Technique For Order Performance By Similarity To Ideal Solution Method Research Articles

Evaluating lecturer performance in Vietnam: An application of fuzzy AHP and fuzzy TOPSIS methods

Higher education is regarded as being of paramount importance in Vietnam and as being essential to raising the level of the country's labor force and promoting economic progress. Evaluation of lecturers is one of the institution's activities and a crucial component of managing human resources in higher education institutions. How to evaluate faculty members' overall performance using a range of criteria is one of the key evaluation-related challenges. This study presents a method that uses fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) to assess and rank the performance of lecturers. Specifically, the evaluation framework is developed by identifying criteria and sub-criteria based on a comprehensive review of existing literature. Following that, the fuzzy AHP approach is used to determine the weights of the criteria and sub-criteria using the pairwise comparisons. The Fuzzy TOPSIS approach is employed to assess and prioritize lecturers identified through expert evaluation. When applied in group decision-making, utilizing fuzzy AHP and fuzzy TOPSIS promotes agreement among decision-makers and diminishes uncertainty in decision-making processes. The utilization of the multiple criterion measurement approach can then be used to carry out the evaluation. The suggested framework is also demonstrated via a case study. The use of this framework can improve the evaluation's objectivity, accuracy, and scientific methodology. It is believed that this work will assist managers of higher education institutions improve their standards for educational quality.

Land Use Change and Ecological Security Evaluation in Haikou City

With the continuous development of society and the acceleration of urbanization, profound changes have occurred in the utilization of land resources. Unreasonable land use and development pose serious threats to land ecological security, leading to a series of ecological security issues that constrain regional sustainable development. This study takes Haikou City as the research object and utilizes remote sensing (RS) and geographic information system (GIS) technologies to analyze the changes in land use in Haikou City from 2002 to 2022. Simultaneously, a land ecological security evaluation system is constructed based on the "Pressure-State-Response" (PSR) model. The weights of the indicators are determined by combining the Entropy weight method and the analytic hierarchy process (AHP). The TOPSIS (technique for order performance by similarity to ideal solution) method is then applied to evaluate the land ecological security index. The results indicate that during the period from 2002 to 2022, the cropland and impervious areas in Haikou City exhibited significant growth trends, while the areas of grassland, water, barren land, and forest decreased. Despite this, the comprehensive ecological security index of Haikou City showed an overall fluctuating upward trend, reaching its optimal state in 2022, with the land ecological security level improving from "Minor Warning" to "Relatively Safe". These research findings provide scientific evidence for adjusting land use policies and formulating relevant measures, and also serve as an important reference for promoting the sustainable utilization and ecological security protection of land resources in Haikou City.

Ranking Strategic Goals with Fuzzy Entropy Weighting and Fuzzy TOPSIS Methods: A Case of the Scientific and Technological Research Council of Türkiye

This study involves an integrated approach consisting of the Fuzzy AEW Method which considers all relevant criteria and involves the contribution of all members of a strategic planning team in the determination of strategic goals as well as the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. The Fuzzy AEW method has been used in weighing the criteria whereas the Fuzzy TOPSIS method has been used in determining the order of the alternatives. This article presents a real-world case using this model in setting targets for improving the institutional capacity of the Scientific and Technological Research Council of Türkiye (TÜBITAK). In this study, strategic goals have been set forth considering the opinion of strategic planning experts and previous strategic plans, and then the said method has been applied. The model could easily be applied both in the public and private sectors. This new model involves the effective planning of scarce resources and ensures digitalization in planning as well as the determination of goals through analytical methods. Ineffective meetings and workshops of the past will be replaced by a participatory and transparent structure.

Selection of virtual team members for smart port development projects through the application of the direct and indirect uncertain TOPSIS method

The virtual team member selection problem has a significant impact on the execution efficiency of smart port development projects, especially in this temporarily constructed team environment and in the era of information and communication technology development. On the other hand, selecting the optimal virtual team members will directly affect the harmony of the remote team working environment. Apart from that, the process of selecting virtual team members from all candidates is a multicriteria decision-making (MCDM) problem. This paper develops a direct and indirect uncertain MCDM methodology (called DI uncertain TOPSIS) based on failure mode and effect analysis (FMEA), decision-making trial and evaluation laboratory (DEMATEL), and a technique for order performance by similarity to ideal solution (TOPSIS) to help port companies select the optimal team members in a virtual environment where smart port development projects execute more efficiently, safely, and remotely working environments harmoniously. The FMEA and DEMATEL methodologies are combined with the TOPSIS method to analyze the uncertainty of the virtual team member selection problem and determine the Risk Priority Number (RPN) for the MCDM criteria. The TOPSIS method approaches the MCDM problem and obtains the final virtual team member combinations.

Evaluation of quality of online shopping services in times of COVID-19 based on E-S-QUAL model and Fuzzy TOPSIS method.

This paper aims to propose an approach to evaluate the quality of online shopping services in times of pandemic COVID-19, from the ordering of quality attributes taking into account customers' perception. The proposed approach was developed from a structured questionnaire containing 25 quality attributes adapted from the E-S-QUAL model and applied to consumers of online shopping services. Fuzzy set theory was used in the approach to simplify the subjectivity of human judgment, along with the extension of Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). Therefore, this research was classified as applied, exploratory, quantitative and survey. To achieve the research objective, 819 questionnaires were collected. Among the main findings, it is highlighted that the attributes "product availability", "products with excellent quality", "confidence in online shopping processes" and "ease of buying online" were the ones that presented the best perceptions of quality by the respondents. At the other end, the attributes "opinion sharing on social networks", "buying online is a good option when you have little time", "distraction in online shopping searches" and "shopping online is a pleasure" showed the highest level of dissatisfaction with the service. Thus, this article highlights the importance of online shopping services in times of the pandemic caused by COVID-19, and its main contribution and originality is the development of an approach that aims to support the decision-making process, establishing strategic actions for the continuous improvement of online shopping services with the reduction of subjectivity in customer perception and with successive refinements.

Selection of Business Process Modeling Tool with the Application of Fuzzy DEMATEL and TOPSIS Method

The business process modeling tool selection problem has a significant impact on the overall performance of enterprise business process modeling, which will directly affect the development of enterprise information systems. Apart from that, the process to select the business process modeling tool from all alternatives is a Multi-Criteria Decision Making (MCDM) problem. This paper develops a methodology based on the hybrid fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to help companies select the optimal business process modeling tool, where the business process modeling process is more efficient, economic and safe. The proposed method has the following state-of-the-art contributions and features: (1) the latest application of the MCDM methodology to the field of BPM tool selection, (2) addressing the direct and indirect impact between criteria in the selection of BPM tools, and (3) considering the hybrid fuzzy (uncertainty) decision-making issue in the BPM tool selection process. Meanwhile, the mathematical formula in TOPSIS can be regarded as a formula for solving a symmetric problem. The hybrid fuzzy DEMATEL method is used to obtain the weight for the criteria to be considered in the BPM tool selection process, and the TOPSIS method is used to obtain the final business process modeling tool.

Performance assessment of Coupled Model Intercomparison Project Phase 5 models in tropical South America using TOPSIS‐based method

AbstractThe use if the general circulation model (GCM) as a boundary condition for the dynamic regionalization process, without robust technical criteria, is one of the problems responsible for increasing the uncertainties of simulations and projections of climate scenarios, mainly affecting the observed trends and consequently the representation of future trends. Based on this premise, the present study evaluated the historical ability of 30 models that make up phase 5 of the Coupled Model Intercomparison Project using the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. The meteorological variables used in this assessment were precipitation and near‐surface air temperature from 1975 to 2005 in the southern sectors of the Amazon Basin (SAMZ), Eastern Northeast Brazil (ENEB), and the intersection area between these regions, called MATOPIBA, was used to select the best GCM. The annual cycle, Taylor diagram and TOPSIS method were used as metrics of similarity between the models and the reference dataset. In general, the models simulated the temperature more accurately than the precipitation, with lower dispersions. According to the TOPSIS, over SAMZ, the HadGEM2‐ES (MIROC5) model was ranked with greater (less) ability to represent precipitation. In turn, for temperature the ensmean_cmip5 (IPSL‐CM5A‐MR) was the best (worst). Over ENEB, the model that showed the greatest (lowest) ability to simulate precipitation was CSIRO‐ACESS1.0 (HadGEM2‐AO). For temperature, the NorESM‐ME model (INMCM4) was ranked first (last). Over MATOPIBA, the CSIRO‐ACESS1.0 (NorESM‐ME) model was selected as having the best performance and the MPI‐ESM‐LR (MPI‐ESM‐LR) as the worst performance when representing precipitation (temperature). The similarities and discrepancies in the capacity of the GCM presented in the different metrics covered in this study can assist in the selection of more appropriate climate models in other regions for future studies of climate change.

Application of multi-criteria decision making models to forest fire management

The study of the effective factors for forest fire prevention policy can help to reduce long term extensive environmental fire damage. In other words, forest fire management is the result of a complex interaction of many criteria. The present study aims to analyze the most effective criteria for the Arasbaran forest, Iran, based on the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and the Simple Additive Weighting (SAW) methods. The five top optimal criteria selection by the TOPSIS method identified that, “association and cooperation between the executive and responsible institutions” is ranked highest (CCi + =0.85). “Lack of deterrence law in dealing with forest fire offenders in human-caused forest fires” has the second rank (CCi + = 0.84), followed by “Lack of up-to-date scientific information on susceptible areas in the region”, “Increasing the cooperation of NGOs and increase public trust”, and “Lack of forest road network access to ignite regions” (CCi + = 0.789, 0.787, and 0.77, respectively). The five top optimal criteria resulting from the SAW method showed that “Local people participations” provided the highest score (FS = 0.39), followed by “association and cooperation between the executive and responsible institutions (FS = 0.39), “Increasing the cooperation of non-governmental organizations (NGOs), “increasing public trust” (FS = 0.36), “Raising awareness of the position of natural resources among local peoples and attracting their cooperation” (FS = 0.35), and “Optimal use past experiences” (FS = 0.34). From these results, we find that collaboration between institutions is a major requisite for successful fire management, and should be done in parallel with involvement of local actors in a combined synergistic bottom-up top-down approach. We suggest that the impact of ecological and environmental factors on the occurrence and spread of forest fires (as complementary factors) should be investigated to demonstrate the best criteria for comprehensive management strategies in future studies.

Extension of the TOPSIS method to group decision-making

TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) is a very practical decision support method used in several areas of life. This method already exists in the literature in the context of a single decision maker. In order to adapt this method to group decision making, which can be easily applied in various situations, this work extended the TOPSIS method to group decision making using the quadratic mean and the geometric mean. In this work, numerical applications have been made and interesting results have been obtained.

A novel systematic multi-objective optimization to achieve high-efficiency and low-emission waste polymeric foam gasification using response surface methodology and TOPSIS method

Gasification is one of the most important thermochemical processes to convert a solid fuel to energy carriers of a gaseous product called syngas. This process has been well addressed in the literature for biomass and several valuable researches have been performed on plastic waste gasification. However, it is the first effort for a systematic comprehensive investigation and multi-objective optimization of gasification process for waste polymeric foams using response surface methodology and TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) approach. Air and steam waste rigid polyurethane foam gasifications were modeled using coupled method of Gibbs minimization free energy and Lagrange method of undetermined multipliers, and then, validated. Effects of key features consisting gasification temperature and moisture content in both air and steam gasification types and equivalence ratio in air and steam to waste foam ratio in steam types were thoroughly studied on gas composition and energy and hydrogen efficiencies. Analysis of variance was employed for recognizing the most effective parameters on air and steam gasification performances. The results revealed that hydrogen and energy efficiencies of air gasification at multi-objective optimum conditions were 42.68 % and 89.58 %, respectively, and these values were 64.02 % and 96.52 % for steam gasification type. Air gasification of waste rigid polyurethane foam produced 3.13 g of CO2 emission at optimum state; however, its value was 10.02 g for steam gasification type. A multi-criteria decision analysis based on TOPSIS method was utilized for selecting the best gasification type in different scenarios and the findings indicated that air gasification had better performance compared to steam type due to its low emissions.

Application of Fuzzy AHP and Fuzzy TOPSIS methods for the new normal problem

The government have new program to deal with the pandemic impact of the Coronavirus, which is said with the new normal. The implementation of the new normal itself requires more attention in terms of the readiness of a region to implement the program. Determination of these areas can be done using the Fuzzy Analytical Hierarchy Process(AHP) and fuzzy technique for Order Performance by Similarity to Ideal Solution(TOPSIS) methods. Fuzzy AHP is used to determine the weight of the existing criteria, while Fuzzy TOPSIS is used to determine the safest area and is ready to implement the new normal. The case study for the problem in this paper is Central Java Province, with alternatives districts and cities. The influencing criterias are the number of People Under Surveillance (PeUS), the number of Patient Under Surveillance (PaUS), the number of patients being treated, the number of positive patients who died, and positive patients who recovered. Triangular fuzzy number approach both methods. The results of this study found that 30% of the total number of districts or cities are ready to implement the New Normal program. This study also explain that the higher of the patient number being treated, the less safe an area will be. While the patients number who recover is increasing, then an area will be ready to implement a New Normal program.

Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment

COVID-19, which emerged in December 2019, has affected the entire world. Therefore, COVID-19 has been a subject of research in various disciplines, especially in the field of health. One of these studies was the report made by the Deep Knowledge Group (DKG) consortium in which safe regions for COVID-19 were determined. In the report, the main criteria of quarantine efficiency, government efficiency of risk management, monitoring and detection, health readiness, regional resilience, and emergency preparedness are used in the evaluation of countries and regions (alternatives). As the data and research structure used in this report are based on multi-criteria, the purpose of this study is to evaluate and analyse the safety levels of 100 regions in the world in terms of COVID-19 using Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumsa Optimizacija I Kompromisno Resenje (VIKOR) and Complex Proportional Assessment (COPRAS) methods. The data and information required in the methods were obtained from a report prepared by the DKG. The results of the methods were compared with the ranking results presented in a report of the DKG. Accordingly, it has been observed that the method that provides the closest results to the results of the report is the COPRAS method, and the method that gives the most distant results is the VIKOR method.

Optimization of Performance and Exhaust Emissions of a Spark-Ignition (SI) Engine Fueled with Bioethanol-Gasoline Blends using TOPSIS Methodology

Energy crises and environment impact forced governments and countries to develop utilizing renewable energy in different forms. One of the first forms of alternative energy which was in use is bioethanol which is using in SI (spark ignition) engines as pure fuel or after blending with gasoline. The selection of proper blending of bioethanol plays an important role in the production of alternative energy. In this investigation, the use of TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) to select the optimal value of the engine performance and exhaust pollutants which run at various bio-ethanol-gasoline fuel blends, namely E5, E10, E15 and E20. Obtained results indicate that performance parameters (i.e. torque, power) for different bioethanol-gasoline blends improved. It is evident from results that there is an increase in NOX as well Carbon dioxide (CO2) emission while UBHC and CO decreased. TOPSIS methods indicate that a mixture ratio equal to 20% (E20) and an engine speed equal to 3000 rpm were chosen to be optimal working condition.

Prioritization in Strategic Environmental Assessment Using Fuzzy TOPSIS Method with Random Generation for Absent Information in South Korea

This study evaluated a fuzzy technique for order performance by similarity to ideal solution (TOPSIS) as a multicriteria decision making system that compensates for missing information with undefined weight factor criteria. The suggested Fuzzy TOPSIS was applied to ten potential dam sites in three river basins (the Han River, the Geum River, and the Nakdong River basins) in South Korea. To assess potential dam sites, the strategic environment assessment (SEA) monitored four categories: national preservation, endangered species, water quality, and toxic environment. To consider missing information, this study applied the Monte Carlo Simulation method with uniform and normal distributions. The results show that effects of missing information generation with one fuzzy set in GB1 site of the Geum River basin are not great in fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) estimations. However, the combination of two fuzzy sets considering missing information in Gohyun stream (NG) and Hoenggye stream (NH) sites of the Nakdong River basin has a great effect on estimating FPIS, FNIS, and priority ranking in Fuzzy TOPSIS applications. The sites with the highest priority ranking in the Han River, Geum River, and Nakdong River basins based on Fuzzy TOPSIS are the Dal stream 1 (HD1), Bocheong stream 2 (GB2) and NG sites. Among the sites in all river basins, the GB2 site had the highest priority ranking. Consequently, the results coincided with findings of previous studies based on multicriteria decision making with missing information and show the applicability of Fuzzy TOPSIS when evaluating priority rankings in cases with missing information.

Selection of appropriate hydrocolloid for eggless cakes containing chubak root extract using multiple criteria decision-making approach

The aim of this study was to choose the best hydrocolloid to make eggless cakes, where egg was replaced with Chubak root extract (CRE, Acanthophyllum glandulosum). The effect of xanthan gum (XG), guar gum (GG) and carrageenan gum (CG) were investigated on functional properties of batter (microstructure, consistency, density, textural properties) and cake (volume, density, texture, color and sensory attributes). The best hydrocolloid was selected using TOPSIS (Technique for Order Performance by Similarity to Ideal Solution), which is one of the best multiple criteria decision-making (MCDM) approaches. The results revealed that replacement of egg by CRE reduced the consistency and gas cells of batter, cake volume, textural properties and sensory scores and increased batter and cake density, presenting low quality of the eggless cake. Incorporation of hydrocolloids improved the quality of the final product by increasing consistency and gas cells of batter and volume, texture and sensory parameters of cake. The eggless cakes containing guar gum showed the best quality compared with other hydrocolloid-containing eggless cakes based on TOPSIS method.

Evaluation of Agricultural Extension Service for Sustainable Agricultural Development Using a Hybrid Entropy and TOPSIS Method

Agricultural extension service is the foundation of sustainable agricultural development. The evaluation and analysis of the agricultural extension service for sustainable agricultural development can provide an effective analytical tool for sustainable agriculture. This paper analyzes the influence of agricultural extension service on sustainable agricultural development, and constructs an evaluation system for sustainable agricultural development from the four dimensions of agricultural environment, society, economy, and agricultural extension service. This work proposes a framework based on the combination of technique for order performance by similarity to ideal solution (TOPSIS) and entropy method to evaluate the performance of the evaluation system. Taking three national modern agriculture demonstration zones in Suzhou in Jiangsu Province as a case study, the method was verified. Moreover, the main factors affecting sustainable agricultural development are discussed, and the improvement measures and management suggestions are also put forward to reduce the obstacles to sustainable agricultural development and improve sustainable agriculture practice.

Optimization of wollastonite reinforced epoxy composites

In the recent years, one of the main challenges faced by industries, is developing a new material for the replacement of the conventional one’s so as to meet the current and future demand- all at the lowest possible cost. Out of the may available new materials, selection of appropriate material for engineering applications is another challenge which is often observed to be a multi-criterion decision-making (MCDM) problem. Rapidly altering product requirements has led to use different MCDM techniques for meeting the customer needs. This paper presents a procedure to evaluate optimal material using an MCDM method for manufacturing of light weight component in different industries. The epoxy micro-composites developed are given ranking using the TOPSIS (Technique for order performance by similarity to ideal solution) method. In this work, there are six number of alternatives i.e., epoxy composites with and without wollastonite (CaSiO3) fillers viz. EW0: Epoxy + 0 wt% CaSiO3, EW1: Epoxy + 1 wt% CaSiO3, EW2: Epoxy + 2 wt% CaSiO3, EW3: Epoxy + 3 wt% CaSiO3, and EW4: Epoxy + 4 wt% CaSiO3, EW5: Epoxy + 5 wt% CaSiO3 and six criterions for material selection used for the optimization. The outcomes show that the epoxy composite with 2 wt% CaSiO3 has highest relative closeness and therefore, shows the highest ranking.

SELECTION OF PHASE CHANGE MATERIAL FOR DOMESTIC WATER HEATING USING MULTI CRITERIA DECISION APPROACH

ABSTRACT The Selection of appropriate phase change material in the field of domestic water heating system is a difficult task due to the availability of the variety of an enormous number of different materials. The researcher’s primary objective is a selection of the desired PCM, which will give the best thermal performance of the domestic water heating latent heat storage system. The paper reports the selection of phase change material with three multi-criteria decision-making (MCDM) methods with conflicting and different objectives. An analytic hierarchy process (AHP) and entropy methods are used to determine the importance criteria weights. For selection purpose, the TOPSIS (Technique for order performance by similarity to ideal solution), VIKOR (Vise KriterijumskaOptimizacijaKompromisnoResenje), and EXPROM2 methods were used, which gives ranking to the alternatives using compromised weight obtained by AHP and entropy methods. This MCDM technique provides a list of all desirable options from the best to the worst suitable materials. Depending on the rankings obtained, the best PCM was found to be the Sodium acetate trihydrate (90%) + graphite (10%), Stearic acid and n-Hexacosane at the given criteria due to its higher physical, chemical, and thermal properties than other alternative PCM materials. The results obtained showed that these methods are feasible tool for the Selection of the best-suited phase change material and can be applied to any material selection decision-making problem to find the best materials.

Enhanced cognitive workload evaluation in 3D immersive environments with TOPSIS model

Research puts forward perception-based cognitive workload evaluation methods to help VR developers and users measuring their workload when playing with a VR application. Approaches to measure workload based on biosensors have progressed significantly, while evaluation based on subjective methods still rely on standard questionnaires such as the NASA-TLX table, the Subjective Workload Assessment Technique and the Modified Cooper Harper scale. The pre-defined questions enable operators to carry out experiments and analyse the data more easily than with biofeedback. However, the subjective evaluation process can bias the results because of unperceived internal changes and unknown factors among users. It is therefore necessary to have a method to handle and analyse this uncertainty. We propose to use the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model to analyse the NASA-TLX table for measuring the overall user workload instead of using the classical weighted sum method. To show the advantage of the TOPSIS approach, we performed a user experiment to validate the approach and its application to VR, considering factors including the VR platform and the scenario density. Three different weighting methods, including the fuzzy Analytic Hierarchy Process (AHP) from fuzzy logic, the classical weighting based on pairwise comparison and the uniform weighting method, were tested to see the applicability of the TOPSIS model. The results from TOPSIS were consistent with those from other evaluation methods; a significant reduction in the coefficient of variation (CV) was observed when using the TOPSIS model to analyse the NASA-TLX scores, indicating an enhanced precision of the workload evaluation by the TOPSIS method. Our work has a potential application for VR designers and experimenters to compare cognitive workload among conditions and to optimize the settings.

Risk assessment of water inrush to coal seams from underlying aquifer by an innovative combination of the TFN-AHP and TOPSIS techniques

Coal seam exploitation in many coal mines of North China coalfields is threatened by water hazards from underlying aquifers. Resolving the potential risk zonation of water inrush to prevent and control flood water disasters is important and challenging work. To effectively assess the risk of water inrush from the Ordovician aquifer to the exploitation of the No. 11 coal seam in the Zhaizhen coal mine, Xinwen coalfield, China, this paper proposes a water inrush risk index (WIRI) model based on a combination of the triangular fuzzy number (TFN), analytic hierarchy process (AHP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) methods. The WIRI model integrates seven factors, namely, the water abundance of the Ordovician limestone aquifer, water pressure of the Ordovician limestone aquifer, aquifuge thickness, brittle rock percentage within the aquifuge, fault intensity index, fault endpoint and intersection density, and height of the mine-damaged zone, and was established using the TOPSIS method based on the initial decision data and the factor weights determined by the TFN-AHP method. The potential water inrush risk zonation was finally determined by the WIRI method. There are two zones in the study area: the safe area, and the dangerous area which was further classified into two subzones including the moderately dangerous area and the extremely dangerous area, providing guidance for engineers to predict and control floor water inrush. The predictions were validated by limited water inrush cases and safe mining samples. The results predicted by the WIRI method conform to the actual results, while the predictions by the T method do not agree well with the observation: with the T method, the three water inrush samples were judged to be safe. These results indicate that the proposed method can effectively predict water inrush risk and can be extended to other mines threatened by an underlying aquifer.