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Nowadays, to achieve sustainable development and avoid devastating impacts on the environment, India is making rapid and broad changes to green energy technology. In order to provide long-term energy security with lower emissions, renewable energy sources are essential. It is well known that renewable energy technologies, or RETs, have the capacity to significantly meet the demand for electricity while lowering pollution levels. The nation has set up an ecologically friendly energy route in recent years. In this paper, we apply multi-criteria decision making (MCDM) models to determine the best renewable energy technology for India. We use the MULTIMOORA model to identify the best technology under the pythagorean neutrosophic fuzzy set (PNFS) and the FUCOM to obtain the criteria weights. In this study, we investigate sources of renewable energy using the newly developed idea of the PNFS and recommend the best renewable energy source for India. [ABSTRACT FROM AUTHOR]

The article aims at multi-criteria decision making analysis using the fuzzy set theory to evaluate different policy options to reduce CO2 emissions from road transport in case of India. The fuzzy set theory has been applied to investigate different possible measures which the government can adopt to reduce CO2 emissions and improve air quality. Ordered weighted average operator of linguistic fuzzy set theory is used to rank different policy options. Findings show that the use of low emissions vehicles and the adoption of sustainability-oriented behaviour are considered the most preferred options, which can effectively reduce the CO2 emissions from road transport. The research shows that fuzzy set theory may be an effective tool in analyzing environmental uncertainties. Analyzing the optimal policy measure and prospects of it in different scenarios would help policymakers in designing and focusing on effective policy measures in order to reduce CO2 emissions from road transport in a country like India. The study implies ways to assess policy options for environment management and dealing with climate change. This study also fills the gap between the theoretical fuzzy model and its practical implication in policy decision-making, especially in a sustainability area that is more uncertain. [ABSTRACT FROM AUTHOR]

The development opportunities and high-performance capacity of offshore wind energy project depends on the selection of the suitable offshore wind power station (OWPS) location. The present study aims to introduce a decision-making model for assessing the locations for OWPS from multiple criteria and uncertainty perspectives. In this regard, the concept of interval-valued intuitionistic fuzzy set (IVIFS) is utilized to express uncertain information. To quantify the degree of difference between IVIFSs, an improved distance measure is proposed and further utilized for deriving the objective weights of criteria. Numerical examples are discussed to illustrate the usefulness of introduced IVIF-distance measure. The RANking COMparison (RANCOM) based on interval-valued intuitionistic fuzzy information is presented to determine the subjective weights of criteria. With the combination of objective and subjective weights of criteria, an integrated weighting tool is presented to find the numeric weights of criteria under IVIFS environment. Further, a hybrid interval-valued intuitionistic fuzzy Weighted integrated Sum Product (WISP) approach is developed to prioritize the OWPS locations from multiple criteria and uncertainty perspectives. This approach combines the benefits of two normalization tools and four utility measures, which approves the effect of beneficial and non-beneficial criteria by means of weighted sum and weighted product measures. Further, the developed approach is applied to the OWPS location selection problem of Gujarat, India. Sensitivity and comparative analyses are presented to confirm the robustness and stability of the present WISP approach. This study provides an innovative decision analysis framework, which makes a significant contribution to the OWPS locations assessment problem under uncertain environment. [ABSTRACT FROM AUTHOR]

In order to delineate the randomness and imprecision in a single framework of time-sequential information, we proposed the notion of a time-sequential probabilistic fermatean hesitant set (TS-PFHS). Triangular time-sequential probabilistic fermatean hesitant number (Tr-TS-PFHN) is also initiated as a triangular version of TS-PFHS along with its fundamental operations and a ranking function. For the employment of our proposed set, we formulated a solid transportation problem (STP) with multiple objectives with TS-PFHS parameters for a sustainable green environment. Consequently, we proposed an algorithm by utilizing fuzzy programming (FP) and weighted sum technique (WST) to address proposed green transportation model. Additionally, to highlight the significance of the suggested technique, a numerical instance based on the transportation of electric vehicles in India is also investigated. Finally, conclusion along with the future prospects and limitations of the work done are discussed. [ABSTRACT FROM AUTHOR]

Municipal solid waste management (MSWM) has emerged as a major issue in India due to the massive amounts of waste generated on a daily basis. Governments are focusing on establishing a proper waste management system, including a timeline for the installation of waste processing and disposal facilities to reduce waste. For this problem, waste to energy (WtE) technologies have been identified because of their ability to convert waste into green energy while minimizing associated issues. This research investigates the different kinds of WtE treatments based on various factors, namely eco-friendly, budget friendly, technical, and social aspects. The findings of this study revealed which WtE treatments are best suited for waste management systems that convert green energy. In this paper, we propose the best WtE treatment for India using the interval-valued pythagorean neutrosophic fuzzy set (PNFS). We employ the WASPAS-CRITIC method in the interval-valued pythagorean neutrosophic fuzzy environment for this WtE treatment problem. Finally, a numerical example and a comparison are provided to illustrate the reliability and efficiency of the proposed technique. [ABSTRACT FROM AUTHOR]

A country that relies on developing industrialization and GDP requires a lot of energy. Biomass is emerging as one of the possible renewable energy resources that may be used to generate energy. Through the proper channels, such as chemical, biochemical, and thermochemical processes, it can be turned into electricity. In the context of India, the potential sources of biomass can be broken down into agricultural waste, tanning waste, sewage, vegetable waste, food, meat waste, and liquor waste. Each form of biomass energy so extracted has advantages and downsides, so determining which one is best is crucial to reaping the most benefits. The selection of biomass conversion methods is especially significant since it requires a careful study of multiple factors, which can be aided by fuzzy multi-criteria decision-making (MCDM) models. This paper proposes the normal wiggly interval-valued hesitant fuzzy-based decision-making trial and evaluation laboratory model (DEMATEL) and the Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE) for assessing the problem of determining a workable biomass production technique. The proposed framework is used to assess the production processes under consideration based on parameters such as fuel cost, technical cost, environmental safety, and C O 2 emission levels. Bioethanol has been developed as a viable industrial option due to its low carbon footprint and environmental viability. Furthermore, the superiority of the suggested model is demonstrated by comparing the results to other current methodologies. According to comparative study, the suggested framework might be developed to handle complex scenarios with many variables. [ABSTRACT FROM AUTHOR]

Condition assessment is a crucial step in maintaining and managing existing concrete bridges. Most of the studies consider defects and their severity levels in the evaluation process in the literature. As of now, underlying causes, which play an important role in magnifying existing defects, do not get much attention in the assessment process as per the literature. Thus, this study proposes an integrated Analytic Hierarchy Process (AHP) and fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (fuzzy-TOPSIS) approach to rank defects considering underlying causes. Here, causes and sub-causes are considered as criteria and sub-criteria, respectively. AHP is used to determine relative importance weights of causes and sub-causes. The final defects ranking is obtained using fuzzy-TOSIS. The applicability of the model is demonstrated through a case study on a reinforced concrete bridge deck in Gujarat, India, where 'cracks' is obtained as the most critical defect for this specific case. The robustness of the model is examined by a sensitivity analysis. The proposed approach can help bridge owners to enhance the existing decision-making process of concrete bridges. Moreover, this approach can be considered as a first step into a new way of ranking defects of a more comprehensive system. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a non-parametric approach for the estimation of production frontier that is used to calculate the performance of a group of similar decision-making units (DMUs) which employ comparable inputs to produce related outputs. However, observed values might occasionally be confusing, imprecise, ambiguous, inadequate, and inconsistent in real-world applications. Thus, disregarding these factors may result in incorrect decision-making. Thus neutrosophic sets have been created as an extension of intuitionistic fuzzy sets to represent ambiguous, erroneous, missing, and inaccurate information in real-world applications. In this study, we have proposed a technique for solving the neutrosophic form of the Charnes-Cooper-Rhodes (CCR) model based on single-value trapezoidal neutrosophic numbers (SVTrNNs). The possibilistic mean for SVTrNNs is redefined and applied the Mehar approach to transforming the neutrosophic DEA (Neu-DEA) model into its corresponding crisp DEA model. As a result, the efficiency scores of the DMUs are calculated using different risk parameter values lying in [0, 1]. A numerical example is given to analyze the performance of the all India institutes of medical sciences and compared it with Abdelfattah's ranking approach. [ABSTRACT FROM AUTHOR]

Green supplier management (GSM) gained significant importance in addressing environmental concerns, promoting resource efficiency, and enhancing eco-efficiency within the green supply chain system. This study presents a systematic review to provide insights into the current research status and prospects in GSM literature. Results indicate that the research about GSM is gaining consistently growing attention over the past decades. However, there exists a regional imbalance in academic research, with a substantial portion of the authors originating from developing countries in China and India. The topics of green supplier selection and evaluation have received considerable attention in academia. In addition, the multi-attribute decision-making methods, such as TOPSIS, VIKOR, and AHP, and some mathematical modeling approaches have played a crucial role in the methodology employed for GSM research. As a fundamental algorithm in the artificial intelligence area, fuzzy sets theory has also been extensively employed in supplier selection and evaluation studies, whereas other big data analysis approaches have received little attention. Considering the inherent risks and uncertainties in the business strategy environment and developing more big data and artificial intelligence techniques represent promising avenues for future research in the field. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) development has increased on a global scale, posing significant socio-economic and environmental challenges. Waste to energy conversion is strategic because it can determine the best waste-to-energy technology. Recognizing the significance of waste in energy selection, this research aims to select the best technology based on its eco-friendliness. A two-phase methodology is used for presenting a framework for waste to energy technology selection. The first phase involves the selection of criteria for this problem and the perspectives of decision makers. Ranking the selected criteria using a novel SWARA method, and ranking the technology of WtE with respect to selected criteria weights are obtained. In this paper, we investigate the WtE technology problem using the novel concept of the Pythagorean Neutrosophic fuzzy set (PNFS). This set is a hybrid of the Pythagorean and Neutrosophic fuzzy sets. In this paper, we propose the best WtE technology for India. We employ the MULTIMOORA method in the Pythagorean Neutrosophic fuzzy environment for this purpose. Sensitivity and comparison analyses are also performed to ensure the framework's robustness. The findings of this study are useful in ranking the technology, and as a result, waste management can replicate the proposed framework for waste disposal for their new platform. [ABSTRACT FROM AUTHOR]

Three objectives, maximization of resilience, minimization of cost, and minimization of leakages, were considered in the Water Distribution Network (WDN) framework for a benchmark problem of Hanoi WDN and a real-world problem, Pamapur WDN, Telangana, India. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is employed to generate Non-dominated WDN Strategies (NWDNS). In order to simplify the decision-making process of engineers, Fuzzy Cluster Analysis (FCA) is employed to categorize NWDNS into groups. An optimum number of clusters are based on the Elbow method. Representative NWDNS, i.e. RNWDNS for each sub-cluster, is based on the membership values. Ranking of RNWDNS is performed with two decision-making algorithms, namely, Preference Ranking Organization METHod for Enrichment of Evaluations-2 (PROMETHEE-2) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The additive ranking rule is also applied to obtain ranks in a group decision-making environment to arrive at the optimal WDN. It is observed that 1020 NWDNSH (H represents Hanoi) generated for the Hanoi are optimally classified into 18 clusters based on the Elbow method, and A13 representing RNWDNSH 37 (cost, resilience, and leakages, respectively, are 7.8818 × 106 $, 0.3194, 0.2024 × 10−3 m3/s) is preferable, respectively. Whereas 272 NWDNSP (P represents Pamapur) generated for the Pamapur are classified into 9 clusters where S6 representing RNWDNSP 203 (cost, resilience, and leakages, respectively, are 3.5159 × 106 Rs., 0.8367, 0.5317 × 10−3 m3/s) is preferred, respectively. The present study facilitated seamless integration of NSGA-II (generation of Pareto front), FCA (reducing to manageable set) and MCDM methods (to rank the reduced manageable set) in a robust manner. [ABSTRACT FROM AUTHOR]

Sustainable supplier selection (SSS) is recognized as a prime aim in supply chain because of its impression on profitability, adorability, and agility of the organization. This work introduces a multi-phase intuitionistic fuzzy preference-based model with which decision experts are authorized to choose the suitable supplier using the sustainability "triple bottom line (TBL)" attributes. To solve this issue, an intuitionistic fuzzy gained and lost dominance score (IF-GLDS) approach is proposed using the developed IF-entropy. To make better use of experts' knowledge and fully represent the uncertain information, the evaluations of SSS are characterized in the form of intuitionistic fuzzy set (IFS). To better distinguish fuzziness of IFSs, new entropy for assessing criteria weights is proposed with the help of an improved score function. By considering the developed entropy and improved score function, a weight-determining process for considered criterion is presented. A case study concerning the iron and steel industry in India for assessing and ranking the SSS is taken to demonstrate the practicability of the developed model. The efficacy of the developed model is certified with the comparison by diverse extant models. [ABSTRACT FROM AUTHOR]

Cancer is a disease that is rapidly expanding in prevalence all over the world. Cancer cells canmetastasize, or spread, across the body and impact several different cell types. Additionally, the incidence rates of several subtypes of cancer have been on the rise in India. The countermeasures for the cancer disease can be taken by determining the specific expansion rate of each type. To rank the various forms of cancer's rate of progression, we used some of the available data. Numerous studies are available in the literature which show the growth rate of cancer by different techniques. The accuracy of the scheme in determining the highest growth rate may vary due to the variation in the dependent factors. Within the context of this research, the Fuzzy triangular technique for order preference by similarity to ideal solution (TOPSIS), is utilized to rank the various categorizations of cancer with the help of four groups of medical professionals acting in the capacity of decision-makers. The number of decision-makers may variate according to the required accuracy of results. The findings of the three-dimensional Fuzzy TOPSIS analysis categorize each variety of cancer according to the rate at which it spreads over time. Numerical results along with visual representation are presented to examine the efficiency of our proposed work. [ABSTRACT FROM AUTHOR]

The airline industry is currently the world's largest mode of transportation. It focuses on a service approach to attract customers, as providing excellent service is a means to earn customer loyalty. The goal of this research is to find the best airline in terms of service quality, which includes tangibility, trustworthiness, and sensitivity. Pythagorean fuzzy sets (PFSs) proposed by Yager [55, 56] is a significant tool for displaying ambiguous and vague information. The goal of this investigation is to broaden application of logarithmic similarity measures under PFSs. Novel logarithmic similarity measures for PFSs are developed. Numerical computations have been carried out to validate our proposed measures. Application of logarithmic similarity measures have been applied to some real-life decision-making problems of customer satisfaction to airlines in India using PFSs based on PFSs. Moreover, comparison of the result for the proposed measures has been carried out with the existing analogous similarity measures to show the efficacy. [ABSTRACT FROM AUTHOR]

Suitable management and treatment of healthcare waste (HCW) have become a key issue due to its potential risk to human health and the environment, predominantly in emerging nations. The selection of an optimal HCW treatment (HCWT) option is a complicated multicriteria decision-making (MCDM) problem that includes several incompatible qualitative and quantitative attributes. This article presents an extended MCDM methodology for assessing and choosing the HCWT options using Pythagorean fuzzy stepwise weight assessment ratio analysis (PF-SWARA) and additive ratio assessment (PF-ARAS) approaches. To do this, attribute weights are estimated by the SWARA model and the ARAS framework decides the preference order of the options on Pythagorean fuzzy sets (PFSs). Furthermore, a selection problem of HCWT options in India is presented within PFSs to illustrate the efficiency and practicality of the introduced framework. Comparative discussions and sensitivity analysis are presented to demonstrate the rationality and stability of the developed approach for prioritizing HCWT options. [ABSTRACT FROM AUTHOR]

The present study investigated the relationships among nine Service Level Benchmarking performance indicators (PI) of urban water supply in India using Interpretive Structural Modeling (ISM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). The findings of ISM have indicated per capita water supply (PCS) as a dependent variable, while water quality is an autonomous variable; complaint redressal and collection efficiency of water charges are driving variables, and the remaining five are linkage variables. Further, fsQCA was used to validate the results of ISM across 53 Indian million-plus cities in the three Baseline Water Stress (BWS) regions, including 17 low-to-high (< 40%), 13 high (40-80%), and 23 very high (> 80%) BWS cities. With PCS as the output condition and the remaining eight PIs as input conditions, fsQCA indicated that low-to-high and high BWS cities follow the ISM results. In contrast, very high BWS cities exhibit contradictory relations to the ISM results. [ABSTRACT FROM AUTHOR]

Following the breakout of the novel coronavirus disease 2019 (COVID‐19), the government of India was forced to prohibit all forms of human movement. It became important to establish and maintain a supply of commodities in hotspots and containment zones in different parts of the country. This study critically proposes new exponential similarity measures to understand the requirement and distribution of commodities to these zones during the rapid spread of novel coronavirus (COVID‐19) across the globe. The primary goal is to utilize the important aspect of similarity measures based on exponential function under Pythagorean fuzzy sets, proposed by Yager. The article aims at finding the most required commodity in the affected areas and ensures its distribution in hotspots and containment zones. The projected path of grocery delivery to different residences in containment zones is determined by estimating the similarity measure between each residence and the various necessary goods. Numerical computations have been carried out to validate our proposed measures. Moreover, a comparison of the result for the proposed measures has been carried out to prove the efficacy. [ABSTRACT FROM AUTHOR]

Digitalization accumulates data in a short period. Smart agriculture for crop identification for cultivation is a common problem in agriculture for agronomists. The generated data due to digitalization does not provide any useful information unless some meaningful information is retrieved from it. Therefore from the existing information system, prediction of decision for unseen associations of attribute values is of challenging. This paper presents a model that hybridizes a fuzzy rough set, real-coded genetic algorithm, and linear regression. The model works in two phases. In the initial phase, the fuzzy rough set is used to remove superfluous attributes whereas, in the second phase, a real-coded genetic algorithm is used to predict the decision values of unseen instances by making use of linear regression. The proposed model is analyzed for its viability using agricultural information system obtained from Krishi Vigyan Kendra of Thiruvannamalai district of Tamilnadu, India. Further, the accuracy of the proposed model is compared with existing techniques. [ABSTRACT FROM AUTHOR]

The increased demand for this form of food delivery has been expected to drastically alter restaurant patrons' dining habits. As people have been forced to stay indoors to prevent the virus from spreading, food delivery services over the internet are in high demand. As established in this study, the planned ideal is a good executive implementation for online meal delivery services. Food delivery services are rapidly growing in India, opening up several opportunities for a wide range of online food delivery (OFD) platforms while also generating a competitive commercial sector. Following that, the fuzzy technique for order performance by similarity to ideal solution method (FTOPSIS) is used to rank online food delivery (OFD) enterprises based on the characteristics chosen. In this paper, we study the present multi-criteria decision-analysis (MCDA) paradigm based on the fuzzy analytic hierarchy process (FAHP) and the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) method to achieve the goal. After that, a hierarchy multiple criteria decision-analysis (MCDA) model based on fuzzy sets theory is introduced to deal with the online food delivery Service in the chain system. The fuzzy analytic hierarchy process (FAHP) is a fuzzy set theory technique for generating criteria weights, which are then used to interpret expert phonological evaluation statements. [ABSTRACT FROM AUTHOR]

Summary: In current years, the death rate from skin cancers (SCs) tends to develop pretty. Various research verified that SC rank third as a deadliest disease, after breast and lung cancer. It will become vital to diagnose this malignancy at an early stage. The objective of this research is to mix machine learning and soft computing techniques to gain higher accuracy within the prediction of SC. To play out the exploration work, we utilized two data sets, one from "Save Life Hospital," India, and the other is the UCI repository skin cancer data set. In this article, three meta‐heuristic algorithms, the FS_GA, the FS_PSO, and the FS_ACO, were used to select the best features from the data set provided to it. The AFRG_algorithm generates a set of fuzzy rules automatically and the RR_algorithm reduces certain fuzzy rules from the fuzzy system. For the SCC_dataset, the end accuracy obtained was 97.67%, 98.45%, and 99.22%. For the UCI_dataset, the end accuracy obtained was 98.81%, 99.72%, and 99.67%. Experimental results on the used datasets show that the proposed method strikingly improves the forecast exactitude of skin malignancy. [ABSTRACT FROM AUTHOR]

Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm‐based feature selection design for thyroid disease classifier with rough type‐2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top‐n features. The proposed hybrid firefly butterfly optimization‐rough type‐2 fuzzy support vector machine (HFBO‐RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well‐known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixed‐kernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO‐RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively. [ABSTRACT FROM AUTHOR]

The concept of domination is one of the most significant topics in graph theory to handle unpredictable phenomena. In this study, an unprecedented idea of domination is introduced in m-polar interval-valued fuzzy graph (m-PIVFG). Domination number (DN), isolated vertex, total dominating set, independent set of domination on m-PIVFG are discussed. Some algebraic properties of domination on m-PIVFG are investigated. Weak domination, strong domination, split and non-split domination, cototal and global dominating sets on m-PIVFG are investigated with some fundamental hypotheses and models. We explore the concept of domination in m-PIVFG by solving a case study of locating new facilities to handle a catastrophe reaction activity due to the "COVID-19 pandemic" in West Bengal, India. Ultimately, conclusions and avenues of future scopes are placed at the end of this study. [ABSTRACT FROM AUTHOR]

In the present situation, the sustainable development goal on "Gender Equality" (SDG 5) is a challenging problem over the entire globe, also in India. In this paper, the fuzzy graph theory is used to analyse the overall performance of India for the SGD 5. To solve this problem, the new concept of pseudo-vertex-covering set of a fuzzy graph to make a well coverage of the entire fuzzy graph with the combined coverage of vertex-covering set and underlying edge-covering set of the fuzzy graph is defined. With this new covering concept of pseudo-vertex-covering set of fuzzy graphs, an analysis of the performance status of India for SDG 5 is made with the individual performance status of all the states/UTs of India. We show a nice covering of the whole country by some particular states/UTs which are actively helping to make the pseudo-vertex-covering of the transformed form of the graph of India as a fuzzy graph. At last, a comparison between the covering score and index score for SDG 5 is given. Here, covering score is a new term in fuzzy graph theory for coverage of a fuzzy graph by some vertices with underlying some edges. Also, an analysis is given which reflecting a clean idea of the methodology for the performance status of India and these help to find the self-governing states/UTs in India for a better performance. [ABSTRACT FROM AUTHOR]

The paper reports a study on the air quality over Kolkata, India during the transition period from post-monsoon to winter. A fuzzy binary relation based approach is implemented to O3, N O2 and P M2 5 and the overall air quality index (AQI). Individual pollutants have been converted to fuzzy sets based on their concentrations as elements of the universe of discourse with appropriate fuzzy membership functions defined on them. AQI has been divided into categories based on the recommendations of the Central Pollution Control Board (CPCB), Govt of India. After defining the fuzzy relations, the fuzzy composite relations have been derived to construct fuzzy membership matrices based on the composite binary fuzzy relations. Afterwards, the membership grades of the matrices for the composite fuzzy binary relations have been studied and N O2 along with O3 have been consideblack as having highly significant influence on the overall AQI over the polluted study zone during the transition from post-monsoon to winter. [ABSTRACT FROM AUTHOR]

The present study contributes to the limited literature on the assessment of job quality (JQ) in India. In doing so, it acknowledges the close relationship between JQ, informality and worker wellbeing. We propose an alternative method of defining informality by distinguishing workers based on multiple dimensions of JQ. Our empirical exercise is guided by the capabilities framework as developed by Amartya Sen, Martha Nussbaum, among others. The study uses the National Sample Survey Office (NSSO) data for the three years 2004–05, 2009–10 and 2011–12. Cluster analysis is used to distinguish workers into good-quality and bad-quality jobs. We also apply the fuzzy set theory to compare the JQ of formal and informal workers under different dimensions. Our results indicate a significant overlap in the trends and determinants of informality under alternative definitions. Further, we find extensive work-based insecurities among the informally employed, especially among specific segments of the population. [ABSTRACT FROM AUTHOR]

Purpose: Data envelopment analysis (DEA) has wide applications in the agricultural sector to evaluate the efficiency with crisp input and output data. However, in agricultural production, impreciseness and uncertainty in data are common. As a result, the data obtained from farmers vary. This impreciseness in crisp data can be represented in fuzzy sets. This paper aims to employ a combination of fuzzy data envelopment analysis (FDEA) approach to yield crisp DEA efficiency values by converting the fuzzy DEA model into a linear programming problem and machine learning algorithms for better evaluation and prediction of the variables affecting the farm efficiency. Design/methodology/approach: DEA applications are focused on the use of a common two-step approach to find crucial factors that affect efficiency. It is important to identify impactful variables for minimizing production adversities. In this study, first, FDEA was applied for efficiency estimation and ranking of the paddy growers. Second, the support vector machine (SVM) and random forest (RF) were used for identifying the key leading factors in efficiency prediction. Findings: The proposed research was conducted with 450 paddy growers. In comparison to the general DEA approach, the FDEA model evaluates fuzzy DEA efficiency giving the user the flexibility to measure the performance at different possibility levels. Originality/value: The use of machine learning applications introduces advanced strategies and important factors influencing agricultural production, which may help future research in farms' performance. [ABSTRACT FROM AUTHOR]

This paper proposes a structured, integrated decision model for evaluating suppliers by combining the fuzzy analytical hierarchy process (FAHP) and grey relational analysis (GRA). The qualitative and partially-known information is incorporated in this decision model using the fuzzy set theory. In this proposed methodology, the weights of the evaluation criteria are calculated by using FAHP, then the ranking of the suppliers is determined by using GRA. Finally to show the robustness of the model, a sensitivity analysis is also performed. In this study, the supplier selection problem of an electroplating industry in the southern part of India was investigated, demonstrating the effectiveness of this developed integrated model. This model can help in solving the complex decision in supplier selection practice. The results generated from the model are properly validated and finally a systematic solution with decision support is provided for decision makers. This model can be integrated with other decision support systems of similar kinds of industries. [ABSTRACT FROM PUBLISHER]

ERP system is a software package that integrates and manages all the facets of the business and deeply influences the success of a business endeavor. The increasing competition in the market, rapidly changing demands, and increasing intricacy of business procedures induce enterprises to adopt ERP solutions. Adopting an ERP solution increases synchronization between business activities and reinforces managerial decision-making. However, it also involves a large investment, a significant amount of human resources and time, and risk of failure. Therefore, the selection of an ERP solution is a crucial decision for enterprises. To address this decision-making problem, we propose a four-stage multi-criteria decision-making approach in this paper. Three prevalent MCDM techniques, DEMATEL, IF-ANP, and IF-AHP, are used in different stages of the methodology to achieve better outcomes. The methodology incorporates the intuitionistic fuzzy sets to capture uncertainty and hesitancy involved in decision makers' judgments. In addition, we develop a novel priority method to derive weights from the intuitionistic fuzzy preference relations. To validate the feasibility of the proposed approach, a case study is carried out on the selection of cloud-based ERP system for SMEs in the Chhattisgarh state of India, which indicates that the proposed four-stage approach effectively handles the ERP selection problem. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to identify, model, analyze and prioritize the barriers in implementing sustainable practices in rubber products manufacturing industry in Kerala, a state in South India. This research provides a more reliable quantitative measure of association of the barriers in the implementation of sustainable practices in the rubber products manufacturing sector. Design/methodology/approach: Interpretive structural modeling (ISM) is used to develop the hierarchical relationships among the barriers in the implementation of sustainable supply chain practices in the rubber products manufacturing sector. The hierarchical structure among the barriers is framed using the experts' opinion. Cross-impact matrix multiplication applied to the classification (MICMAC) analysis is integrated with the output of ISM to classify the barriers into different categories based on the driving power and the dependence power. The driving power and the dependence power of the barriers obtained from ISM constitute two criteria for prioritizing the barriers. The change management required is considered as the third criterion. The fuzzy-analytic hierarchy process (F-AHP) methodology is adopted to prioritize the barriers based on these three criteria. Findings: The hierarchical relationship obtained through ISM methodology shows that lack of government initiatives and lack of benchmark on sustainability measurement in Indian conditions are the major barriers in implementing sustainable practices in the rubber products manufacturing sector. These barriers lead to the lack of management commitment for the implementation of sustainable practices in the organizations under study. The prioritization obtained through the F-AHP method suggests that lack of top-level management commitment, lack of motivation, lack of government initiatives and high initial cost of implementation are some of the major barriers in implementing sustainable practices in the organizations. Research limitations/implications: In the application of the ISM methodology, the contextual relationship between the barriers specified by the experts and their preferences may involve bias. Another limitation of this research is that the modeling and prioritization of the barriers are executed based on the opinion of experts from rubber product manufacturing companies in one state only. Originality/value: To the best knowledge of the authors, this research is the first study on the identification and prioritization of the barriers in sustainable supply chain implementation in the rubber products manufacturing sector. Modeling the inter-relationship among the barriers using ISM technique and prioritizing the barriers using F-AHP are the novel features of the contributions of this work. [ABSTRACT FROM AUTHOR]

• A Fuzzy Inference system is developed for site suitability evaluation of water harvesting structures. • FIS showed high suitability for check dams and farm ponds in the study area. • Suitability maps are envisaged to aid the participatory watershed management in reaching efficient decisions. Watershed management (WM) aims at enhancing the water availability in rainfed areas through water conservation structures, which facilitate storage of water and recharge to ground water. Identification of suitable locations for placing these structures play a major role in the effectiveness of the water conservation. Site suitability evaluation of water conservation structures is performed through an assessment of various biophysical and socio-economic factors. Many of these factors are expressed in linguistic terms rather than precise numeric values, and therefore the output of the evaluation gets subjective. In this study, a fuzzy inference system (FIS) is developed for site selection of water harvesting structures (check dams, farm ponds, and percolation tanks), owing to its capability to handle linguistic data effectively. The suitability zones were identified using the slope, soil permeability and runoff potential as input variables to the FIS. Trapezoidal membership function (MF) was considered for the input and output variables for the fuzzy model and MF parameters were obtained from literature and expert knowledge. The developed FIS is illustrated through an application to Kondepi watershed, Andhra Pradesh, India. The FIS categorized the majority of the watershed area into high suitability class for both farm ponds and check dams. However, the watershed characteristics were not conducive for percolation tanks according to the FIS. A sensitivity analysis of the FIS parameters suggested that the check dam suitability was sensitive to the soil permeability classes. The suitability maps from the FIS were in good agreement with the location of the existing structures in the watershed, suggesting potential use of the developed FIS in WM decisions. [ABSTRACT FROM AUTHOR]

Pythagorean fuzzy numbers have been playing an essential role in decision-making problems. The existing operational rules (ORs) constructed based on Einstein t-norm and t-conorm operators in the Pythagorean fuzzy environment have some drawbacks. This type of t-norm and t-conorm operators need to consider the interaction between the membership degree and non-membership degree of Pythagorean fuzzy numbers and, consequently, produces some unreasonable results in some realistic decision-making problems. To eliminate such deficiencies, interactive operational rules have been formed, revising the existing operational rules. A new aggregation operator, namely, Pythagorean fuzzy Einstein interactive weighted averaging operator, has been presented in this article based on newly revised ORs, and some properties of this operator are discussed. At the same time to eliminate or reduce the effect of existing biases that occurred from the end of decision makers in the evaluation procedure of alternatives in multi-attribute group decision-making problem, we have presented the Pythagorean fuzzy Einstein interactive power weighted averaging operator. A novel multi-attribute group decision-making method for the best sustainable cement industry evaluation in India is presented based on Pythagorean fuzzy Einstein interactive power weighted averaging and Pythagorean fuzzy Einstein interactive weighted averaging operators. The validity of the method is tested by comparing some existing methods. At last, a comparative study with sensitivity analysis shows that the proposed method is superior to some considered existing methods. • The existing operational rules for t-norm and t-conorm are modified in Pythagorean fuzzy environment. • The new PyFEIWA operator is defined based on new operational rules. • To reduce biasness of decision-makers on evaluation of alternative PyFEIPWA operator is introduced. • A MAGDM method is developed using PyFEIPWA function. • A case study is performed for sustainable cement industry to show applicability of the proposed method. • Sensitivity and comparative analysis are verified to validate the results. [ABSTRACT FROM AUTHOR]

This research aims to determine the prime energy security criteria and indices in the case of India. The energy security indices are determined through literature review and energy portfolio experts. This study explores five new perspectives about India's energy security; energy flow risk, operational risk, financial risk, strategic risk, and environmental risk. The study uses integrated methodology hesitant fuzzy set and regret set theory for energy security indices evaluation and selection. This integrated approach emphasizes the psychological characteristics of experts and the variation in decision making. The study reveals that Government has to focus on alternative fuel selection (wj = 0.1077), geopolitical risk (wj = 0.1042), and less capacity of strategic reservoir (wj = 0.0987). Further, the results show that for long term energy security India have to focus on the strategic risk; policies formulation for new fuels, more strategic reservoir, and diversity their energy sources. • The research aims to present India's energy security knowledge map. • Study determines the prime energy security criteria and indices. • Hesitant fuzzy set and regret set theory used for evaluation and selection. • Results reveals that government has to focus on alternative fuel selection. • Strategic and operational risk should be prime consideration. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this study is to explore the implementation of green supply chain management (GSCM) strategies and to select the best GSCM strategy using fuzzy analytical network process (ANP) methodology. The ANP helps in analyzing the interdependence and interrelations among the various determinants and dimensions of GSCM strategy selection. Fuzzy set theory is applied to avoid the vagueness and uncertainty in human preference judgement. This study uses an empirical case study of an Indian automobile organization to validate the applicability of the proposed model. The results show that the resource based strategy is in first position, having the maximum impact on each determinant. The case organization should improve the green management system with the assistance of a suitable GSCM strategy, i.e. the resource based strategy. This study may help managers to make decisions, and to analyze and standardize their environmental advantages dynamically. The robustness of the projected model is checked by conducting a sensitivity analysis. Copyright © 2018 John Wiley & Sons, Ltd and ERP Environment [ABSTRACT FROM AUTHOR]

In resolving multicriteria decision-making (MCDM) problems, it is observed that the solution process is frequently affected by the available vague information together with intra-personal uncertainties associated with expert's opinion. In most of the time fuzzy set or its existing variants fail to capture such uncertainties for making reasonable balance in decision making processes. To reduce the effects of uncertainties, this paper introduces a new variant of fuzzy set, viz., type-2 Pythagorean fuzzy set (T2PFS). The main advantage of using T2PFS in MCDM is that the membership and non-membership values of each argument are presented by Pythagorean fuzzy numbers rather than using fuzzy number or intuitionistic fuzzy number. In model formulation process, at first, T2PFS is explicitly formulated and some basic operations are defined. Subsequently, four aggregation operators are constructed to aggregate the arguments, and their properties are verified. Afterwards, two information measures and a score function are defined. Moreover, two MCDM algorithms based on the developed aggregation operators and TOPSIS are framed. To show the application potentiality of the proposed method, a case study relating to the selection of sustainable transport system in India is considered and solved. In the case study five different fuel-based vehicles, mostly used in India in recent times, are evaluated by the experts as transport alternatives with the consideration of thirteen sustainability related criteria. From the achieved result it is found that in future electric cars would become the most sustainable vehicle in India. Sensitivity analysis of the developed models and comparative study with existing fuzzy and non-fuzzy MCDM methods establish effectiveness of the developed models. This study may facilitate transport authority to select appropriate transport system in connection with sustainable development of a region. • As a new variant of fuzzy set, type-2 PFS is coined for resolving uncertainties associated with MCDM problems. • Terminologies, ranking of type-2 PFSs with aggregation operators are developed by defining arithmetic operations. • Defining distance and entropy measures, type-2 PF entropy weight model is introduced to determine criteria weights. • Two type-2 PF MCDM algorithms based on aggregation operators and TOPSIS are presented. • A case study is performed for the selection of sustainable transport system in India. [ABSTRACT FROM AUTHOR]

Coastal shipping has been widely recognised as a sustainable and efficient alternative to road transport. However, the barriers encountered in the industry have not been systematically studied in any region. From an Indian perspective, this study aims to prioritise barriers to coastal shipping development for effective policy interventions. It identifies important barriers through a Delphi study and then quantifies their cause-and-effect relationships by the decision making-trial and evaluation laboratory analysis ( DEMATEL ) technique. It is interesting that the main barriers, those have most impact on coastal shipping development, are not necessarily the ones most widely recognised. The study also uncovers the hidden cause-and-effect relationships between several barriers. Four main barriers are identified: (1) Indian maritime legislation (especially cabotage rules); (2) issues in the infrastructure and procedures at port and port-centric areas; (3) underdevelopment of small ports; (4) lack of a collaborative culture among the various service providers involved in the logistics supply chain. This study finally recommends relaxing cabotage rules to stimulate the inflow of foreign capital to grow coastal shipping, improving the current port system through joint efforts of the ports, Indian customs and government, and fostering supply chain collaboration. [ABSTRACT FROM AUTHOR]

Several studies have been performed in last many years as to which type of energy source would be suitable for extending energy access for remote rural areas. Energy planning using multi-criteria analysis has attracted the attention of decision makers for a long time. Multi-Criteria Decision Making (MCDM) techniques are attractive in problems having multiple and conflicting objectives. This article develops a methodological framework providing insights to suitability of multi-criteria techniques in the context of operation energy alternatives namely Central Grid/Grid Extension, Solar Home Systems and Microgrids in India. The model was built using the Analytic Hierarchy Process (AHP) with empirical data from various sources. Several parameters like generation cost, price, losses, reliability, capacity, availability, and constraints (geological/local) were investigated. Based on the scheme developed by Saaty—the AHP and Fuzzy Sets using MATLAB these multi-criteria are evaluated and compared. The analysis were carried out under two scenarios namely—environment and cost. Finally alternatives for energy generation were ranked based on the AHP and Fuzzy logic. The results indicate that MICROGRID is the ideal choice among the alternatives for energy generation in a decentralised way and is a possible solution for eliminating energy poverty. [ABSTRACT FROM PUBLISHER]

Artificial intelligence has evolved into a mechanism that can predict environmental changes and investigate climatic conditions. In recent days, extreme precipitation and flooding have resulted in huge damage to the society of human beings and farmers. In real-life, the climatic conditions were predicted using huge collected data. However, the collected data are not utilized properly due to the presence of uncertainty. Most of the research was carried out in time series data for classification and prediction. However uncertain data leads to erroneous predictions. Thus, an integrated model is required to overcome the uncertainty and to improve the prediction accuracy. The paper aims in integrating the Rough Set on Fuzzy Approximation Space (RSFAS) with a Deep Learning (DL) techniques to predict the precipitation level in the southern coastal areas of India in a seasonal way. The proposed model can handle uncertainty using an RSFAS whereas the DL technique is utilized for classification and prediction. The developed model is estimated based on various evaluation metrics and optimizers, benchmarked with existing Deep Learning and Machine Learning (ML) techniques to demonstrate the prediction accuracies obtained with minimized error rate. Consequently, the proposed model assists in understanding the climatic conditions in extreme events to provide a prior warning about heavy rainfall to the people who live near the coastal areas. • Towards the development of a low-cost solution for seasonal precipitation prediction. • Rough Set on Fuzzy Approximation Space with LSTM used for precipitation prediction. • High accuracy as impact of tuning hyperparameters with adam optimizer for prediction. [ABSTRACT FROM AUTHOR]

Fermatean fuzzy set (FFS) is an expedient tool in today's world for coping with uncertainty, ambiguity, and incompleteness that emerge in many decision-making processes. In light of the relevance of FFS, this work offers a Multi-Criteria Group Decision Making (MCGDM) method together with Bonferroni mean and weighted Bonferroni mean operators in FF environment. These operators consider the interdependencies between the parameters into account during aggregation process in group decision making. On the other hand, health care waste (HCW) is an unavoidable byproduct of healthcare institutions, and so HCW management is vital owing to its negative influence on public health and environment. In this study, the proposed FFMCGDM has been applied to determine the optimal HCW treatment technologies (TT) for assuring sustainable environmental development, considering six TTs as alternatives assessing against nine criteria. Moreover, the proposed approach has been illustrated through an empirical case study of many district hospitals in Tripura, India. The obtained results are compared to other two well-known MCDM techniques as well as existing Fermatean Fuzzy Aggregation Operators (FFAOs). A sensitivity analysis has also been carried out to examine the stability of the model. From the analysis, it has been found that the proposed technique is competitive to obtain the optimal HCW TT. For further validation of the proposed method, a well-known MCDM problem, namely Green Supplier Selection (GSS) has been modeled and solved. [ABSTRACT FROM AUTHOR]

Recent disasters have highlighted the need to better understand why communities recover differently from the same disaster. This research aims to study what pre- and post-disaster conditions lead to infrastructural and social recovery. We collected data from 15 villages in India that were affected by the 2004 Indian Ocean Tsunami through observations, interviews, and secondary sources on pre-disaster community conditions, post-disaster strategies, and infrastructural and social recovery outcomes. We examined conditions posited to affect recovery and analyzed the data using fuzzy-set qualitative comparative analysis. There were multiple pathways to infrastructural and social recovery, but strong access to government resources and good recovery agency coordination were necessary in all pathways. A lack of social vulnerability, recovery agency embeddedness, and contracting agency oversight were also important. Through this research, we extend theories of community resilience and vulnerability by linking both pre- and post-disaster conditions to multi-sector recovery outcomes. Using these findings, practitioners can target specific dimensions of community resilience prior to disasters and target successful post-disaster strategies to contribute to recovery. [ABSTRACT FROM AUTHOR]

In January 2020, the World Health Organization (WHO) identified a world-threatening virus, SARS-CoV-2. To diminish the virus spread rate, India implemented a six-month-long lockdown. During this period, the Indian government lifted certain restrictions. Therefore, this study investigates the efficacy of India's lockdown relaxation protocols using fuzzy decision-making. The decision-making trial and evaluation laboratory (DEMATEL) is one of the fuzzy MCDM methods. When it is associated with intuitionistic fuzzy circumstances, it is known as the intuitionistic fuzzy DEMATEL (IF-DEMATEL) method. Moreover, converting intuitionistic fuzzy into a crisp score (CIFCS) algorithm is an aggregation technique utilized for the intuitionistic fuzzy set. By using IF-DEMATEL and CIFCS, the most efficient lockdown relaxation protocols for COVID-19 are determined. It also provides the cause and effect relationship of the lockdown relaxation protocols. Additionally, the comparative study is carried out through various DEMATEL methods to see the effectiveness of the result. The findings would be helpful to the government's decision-making process in the fight against the pandemic. [Display omitted] • This study shows the efficiency of Indian government lockdown relaxation protocols. • An IF-DEMATEL method is constructed to scrutinize the relaxation protocols. • The interrelation between lockdown relaxation protocols is determined. • The TIF numbers are involved in dealing with the uncertainties. • The CIFCS algorithm is proposed as a defuzzification approach for a TIF set. [ABSTRACT FROM AUTHOR]

Changepoint (CP) detection methods can be applied to a broad range of real-world problems and are recently gaining popularity in hydrologic studies. The hydrology of river basins is impacted by several factors such as changes in land use, operation of water storage and distribution systems, and climate change; and as a result, a hydrologic series may exhibit distributional shifts and nonstationarity. In this paper, a fuzzy Bayesian model is studied for CP detection in hydrologic time series of annual rainfall and streamflow in the Mahanadi River Basin in India. The model is based on a two-step, fuzzy Bayesian formulation, which gives a probability distribution for the location of the CP. The methodology has broad applicability since it does not require making any distributional assumptions for the data. The first step consists of a fuzzy clustering of raw time series which transforms the initial data with arbitrary distribution into data that can be approximated with a beta distribution. The second step uses the Bayesian approach with the Markov Chain Monte Carlo (MCMC) method for CP detection in the transformed time series. The method is applied to annual maximum and annual average streamflow and subbasin rainfall for Basantpur and Hirakud stations on the Mahanadi river in India. The results obtained are compared with those obtained using standard CP detection procedures, such as a modified standard normal homogeneity (SNH) test and Wilcoxon's nonparametric rank sum test. Both classical and Bayesian CP detection methods used show that the annual streamflow and the annual rainfall have decreased significantly in the Mahanadi Basin, with a possible CP for the Basantpur station between 1975 and 1980 and for the Hirakud station around 1964. The abrupt decrease was most marked in the annual maximum streamflow series for the Basantpur and Hirakud stations and was attributed to the effects of climate change in the upstream regions of the Mahanadi River. [ABSTRACT FROM AUTHOR]

The logic of fuzzy sets derived from the mathematical theory developed by Zadeh (1965) has not only influenced the development of fuzzy set qualitative comparative analysis (fs/QCA), but has also found entry into poverty research. The aim of this paper is to point the reader to these parallel developments. Poverty researchers necessarily have to make two methodological choices which are always contestable: the identification of a poverty line or threshold, the identification problem, and the choice of a unit of analysis as well as of a measure, the aggregation problem. Current theoretical and practical contributions to poverty research have pointed to the vagueness of the concept itself and the complexity of its measurement and have made use of fuzzy set logic to address these challenges. As shall be argued, fuzzy set theory provides a solution or at least a less arbitrary way forward to address these problems and is particularly suitable for the analysis of multidimensional poverty. Using unique household level data from two villages of Andhra Pradesh, India from 2007, the paper provides an illustrative example of what steps to take to derive a multidimensional poverty index based on fuzzy set theory and discusses the distinct advantages such a procedure offers for policy analysis and evaluation purposes. [ABSTRACT FROM AUTHOR]

Multi-objective fuzzy linear programming (MOFLP) approach is applied with four conflicting objectives, viz maximization of net benefits, employment generation, minimization of cost of cultivation and maximization of revenue generation from municipal and industrial supplies (M and I), on a water resources project (Ukai), Gujarat, India. The results from the model are reported for the most critical year (90% exceedance probability), critical year (85% exceedance probability), normal year (75% exceedance probability), and wet year (60% exceedance probability) inflow conditions. The degree of satisfaction of the proposed MOFLP model, considering all objectives together, for wet year, normal year, critical year and most critical year are found to be 0.527, 0.515, 0.50, and 0.46 respectively; and corresponding net irrigation benefits for different inflow conditions are computed as 10 611.91 Million Rs, 10 476.67 Million Rs, 8 311.0044 Million Rs, and 6 900.051 Million Rs, respectively. The proposed MOFLP model indicated that probable inflow corresponding to 75% dependability level is marginally sufficient to meet the requirement of the study area, and water availability becomes deficit in the command area for 85% dependability inflow condition. The optimized crop areas from the model, complying with the requirement of existing flood rules, and satisfying relevant conflicting objectives would help the decision makers in sustainable management of water resources in Ukai command area. [ABSTRACT FROM AUTHOR]

Abstract: The main objective of the paper is to developed a methodology, named as vague Lambda-Tau, for reliability analysis of repairable systems. Petri net tool is applied to represent the asynchronous and concurrent processing of the system instead of fault tree analysis. To enhance the relevance of the reliability study, vague set theory is used for representing the failure rate and repair times instead of classical(crisp) or fuzzy set theory because vague sets are characterized by a truth membership function and false membership functions (non-membership functions) so that sum of both values is less than 1. The proposed methodology involves qualitative modeling using PN and quantitative analysis using Lambda-Tau method of solution with the basic events represented by intuitionistic fuzzy numbers of triangular membership functions. Sensitivity analysis has also been performed and the effects on system MTBF are addressed. The methodology improves the shortcomings of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation. The washing unit of a paper mill situated in a northern part of India, producing approximately 200ton of paper per day, has been considered to demonstrate the proposed approach. The results may be helpful for the plant personnel for analyzing the systems'' behavior and to improve their performance by adopting suitable maintenance strategies. [Copyright &y& Elsevier]

An efficient flood alarm system may significantly improve public safety and mitigate damages caused by inundation. Flood forecasting is undoubtedly a challenging field of operational hydrology and a huge literature has been developed over the years. In this paper, we first define ordered ideal intuitionistic fuzzy sets and establish some results on them. Then, we define similarity measures between ordered ideal intuitionistic fuzzy sets (OIIFS) and apply these similarity measures to five selected sites of Kerala, India to predict potential flood. [ABSTRACT FROM AUTHOR]