences. The effectiveness of handover decisions in HWNs relies on the implementation of a suitable network selection mechanism. Multi-attribute decision-making (MADM) is being used to model and analyze appropriate network selection complexities by considering a broad spectrum of intricate and conflicting decision criteria for efficient handover decisions in HWN. This article extensively explores, compares, and analyzes vital MADM techniques utilized for modeling appropriate network selection strategies in terms of algorithmic strategies, cardinality, types and significance of decision attributes, and network utilities. This article also examines, analyzes, and recognizes the recent mobility management challenges and trends in utilizing MADM strategies to tackle network selection issues in high-speed HWNs.

An adaptive metaheuristic optimization approach for Tennessee Eastman process for an industrial fault tolerant control system.

The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for assessing the effectiveness of fault detection and false alarm tracking methods in intricate industrial operations. This paper presents a novel methodology that employs the Adaptive Crow Search Algorithm (ACSA) to improve fault identification capabilities and mitigate the occurrence of false alarms in the TEP. The ACSA is an optimization approach that draws inspiration from the observed behavior of crows in their natural environment. This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. The primary objective of our research is to devise a monitoring strategy that is adaptable in nature, with the aim of efficiently identifying faults within the TEP while simultaneously minimizing the occurrence of false alarms. The ACSA is applied in order to enhance the optimization of monitoring variables, alarm thresholds, and decision criteria selection and configuration. When compared to traditional static approaches, the ACSA-based monitoring strategy is better at finding faults and reducing false alarms because it adapts well to changes in process dynamics and disturbances. In order to assess the efficacy of our suggested methodology, we have conducted comprehensive simulations on the TEP dataset. The findings suggest that the monitoring strategy based on ACSA demonstrates superior fault identification rates while concurrently mitigating the frequency of false alarms. In addition, the flexibility of ACSA allows it to efficiently manage process variations, disturbances, and uncertainties, thereby enhancing its robustness and reliability in practical scenarios. To validate the effectiveness of our proposed approach, extensive simulations were conducted on the TEP dataset. The results indicate that the ACSA-based monitoring strategy achieves higher fault detection rates while simultaneously reducing the occurrence of false alarms. Moreover, the adaptability of ACSA enables it to effectively handle process variations, disturbances, and uncertainties, making it robust and reliable for real-world applications. The contributions of this research extend beyond the TEP, as the adaptive monitoring strategy utilizing ACSA can be applied to other complex industrial processes. The findings of this study provide valuable insights into the development of advanced fault detection and false alarm monitoring techniques, offering significant benefits in terms of process safety, reliability, and operational efficiency.

Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives

This article explores the growing importance of the QoE (Quality of Experience) metric as a fundamental criterion in the optimization of heterogeneous multimedia networks. We explore the benefits of using QoE, such as improving user experience, efficient resource management, and adaptation to network conditions. However, this paradigm presents technical challenges, including accurately measuring QoE and managing the complexity of heterogeneous networks. To address these challenges, we highlight promising research prospects, such as the development of advanced algorithms, real-time measurement of QoE, and the integration of machine learning. Ultimately, the use of QoE in the optimization of heterogeneous multimedia networks contributes to a substantial improvement in the quality of multimedia services offered to end users.

Dharumapuri District, Tamil Nadu, Groundwater Quality Status in relation to WASPAS System Pollution

Groundwater Quality. Tamil Nadu, Dharmapuri District, Domestic and Water quality for irrigation purposes to assess water quality survey has been carried out. PH, TDS, TH, Calcium, Magnesium, Chloride, Sulphate. This paper notes that increasing levels of water pollution, the resulting billion-dollar utility and with control schemes, it provides a way to measure and evaluate the quality of given water body Development of water quality codes is necessary. The data output of current water monitoring stations is huge and Dimensional reporting units are different and Not integrated in a straightforward algebraic way, even by scientifically trained users have few means of integrating the data to provide; water quality. That quality is locally better than hook and line to be broadly defined, Because of the importance of downstream streams less emphasized in that context. Ground water by comparing the measured concentration of ions and other parameters with the suggested limits provided by the Bureau of Indian Standards, the acceptability of the groundwater in this area as drinking water was determined lists the quantity and percentage of samples that exceeded the drinking quality criteria suggested by. The quantities of PH, TDS, TH, calcium, chloride, sulphate, and nitrate as well as total hardness were consistently within suggested levels in the 652 groundwater samples that were collected and analyzed water quality for drinking. MCDM difficulties include many incompatible and conflicting criteria, different measurement units in the criteria, and the existence of completely different alternatives. These decision problems describing multidimensional situations are solved by various MCDM methods. In the WASPAS method, a composite criterion is optimally two based on the criteria sought. The first criterion of optimality via a weighted average success criterion is the WSM method, which is based on the decision criteria used to evaluate the accepted MCDM approach alternatives, such as the popular one. Weighted Aggregate Product Assessment (WASPAS) this method is an effective MCDM tool when solving eight production decision problems, namely grinding level, material depletion. From the result it is seen that Karimangalam Taluk is showing the highest value for Pennagaram Taluk is showing the lowest value. Resulting in Karimangalam Taluk ranked first; there Pennagaram Taluk has low rank.