Entropy Weight Method Research Articles

Research on the impact of digital economic development level on ecological environment

PurposeExploring the intrinsic connection between the ecological environment and the digital economy and empirically testing how the level of digital economic development affects the ecological environment. Using the entropy weighting method to analyze the weights of the indicators in the digital economic development level and ecological environment system to explore the factors that have the greatest impact on the ecological environment in the indicator system of the digital economic development level so as to deepen the theoretical understanding of the relationship between the level of development of the digital economy and the ecological environment. Explore the regional heterogeneity of the level of development of the digital economy to promote the healthy development of China’s ecological environment proving the difference in the level of development of the digital economy in the east west and central regions of China and the difference in the effect on the ecological environment.Design/methodology/approachBased on the panel data of 30 provinces in China from 2013 to 2021 this paper fits the index system of digital economy development level with three factors. A digital infrastructure digital industry and digital application combines environmental pollution and energy consumption to construct ecological environment indicators and explored the impact of digital economy development level on the ecological environment by using the entropy weight method and the random effect model.FindingsThe findings indicate that the degree of digital economic development has a positive and significant impact on promoting the healthy development of the ecological environment, in which the digital industry has the greatest impact on the ecological environment. Meanwhile, the improvement of industrial structure also has a positive effect on the improvement of the ecological environment, whereas the level of human capital inhibits the healthy development of the ecological environment, and the governmental support fails to effectively and significantly promote the improvement of the ecological environment. Furthermore, the empirical research indicates that the level of digital economy development has obvious regional heterogeneity on the healthy development of the ecological environment: the eastern and central regions have a significant effect, while the western region has a less significant effect.Originality/valueAlthough domestic and foreign scholars and experts have conducted sufficient studies on the ecological environment and the development level of digital economy respectively, there are few studies on the empirical analysis of the positive significance and regional heterogeneity of the impact of the development level of digital economy on the ecological environment, which can be supplemented and referred to in this study. At the same time, it also provides intellectual support for our country to achieve high-quality development of digital economy and efficient governance of ecological environment.

Optimizing Spatial Layout of Campsites for Self-Driving Tours in Xinjiang: A Study Based on Online Travel Blog Data

Within the economic and cultural context of Xinjiang, the tourism industry has rapidly developed as a strategic pillar of the national economy, with the self-driving tour market emerging prominently. However, the uneven spatial layout and insufficient service facilities of self-driving camps limit their development potential. This study aims to enhance the attractiveness of tourism in Xinjiang and improve the visitor service experience by constructing an evaluation system for the layout of self-driving camps based on online travel blog data, utilizing methods such as literature review, surveys, ArcGIS spatial analysis, and web text analysis. The Delphi method and entropy weight method were applied to determine the weights of the influencing factors. The findings reveal spatial imbalances in the layout of Xinjiang’s self-driving camps and propose eight preferred scenic areas for camp location. This study also suggests sustainable development strategies. These insights and recommendations aim to optimize the layout of self-driving camps, enhance the tourism experience, and promote the sustainable development of Xinjiang’s tourism industry.

Agricultural Land Suitability Assessment Based on GIS at the County Scale in Yibin, China

As agricultural land resources scarce, food security has emerged as a pressing global concern. Conducting a suitability assessment of agricultural land is crucial for agricultural resources and food security. To establish an evaluation index system, we incorporated land, water, climate, and society four dimensions of 16 indicators. And we incorporated Entropy Weight Method (EWM), an objective method, with integrated evaluation model to assess agricultural land suitability in Yibin. Based on actual agricultural production, areas of land of urban construction, rural settlement and mining and water are subtracted. The Findings found that agricultural land suitability in Yibin presented a significant spatial heterogeneity. Areas with suitability and relative suitability accounted for 57.61% of Yibin’s land areas, which suggest Yibin has an abundance of agricultural land resources. Regions moderately suitable predominantly lie in the western and southern areas, covering 32.22% of the land area. Generally suitable areas accounted for 10.03% of Yinbin’s land areas, spatial distribution of which is similar to moderately suitable regions. Moderately and generally areas are potential areas for agricultural production. And regions for unsuitable accounted for only 0.14% of land areas. Overall, results mentioned above indicate that Yibin's agricultural land resources are in good condition. Considered terrain and water sources, this study revealed that the spatial distribution of topography and annual precipitation in Yibin closely mirrored both suitable and relatively suitable regions.

A Decision Model for High School Students' Summer job Choices: a Study Combining Hierarchical Analysis and Entropy Power Methods

As summer vacation approaches, high school students face an annual opportunity: choosing a summer job. These job opportunities not only provide a way to earn money, but also help students explore their career interests, develop key skills, and prepare them for future educational and career paths. However, high school students often have difficulty in finding the right job. To solve this, we have constructed a decision model that combines the hierarchical analysis of hierarchy (AHP) and entropy weighting method, aiming to find the best match among the many choices for high school students. The problem is essentially a multi-criteria decision-making problem that involves a number of decision-making factors, which need to be taken into account, along with the job characteristics and the applicant's profile and preferences. In our research, summer jobs are evaluated on a total of eight criteria: income, safety risk, job purpose, working conditions, and work. The model takes into account the job characteristics and the user's profile/preferences, and is therefore able to recommend the best job that matches the user's profile/preferences. In this paper, the AHP is used to determine the relative importance of these criteria for each individual, while the entropy weighting method is used to determine the relative importance of these criteria from an objective perspective. The model works as expected by validating it against nineteen types of job and ten fictional characters. Not only can we recommend the best jobs with the highest scores, but we can also rank all candidate jobs according to the calculated priority, providing users with a wider range of job options.

Study on the Optimization and Experimental of Fly Ash and Sludge in Ecological Slope Protection Substrate

Study on the Optimization and Experimental of Fly Ash and Sludge in Ecological Slope Protection Substrate

Application of Research on Risk Assessment of Roadway Roof Falls Based on Combined Weight Matter Element Extension Model

Roof falls in coal mine roadways are the main causes of many casualties, shutdowns and production plan delays. To understand the relationship between the influencing factors of roadway roof fall accidents and the importance ranking of the accidents, we will reduce safety accidents in coal mines. To enable the timely prediction and control of roadway roof fall risks, based on the investigation of many roadway roof fall risk factors, 12 evaluation indexes such as the roadway roof rock thickness, geological conditions and roadway section shape were selected. An evaluation index system of roadway roof fall risks is constructed. A risk degree standard of roadway roof falls is proposed. The risk evaluation model of roadway roof falls was established by using the combination weight of the analytic hierarchy process (AHP), entropy weight method (EW) and matter element extension theory. According to the principle of the maximum membership degree, the risk degree of roadway roof falls is determined. Based on Java Web, a risk assessment system for roadway roof falls was developed. We name the system Multiple Weight-Material Element Web (MW-MEW). The MW-MEW system was used to evaluate the risk degree of roof falls in the C9 return airway of the Xingu Coal Mine. Compared with the evaluation results of the AHP matter element extension model, it is found that the evaluation results of the MW-MEW system are more in line with the actual engineering conditions. The successful application of the MW-MEW system will provide new avenues for the quantitative evaluation of roof fall risks in coal mine roadways.

Weighted three-way conflict analysis in multi-attribute decision-making perspective

In real life, multiple issues may have different weights in leading to conflicts. Some of the existing conflict analysis literature related to weight does not involve an explicit weight method, and some use common methods such as entropy weight method. This paper aims to design a reasonable weighting method for the inherent characteristics of conflict analysis. The main motivation is that the issues getting vast majority of support (or opposition) are assumed to be more likely to cause intense conflicts. Firstly, a novel weighting method using N-bounded symmetric concave reciprocal function is brought in. Some properties of the method such as duality, objectivity and symmetry are discussed. Then, by embedding the weights into the existing distance function a weighted three-way conflict analysis model is proposed. Next the thresholds of conflict, alliance and neutrality relations are given by decision-theoretic rough set theory. In addition, as a main characteristic of the model, the conflict distance between any two agents is determined by the overall conflict table rather than their own. Finally, the model of this paper is compared with the existing conflict analysis weight model. At the same time, a comparative simulation experiment was carried out on the weight change and the number of maximal coalitions. The new weighted conflict model proposed in this paper can effectively compensate for the limitations of existing weighted models, especially in three-valued information systems and multi-valued information systems. It can also theoretically expand the field of three-way conflict analysis.

Three-Dimensional Quality Assessment of Urban Underground Space Resource Based on Multiple Geological Environmental Factors

With the rapid advancement of urbanization, the development and utilization of urban underground space resource (UUSR) has become one of the dominant features. However, in certain areas, the development of UUSR may cause disasters and accidents, such as ground collapse, settlements, and tunnel water gushing. Geological environmental factors (GEFs) are recognized as the fundamental constraining factor of UUSR development. In this paper, quality based on GEFs is defined to assess the development difficulty degree of UUSR. A 3D assessment framework is proposed based on 3D geological modelling and the interval continuous mathematical model (ICMM). The subjective and objective joint weight method of analytic hierarchy process and entropy weight method (AHP–EWM) is utilized to determine the weight of each indicator. The quality index (QI) of each spatial node of the 3D geological model is calculated by the ICMM mathematical model. A case study conducted in the Jiangbei New District of Nanjing, China, serves as a demonstration of the UUSR assessment. The results clearly illustrate the 3D distribution characteristics of the quality in the study area, offering valuable insights for future 3D urban underground space planning.

Insurance Risk Assessment Model based on Game Theory-Combined Weighting-TOPSIS

With the increasingly severe climate change and intensifying severity of weather events and natural disasters, ensuring the sustainable development of the property insurance industry becomes particularly important. The policy distribution of insurance companies has become a key issue. This paper proposes a novel Game Theory-Combined Weighting-TOPSIS model, which weights indicators using The Entropy Weight Method and the CRITIC method, combines the weights of the former two through game-based combined weighting, and leverages the advantages of the TOPSIS method in handling multi-criteria decision-making problems. Among them, taking China as an example and applying the model, it is found that southeastern China and coastal areas have higher scores and are suitable for insurance. Through this approach, we can scientifically and reasonably assess the risk levels in different regions, provide decision support for property insurance companies to optimize policy distribution, and promote the healthy development of the insurance industry.

ARIMA-based Freight Forecasting and Network Optimization in E-commerce Logistics

Considering the proliferation of e-commerce platforms exemplified by Taobao and JD, the paradigm of online shopping has evolved into an integral facet of contemporary societal existence. To enhance network transport performance, it is essential to predict freight volume, assess existing capacity, and establish new sites to alleviate network pressure. This study focuses on a logistics transportation network, utilizing daily freight turnover data from January 1, 2021, to December 31, 2022. The ARIMA time series method is employed to forecast freight volumes at different sites within the e-commerce logistics network. Taking three logistics site route combinations, namely DC14→DC10, DC20→DC35, and DC25→DC62, as examples, two differencing operations are applied to meet the stationarity requirement of the ARIMA model. Autocorrelation and partial autocorrelation coefficients are used for preliminary model order determination. By minimizing the Akaike Information Criterion (AIC) value, an ARIMA (5) model is established. Single and multiple-step predictions are conducted, resulting in forecast curves for future freight volumes. Subsequently, an evaluation of the transportation network is performed, considering the importance of nodes and routes. The entropy weight method is applied to determine the weights of evaluation indicators. Importance indices for nodes and routes are calculated, leading to a ranking. For the assessment and selection of new site capabilities, Fisher's discriminant function is employed to classify different sites. The valuation level for site selection is determined, providing a scientific basis for the systematic choice of new sites.

Research on the correlation and distribution law of commodities

In the fresh supermarket, the supermarket will generally make replenishment plans according to the historical sales and demand of each commodity. There is a certain correlation between the sales volume of different commodities. It is crucial to deeply understand the distribution law and correlation relationship of commodity sales to optimize the replenishment strategy. In this paper, Spearman correlation coefficient analysis and entropy weight method-TOPSIS model are constructed to analyze the distribution law and correlation relationship of vegetable products and single product sales volume. First of all, this paper aggregates the sales volume of different vegetable categories and single items by month to get the average monthly sales volume. The average monthly sales data of each vegetable category was visualized, and the distribution pattern of each vegetable category was analyzed. The relation of vegetable categories was analyzed using Spearman's correlation coefficient. Then, for vegetable items, the TOPSIS model based on entropy weight method was constructed, and the 8 most representative items were found among 251 vegetable items. Finally, the average monthly sales data of the selected vegetable products was visualized, the distribution pattern of the sales of each vegetable product was analyzed and the correlation coefficient of Spearman was used to analyze the sales of different vegetables.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

PurposeIn order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.Design/methodology/approachFirst, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.FindingsThe cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/valueThis study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

Predicting regional sustainable development to enhance decision-making in brownfield redevelopment using machine learning algorithms

Brownfield redevelopment and land sustainable management have many interconnected aspects, which could be evaluated by regional development. Most contaminated sites in Taiwan often lack comprehensive consideration during remediation for land reuse planning; therefore, they are operated independently from regional development. Regional development is a dynamic process; however, there is a research gap in understanding how regional development can serve as an incentive for brownfield redevelopment. This study established a model for assessing regional sustainability using machine learning (ML-RSD), that model used principal component analysis (PCA) and support vector machine (SVM) to predict the comprehensive evaluation rating of regional development at the township level and to explore the trends of regional governance sustainability. The results of ML-RSD that are compared to commonly used methods (driver-pressure-state-impact-response and entropy weight method) were consistent. Indicators such as the protection of vulnerable groups (SDG1), food security (SDG2), good health and well-being (SDG3), inclusive and equitable quality education (SDG4), environmental protection (SDG6), accessible and reliable energy (SDG7), and sustainable economic growth (SDG8) were identified as the main factors affecting regional development. Additionally, the model achieved an accuracy value exceeding 80%. Additionally, the results showed that there was no significant correlation between the delisted site (completed remediation) and regional development in southern Taiwan. The model underscored the overall lower comprehensive sustainable values for regions affected by inadequate land rehabilitation governance (SDG15). The results present that brownfield redevelopment strategies should enhance their effectiveness in land adaptation and integrating brownfield redevelopment with regional sustainable development is essential for it to become a win–win solution.

Risk Assessment and Insurance Underwriting Decision Model: A Case Study of Gansu Province and Florida

Extreme weather is driving up economic losses and insurance costs, underscoring the urgency for enhanced disaster risk management and policy reform for sustainability. This study presents a novel Risk Assessment Model for analyzing insurance underwriting risk in specific regions, driven by the increasing economic losses and insurance costs due to extreme weather. The model integrates a comprehensive evaluation system with four primary and twelve secondary indices, applying the Entropy Weighting Method (EWH) and Analytic Hierarchy Process (AHP) to allocate weights and quantify risk (Q_unfavourable) effectively. It also adopts the As Low as Reasonably Practicable (ALARP) principle for setting underwriting decision thresholds, based on risk and profitability. Case studies from Gansu Province, China, and Florida, USA, validate the model's practicality and its potential to enhance insurers' underwriting strategies and policy decisions across varied regions, marking a significant improvement in underwriting risk management and strategic insurance policy formulation.

Research on the Application of Machine Learning Algorithms in Green Economy Indicator Modeling and Sustainable Development Evaluation

This study presents a comprehensive modeling and analysis framework for investigating the impact of green GDP on climate factors and its potential for promoting sustainable development. Our model utilizes the entropy weight method (EWM) and grey correlation analysis (GRA) to optimize and process data, while employing a BP neural network for model training. Results indicate that green GDP leads to smaller global climate damage compared to traditional GDP, indicating its potential to alleviate the global climate crisis and promote green development.Furthermore, we built a green GDP prediction model using LSTM to forecast future changes. From the perspective of economy and management, the results indicate that transitioning from GDP to green GDP initially leads to a decrease in the index, followed by an increase. This suggests that green GDP development has short-term drawbacks but long-term benefits, including reduced resource consumption, improved economic conditions, and contribution to sustainable development. Finally, we analyzed the learning rate, number of iterations, and thresholds of related parameters through Logistic binary classification. Results demonstrate that our model is relatively stable, highlighting how green GDP addresses ecological challenges while promoting economic prosperity. These findings underscore the urgency of adopting the concept of green GDP in the contemporary era for achieving sustainable development goals.Overall, this research provides critical insights on the potential benefits and feasibility of green GDP as a crucial tool for mitigating climate risks and enhancing global prosperity.

Development of the best investment strategy for insurance companies based on ARIMA-SVM

The purpose of this article is to develop the best investment strategy for insurance companies in the context of extreme weather, aiming to improve the profitability of insurance companies and reduce investment risks. This research focuses on Asia and North America, and constructs an ARIMA model to accurately predict the frequency of extreme weather in the next decade. At the same time, a comprehensive evaluation model was constructed by combining the entropy weight method to quantitatively evaluate the payment ability of local residents. On this basis, the SVM classification model was established to predict whether the insurance company should be insured in a certain place, and the performance of the model was deeply analyzed, and the recall rate and accuracy of the classification model were 0.93 and 0.94, respectively, indicating that the model had good reliability. After a series of rigorous model analysis and data validation, it was finally concluded that China in Asia is the best choice for insurance companies. This strategy not only helps insurers optimize resource allocation, but also achieves stable and sustainable development in the context of complex and volatile extreme weather.

The Impact of Digital Infrastructure Development on the Consumption Upgrade of the Population

Building new infrastructure, which is embodied in digital infrastructure, has a major influence on China's economy's quality of development while also having a major effect on the upgrading of citizens' consumption. This paper uses the entropy weight method to synthesize the composite index of residents' consumption upgrading and the composite index of digital infrastructure construction based on panel data from 31 Chinese provinces from 2011 to 2020. Then, it applies the two-way fixed effect model to study and analyze the relationship between residents' consumption upgrading and the development of digital infrastructure. The results of the study show that, firstly, the construction of digital infrastructure is conducive to enhancing residents' consumption upgrading, and passes the stability test and endogeneity test. Second, the mediating effect analysis shows that digital infrastructure construction promotes residents' consumption upgrading through industrial structure upgrading and increasing income level. Third, the promoting effect of digital infrastructure construction on residents' consumption upgrading is more significant for residents with lower levels of population aging, higher levels of innovation, and residents in the western region. Given this, it is recommended that digital infrastructure development is further developed to optimize the impact of digital infrastructure development on residents' consumption upgrading and to promote residents' consumption upgrading.

Prediction of the Development of China's New Energy Industry

In the past decade, China's new energy vehicle industry has developed rapidly due to the influence of national policies. In recent years, China has started planning to cease policy support for the new energy vehicle industry. Therefore, predicting the future development of China's new energy industry is of great significance for policy-making in China. This paper selects 10 factors influencing the development of new energy electric vehicles in the past ten years and 4 indicators reflecting the development of new energy electric vehicles. We combine the Entropy Weight Method with TOPSIS to score the development status of new energy electric vehicles based on 4 indicators. Then, we use Principal Component Analysis to reduce the dimensionality of the ten influencing factors to three principal components. To quantify these effects, Lasso regression is performed using 3 principal components to prevent overfitting. Therefore, this article obtained a model for predicting the development status of China's new energy vehicle industry, with a goodness of fit of over 0.98, indicating a good fit of the model. In order to predict the development score for the next decade, this paper first predict the data of ten factors for the next decade. The factors that remain unchanged are not treated. Factors that change the trend significantly are predicted using GM (1,1) and the forecast is good. For stationary time series, this paper collects data from nearly 20 years and trains on Long Short-Term Memory Networks. R2 = 0.891 and RMSE < 0.1, Therefore, the model can be used to predict data for the next 10 years. After obtaining various data, this paper obtains the development score of new energy electric vehicles in the next 10 years according to the regression equation. Finally, this paper concludes that the development and progress of China's new energy industry will slow down in the next decade, with a score increase of about 20%.

Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang

The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses on historical and potential snow disasters in the Qinghai–Tibetan Plateau (QTP) Region. Utilizing a long-time-series snow depth remote sensing dataset, we extracted six assessment indicators for historical snow disaster risk factors and potential snow disaster risk factors. We determined the weights of these six assessment indicators using the entropy weight method. Subsequently, we established a snow disaster assessment model to evaluate the grade distribution of snow disasters in the study area. This method can effectively solve the problem of the sparse data distribution of meteorological stations and reflect degrees of snow disaster risk on a large spatial scale. The findings reveal that areas with a relatively high snow disaster risk are primarily concentrated in the western part of the Ali Region, the central part of Chamdo, and near the border in Southern Xizang. Additionally, regions with a high frequency of snow disasters are predominantly located at the junction of Nagchu, Chamdo, and Nyingchi in the eastern part of Xizang. These results contribute valuable insights into the risk assessment of snow disasters and facilitate the development of effective strategies for disaster management in the region.

Comprehensive Evaluation of Resilience for Qinling Tunnel Group Operation Safety System Based on Combined Weighting and Cloud Model

An extensive network of tunnels has recently been constructed in the Qinling Mountains. Characterized by high and steep terrain, this network has led to frequent traffic accidents. To address this issue, this paper introduces the theory of resilience into the evaluation system of safety systems during the operation period of highway tunnel groups. Based on this, this paper establishes a resilience evaluation index system for the operation safety system of highway tunnel groups, including a human system, vehicle system, and road system. To address both qualitative and quantitative issues concerning the indicators, this paper employs the analytic hierarchy process (AHP) and entropy weight method to combine and assign weights to the resilience evaluation indicators. Subsequently, the cloud model method is utilized to quantify the level of resilience of the highway tunnel group safety system during the operation period. The study results unveiled the patterns of traffic accidents within the Qinling Tunnel Group from the perspectives of vehicle, road, and human factors. The final weight allocation reveals that the road system has the highest proportion, exerting the greatest influence as a primary level index. Moreover, by taking the Qinling Tunnel Group on the Xihan Expressway as an engineering example, the resilience level of the case project was analyzed and obtained. Proposals for enhancing resilience were put forth, taking into account the project’s unique attributes, encompassing adaptability, resistance, and recovery. Overall, this study validates the feasibility and reliability of the proposed method for assessing the resilience of highway networks, offering empirical support for transportation administrators in the implementation of resilience-enhancing strategies.