Technique For Order Preference By Similarity To Ideal Solution Research Articles

Twist-related parametric optimization of Joule heating-triggered highly stretchable thermochromic wrapped yarns using technique for order preference by similarity to ideal solution

Abstract A scalable approach for manufacturing highly stretchable thermochromic wrapped yarns has been reported previously. Herein, the effects of twist-related technological parameters, namely, wrapping density and outer-inner twist ratio are investigated and have been optimized by technique for order preference by similarity to ideal solution (TOPSIS). The results indicate that the preparatory twist-related parameters have a remarkable effect on the spiral geometrical configuration of yarn constituents, and thus in turn influencing the final tensile and elastic properties of wrapped yarns. The wrapping density of 800 T·m−1 and outer-inner twist ratio of 1.25 were considered as the optimal alternative using TOPSIS. Moreover, a negative relation between voltage and color-changing time of yarn spun with optimal parameters was established. It was also found that the color of yarn above elevated triggered voltages always switched from purple to pink but followed different color-changing paths. More importantly, thermochromic response of yarn is insensitive to the applied strain.

Helical airfoil corrugated tubes: A numerical comparison with common corrugated profiles and geometrical optimization by multi-objective algorithm

In this study, helical airfoil corrugated tube (HACT) has suggested to improve the heat transfer capabilities of the tube heat exchanger. Firstly, thermal-hydraulic performance of HACT is numerically compared with common helical corrugated tube (HCTs) such as helical semi-ellipse corrugated tube (HSECT), helical semi-circle corrugated tube (HSCCT) and smooth tube based on thermal performance parameters: Nusselt number (Nu), friction factor (f) and performance evaluation criteria (PEC). Helium under high Reynolds number (Re) is the working fluid of this study. By analyzing the numerical outcomes, it is found that HACT has the heat transfer coefficient roughly 4 % higher than that of HSECT, around 5 % higher than that of HSCCT and more than 60 % higher than that of smooth tube. Then, a multi-objective optimization (MOO) is carried out to optimize the geometrical configuration of HACT. Initially, a numerical experimental design is constructed using the Box-Behnken method within the response surface methodology (RSM), focusing on two objectives and three factors. Subsequently, significant regression models (SRM) are derived via analysis of variance (ANNOVA). Then, leveraging these significant regression models of Nu and PEC, the geometric optimization of HACT is executed using Non-dominated Sorting Genetic Algorithm (NSGA-II), ultimately leading to the attainment of the Pareto front. Finally, based on Pareto optimal set, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied for decision-making involving multiple objectives.

Wavelet denoising based on comprehensive index optimization and improved L2 regularization for load identification

Load identification, as an inverse problem, is still one of the challenges in the field of vibration engineering. In this paper, a comprehensive load identification method combining entropy weight Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) noise reduction evaluation algorithm and improved L2 regularization method is proposed to improve the load identification accuracy from the following two perspectives. Firstly, the entropy weight TOPSIS method is used to evaluate the wavelet denoising scheme comprehensively, and the optimal denoising scheme is found to reduce the noise interference in the process of inverse problem solving. Secondly, we introduce a new regularization filter function, and search for the regularization parameters by Generalized Cross-Validation (GCV) criterion and one-dimensional optimization algorithm to improve the ill-posedness of the inverse problem. Finally, the simulation and experimental verification of single-source load identification are carried out with the scaled crankcase model, while the accuracy is compared with the Tikhonov regularization method. The results show that the integrated algorithm proposed in this paper can improve the accuracy of load identification while overcoming the inadequacy of inverse problem. Unlike previous studies, the comprehensive evaluation method of denoising effect introduced in this paper provides a way to judge the denoising effect in practical engineering, and the experimental model is closer to practical engineering structures, which shows potential engineering application value.

Characteristics of Urban Flood Resilience Evolution and Analysis of Influencing Factors: A Case Study of Yingtan City, China

Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological, and infrastructural. A hybrid model combining Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight Method (EWM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) From 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR were highly correlated with urban infrastructure development; however, the ecological resilience in the subsystem showed a fluctuating downward trend because of the influence of the surface area of lakes and rivers; (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

Optimal design of colour formulation prediction for cotton fabrics based on NSGA‐II and TOPSIS

AbstractThe prediction of colour formulation is an important step in reproducing the target colour. At present, there are relatively few researches on multi‐objective colour formulation problem, and the colour matching accuracy needs to be improved. In this research, a multi‐objective evolutionary meta‐heuristic method based on the Fast and Elitist Multi‐objective Genetic Algorithm (NSGA‐II) was proposed to predict the target colour recipes. The method used dye concentration as a variable and included three objective functions: (1) minimising the CMC (Colour Measurement Committee) colour difference between the formulation colour and the target colour, (2) minimising the metamerism index, and (3) minimising the cost of the formulation. The algorithm could obtain the Pareto optimal solution set after iteration. On this basis, the best combination of formulations was selected from the optimal solution set by combining the Expert Scoring Method (ESM), Entropy Weight Method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The prediction effect of the model was evaluated by taking cotton fabrics and reactive dyes actually used in plant as examples. The results showed that 87.5% of the formulations met the CMC colour difference value of no more than 1, the metamerism index of 90.0% of the formulations did not exceed 1, and the cost of 92.5% of the formulations was reduced relative to the maximum extent in the Pareto optimal solution set. Further studies should be focused on removing duplicate individuals to give better diversity in the Pareto optimal solution set.

Application of Interval Valued Intuitionistic Fuzzy Uncertain MCDM Methodology for Ph.D Supervisor Selection Problem

The selection of Ph.D (Doctor of Philosophy) supervisor is always a vital and interesting problem in academia and especially for students who want to carry out Ph.D. Nowadays, selecting a supervisor for Ph.D in a scientific manner becomes a challenge for any student because of the variety of options available to the scholar. In this context, the present study aims to formulate a model for Ph.D. supervisor selection from the offered alternatives in an academic institute. A hybrid multi-criteria decision making (MCDM) framework has been applied to select the suitable supervisor of the student’s preferred criteria under interval-valued intuitionistic fuzzy (IVIF) scenario. The IVIF Analytic Hierarchy Process (AHP) has been employed to prioritize the criteria, whereas IVIF Technique for order preference by similarity to ideal solution (TOPSIS) technique is engaged to rank the available supervisors based on criteria weight. A set of eight criteria and five alternatives have been considered for modeling the problem. Moreover, the potential criteria are weighted and ranked by the multiple decision makers in the present study. To examine the consistency and robustness of the proposed integrated approach, sensitivity analysis and comparative analysis have been carried out. From all the analyses, it can be conferred that the suggested approach is quite useful to apply in different decision-making scenarios.

Decision Support System For New Employee Selection Using AHP And TOPSIS

There are so many prospective workers with the same educational background, but not necessarily in accordance with the required company position and not necessarily they have the same expertise. To minimize the occurrence of errors, it can be done by making a decision-making system (DSS) to provide these recommendations. In this study, the Analytical Hierarchy Process (AHP) and the Technique For Order Preference by Similarity to Ideal Solution (TOPSIS) method were used to provide recommendations for prospective new employees. The steps taken are to compare the importance of each criterion weight with the AHP method. Then the ranking stage is carried out using the TOPSIS method to get recommendations for selected employees. The data used in this study is primary data in the form of 70 data on prospective employees from PT Hutama Jaya Perkasa. From the 70 data then selected to be 36 prospective employees based on the order of the highest ranking. Software testing is done by comparing the results of system calculations and the results of company calculations. Based on the test results obtained an accuracy value of 94.4%.

Green Transition Assessment, Spatial Correlation, and Obstacles Identification: Evidence from Urban Governance Data of 288 Cities in China

The green transition of China’s cities is crucial for ecology civilization realization. Based on the driver–pressure–state–impact–response (DPSIR) framework, an integrated technique for order preference by similarity to ideal solution (TOPSIS) model with entropy weight, this study achieved the comprehensive assessment of the green transition of 288 province-level municipalities and prefecture-level cities in China over 18 years from 2002 to 2019, in addition to the spatial correlations and obstacles analysis. The results indicate that major cities in China have a more significant green transition value, and the eastern region is developing fast, while the northeast region is relatively slow. There was heterogeneous spatial distribution for green transition, because of the disequilibrium sustainable development of 288 cities. Green transition has a significantly positive spatial autocorrelation in the cities of China, the high–high significant clusters greatly increased, and the main locations changed from the northeast to southeast of China. Frequent obstacles were also found, including road infrastructure construction, water resources, and the green coverage of urban built-up areas. Based on these results, several policy implications were put forward, including the optimization of environmental laws and regulations, the development of green transportation infrastructure, resource conservation and the circular economy, the establishment of a green financial system, and increasing the linkage for the green transition of different cities.

Multi-objective modeling and evaluation for energy saving and high efficiency production oriented multidirectional turning considering energy, efficiency, economy and quality

To promote energy saving, high efficiency and quality production in the mechanical manufacturing industry, a large number of studies have been conducted on modelling and evaluation. However, most of the approaches have focused on single objective modelling, evaluation and conventional machining method like unidirectional turning (UDT). This paper presents a multi-objective modelling and evaluation method for energy, efficiency, economy and quality using a new process of forward-and-reverse multidirectional turning (MDT). Firstly, the multi-objective influential factors of cutting parameters for energy consumption, cutting time, production cost and surface roughness of MDT are analyzed to establish the corresponding models, respectively. Then taking the energy consumption, cutting time, production cost and surface roughness as the objectives, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied for the multi-objective evaluation through different processes. Finally, the case study shows the practicability of the MDT method and evaluation method for energy optimization and energy conservation.

Numerical simulation and multi-objective optimization of thin-walled aluminum/carbon fiber reinforced plastic hybrid tubes under axial crushing

Crushing behavior analysis and energy absorption optimization are crucial for lightweight structures in automotive applications. The present paper aims to investigate the crushing behavior of thin-walled aluminum/CFRP hybrid tubes under axial loading using an explicit finite element (FE) simulation. The damage constitutive models of aluminum and CFRP are implemented by coding the user-defined subroutine VUMAT in ABAQUS/Explicit, which includes the damage initiation and evolution laws and element deletion scheme. Parametric studies are conducted to assess the effects of radius and aluminum layer thickness on the crushing performance of hybrid tubes. Additionally, a multi-objective optimization is performed on the Isight platform using a non-dominant sorting genetic algorithm (NSGA-II) and technique for order preference by similarity to ideal solution (TOPSIS) with entropy weight method. The optimization aims to maximize crashworthiness and increase energy absorption capacity, enabling designers to select an optimum size ratio.

A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs.

The Multi-Point Relay (MPR) is one of the core technologies for Optimizing Link State Routing (OLSR) protocols, offering significant advantages in reducing network overhead, enhancing throughput, maintaining network scalability, and adaptability. However, due to the restriction that only MPR nodes can forward control messages in the network, the current evaluation criteria for selecting MPR nodes are relatively limited, making it challenging to flexibly choose MPR nodes based on current link states in dynamic networks. Therefore, the selection of MPR nodes is crucial in dynamic networks. To address issues such as unstable links, poor transmission accuracy, and lack of real-time performance caused by mobility in dynamic networks, we propose a comprehensive evaluation algorithm of MPR based on link-state awareness. This algorithm defines five state evaluation parameters from the perspectives of node mobility and load. Subsequently, we use the entropy weight method to determine weight coefficients and employing the method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for comprehensive evaluation to select MPR nodes. Finally, the Comprehensive Evaluation based on Link-state awareness of OLSR (CEL-OLSR) protocol is proposed, and simulated experiments are conducted using NS-3. The results indicate that, compared to PM-OLSR, ML-OLSR, LD-OLSR, and OLSR, CEL-OLSR significantly improves network performance in terms of packet delivery rate, average end-to-end delay, network throughput, and control overhead.

Comparison of TOPSIS Method and AHP Method in Determining Poor Households in Koto Baru Simalanggang

Every year, some social assistance is distributed by the Ministry of Social Affairs to each village/Nagari aimed at low-income families. The problem of poverty data almost occurs in all village/Nagari governments. In Koto Baru Simalanggang, several issues arose, including social assistance being mistargeted, poverty assessment being very complex, no system helped in DTKS, and no empowerment/system was carried out in reducing social problems; thus, all were put into DTKS without being able to see the details. Thus far, for the examination of the data of the proposer of assistance, the Nagari government still uses manual methods; therefore, it takes a long time and is subjective to the settlement of low-income families. Posdaya Bundo Saiyo is assisting in Koto Baru Simalanggang for the poor beneficiaries. There needs to be a decision support system to determine people with low incomes in this Nagari; using the method to determine people with low incomes is more objective. The results of this study are in the form of a decision support system for the determination of people with low incomes, which is used as a reference for the Bundo Saiyo Posdaya to make recommendations for the determination of people with low incomes to the Mayor of Koto Baru Simalanggang using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchy Process (AHP) methods. Based on the comparisons made in this research, the AHP and TOPSIS methods have the same ranking results

Resilience level assessment of regional water resources system based on multi-link

As environmental and human changes combine, water shortage and ecological fragility are becoming increasingly serious issues in the basin. The national “14th Five-Year Plan” proposes to build resilient basins to enhance disaster resilience. On the basis of clarifying the multi-link characteristics of water resources system resilience under the influence of meteorology-hydrology-ecology-society-economy, this paper reveals the interrelationship and chain reaction among the factors affecting the resilience of water resources system by clarifying the multi-link characteristics of water resources system resilience. Based on the interconnections among meteorology-hydrology-engineering-economic society-ecology in regional water resources systems, this study constructs a resilience evaluation index for the water resources system. The index considers different objectives, dimensions, and scales to provide a comprehensive assessment of the system’s resilience. Based on the multi-dimensional relationship of the evaluation index system, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method based on entropy weight method is employed to depict the resilience level of the water resources system at varying spatial and temporal scales. By focusing on Shanxi Province as the research subject, a multi-link evaluation framework for assessing the resilience of the water resources system is established, and a quantitative approach to evaluate its resilience is investigated. The results show that the level of water resources resilience in Shanxi Province has improved from 2017 to 2021, and there are certain spatial and temporal differences. In 2021, only Lvliang City and Yangquan City have low resilience level, and the resilience level of other cities has been improved. Among them, Taiyuan City has the highest resilience level of 0.56, which belongs to the high resilience level. The research results can improve the cognitive level of multi-link evolution mechanism of resilience to a certain extent, and provide scientific basis for multi-dimensional targeted regulation of water resources system resilience.

Network reliability evaluation of a supply chain under supplier sustainability

The United Nations’ sustainable development goals have brought attention to the pressing issue of enhancing supply chain sustainability and ensuring equitable order allocation. In contrast to the previous research, this study involves sustainability in network reliability evaluation. A new algorithm is proposed for computing network reliability, the probability that the current supply chain not only meets demand, budget, and suppliers’ production capacity but also considers the level of supplier sustainability. In the constructed multistate supply chain network (MSCN), a node represents a supplier, warehouse, assembler, or market, and a route connecting the nodes represents a carrier. The capacity of each carrier, represented by the available delivery containers, should not be deterministic as they may be partially reserved by other markets. Grounded in the Method Based on the Removal Effects of Criteria (MEREC) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the supplier sustainability can be assessed, facilitating the selection of sustainable suppliers and the determination of feasible order allocations, which are essential for various industries. An audio production cooperation is used as a case study to demonstrate the utility of the proposed algorithm.

An ICN-Based On-Path Computing Resource Scheduling Architecture with User Preference Awareness for Computing Network

The Computing Network is an emerging network paradigm that aims to realize computing resource scheduling through the intrinsic capabilities of the network. However, existing resource scheduling architectures based on conventional TCP/IP networks for the Computing Network suffer from deficiencies in routing flexibility and a lack of user preference awareness, while Information-Centric Networking (ICN) holds the potential to address these issues. ICN inherently supports dynamic routing in scenarios such as multi-homing and mobility due to its routing mechanism that is based on content names rather than host addresses, and it is further enhanced by the integration with Software-Defined Networking (SDN) technologies, which facilitate convenient network-layer route readdressing, thus offering a conducive environment for flexible routing scheduling. Furthermore, ICN introduces novel routing protocols that, compared with the more rigid protocol designs in conventional TCP/IP networks, offer greater flexibility in field usage. This flexibility allows for the incorporation of customized fields, such as “preference”, enabling the perception of user preferences within the network. Therefore, this paper proposes a novel ICN-based on-path computing resource scheduling architecture named IPCRSA. Within this architecture, an original design for computing resource request packet format is developed based on the IPv6 extension header. Additionally, preference-based computing resource scheduling strategies are incorporated, which employ the technique for order preference by similarity to ideal solution (TOPSIS) combined with the entropy weight method, to comprehensively evaluate computing resource nodes and use a roulette-selection algorithm to accomplish the probability selection of destination nodes. Experimental results indicate that, in comparison to alternative scheduling schemes, IPCRSA exhibits significant advantages in enhancing scheduling flexibility, improving scheduling success rates, and catering to diverse user requirements.

Recommendation of Machine Learning Techniques for Software Effort Estimation using Multi-Criteria Decision Making

 For the development of the software industry, Software Effort Estimation (SEE) is one of the essential tasks. Project managers can overcome budget and time overrun issues by accurately estimating a software project's development effort in the software life cycle. In prior studies, a variety of machine learning methods for SEE modeling were applied. The outcomes for various performance or accuracy measures are inconclusive. Therefore, a mechanism for assessing machine learning approaches for SEE modeling in the context of several contradictory accuracy measures is desperately needed. This study addresses selecting the most appropriate machine learning technique for SEE modeling as a Multi-Criteria Decision Making (MCDM) problem. The machine learning techniques are selected through a novel approach based on MCDM. In the proposed approach, three MCDM methods- Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were applied to determine the ranking of machine learning techniques on SEE performance based on multiple conflicting accuracy measures. For validating the proposed method, an experimental study was conducted over three SEE datasets using ten machine-learning techniques and six performance measures. Based on MCDM rankings, Random Forest, Support Vector Regression, and Kstar are recommended as the most appropriate machine learning techniques for SEE modeling. The results show how effectively the suggested MCDM-based approach can be used to recommend the appropriate machine learning technique for SEE modeling while considering various competing accuracy or performance measures altogether.

A New Multi-Target Three-Way Threat Assessment Method with Heterogeneous Information and Attribute Relevance

Target threat assessment provides support for combat decision making. The multi-target threat assessment method based on a three-way decision can obtain threat classification while receiving threat ranking, thus avoiding the limitation of traditional two-way decisions. However, the heterogeneous situation information, attribute relevance, and adaptive information processing needs in complex battlefield environment bring challenges to existing methods. Therefore, this paper proposes a new multi-target three-way threat assessment method with heterogeneous information and attribute relevance. Firstly, dynamic assessment information is represented by heterogeneous information, and attribute weights are calculated by heterogeneous Criteria Importance Through Intercriteria Correlation (CRITIC). Then, the conditional probability is calculated by the heterogeneous weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the adaptive risk avoidance coefficients are constructed by calculating the uncertainty of the assessment value, and then the relative loss function matrices are constructed. Finally, the comprehensive loss function matrices are obtained by the weighted Heronian mean (HM) operator, and the comprehensive thresholds are calculated to obtain the three-way rules. The case study shows that compared with the existing methods, the proposed method can effectively handle the heterogeneous information and attribute relevance, and obtain the risk avoidance coefficients without presetting or field subjective settings, which is more suitable for the complex mission environment.

Optimizing irrigation schedules of greenhouse tomato based on a comprehensive evaluation model

In the context of climate change, water availability has emerged as a pressing concern in agriculture, indicating the urgency to enhance irrigation management. Nevertheless, accurately quantifying irrigation schedules in diverse climates and soil textures remains a complex challenge. Here, a three-year irrigation experiment (2020–2022) of greenhouse tomato was conducted at three typical locations (Yulin, Yangling and Hanzhong) in northwest China. The irrigation schedules were determined by the cumulative evaporation (CE) measured from a 20 cm pan. A total nine treatments were set by combining three irrigation frequencies (IF) with three single doses (ID). Twenty indices related to tomato growth, photosynthesis, yield and water use, fruit quality, and environmental benefit were evaluated comprehensively by using TOPSIS (technique for order preference by similarity to ideal solution) and GRA (grey relational analysis) methods of combining weights based on game theory. The results showed that the indicator with highest weight was yield, followed by crop water productivity and vitamin C. The optimal ranges of irrigation frequency (irrigation dose) were determined as follows: 10.00–11.08 mm (0.81–0.90 CE) in Yulin, 13.12–15.68 mm (0.77–0.89 CE) in Yangling, and 11.44–16.40 mm (0.67–0.77 CE) in Hanzhong, respectively. This study introduces a method applied to prioritize the key indicators for irrigation scheduling, providing a reference for optimizing irrigation practices in greenhouse tomato production and improving water management in agriculture.

Assessment of Service Quality in Aviation Using the CRITIC-Supported TOPSIS Method: A Case Study of Central Airports in Asia

In this study, the service quality performance of 14 central airports that are members of SKYTRAX in the Asian continent are analyzed. The data of 2022 are taken as basis in the analysis process. The aim of the study is to evaluate the service quality of the airports included in the analysis. In order to compare the service quality performances of the selected airports, The Criteria Importance Trough Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods from Multi-Criteria Decision-Making Methods (MCDM) are used. The data of the airports included in the analysis are obtained through SKYTRAX. While the importance degrees of the criteria of the alternatives are calculated by the CRITIC method, the ranking of the alternatives is obtained by the TOPSIS method. The criteria used in the analysis are security screening, arrival, departure, terminal comfort, terminal facilities, food and beverage services. According to the findings of the CRITIC method; it is concluded that the most important criterion is departure and the least important criterion is terminal comfort. According to the results of the TOPSIS method; Singapore Changi Airport ranks first in terms of service quality performance. Seoul Incheon and Tokyo Haneda airports follow this airport in the ranking. Mumbai Chhatrapati Airport takes the last place.

Multi-criteria evaluation of CMIP6 precipitation and temperature simulations over Iran

Study regionIran. Study focusIn this study, the temporal-spatial performances of Global Climate Models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating precipitation and temperature over Iran are evaluated. The simulations of 37 CMIP6 GCMs are compared to the gauge-based observational data from the University of Delaware (UDEL) at the 0.5°×0.5° spatial resolution for 1985–2014. A multi-criteria statistical approach is used to evaluate the models. In this study, the evaluation criterion for GCMs is the degree of conformity between their outputs and UDEL data. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), is used to integrate statistical test results. New hydrological insights for the regionResults show that the ACCESS-CM2, BCC-CSM2-MR, and ACCESS-ESM1–5 models have superior performance over Iran. The Taylor diagram methodology was employed to assess the spatial agreement between GCMs and gridded observational data. The resulting correlation coefficients for rainfall and temperature variables from different models were (0.35–0.75) and (0.6–0.9), respectively, as determined by the Taylor diagram. The models that achieved higher ratings in the time performance evaluation phase, are also showed the better performance in the evaluation of their spatial pattern.