Improved Analytic Hierarchy Process Research Articles

Coal and gas protrusion risk evaluation based on cloud model and improved combination of assignment

The proposed study presents an enhanced combination weighting cloud model for accurate assessment of coal and gas outburst risks. Firstly, a comprehensive evaluation index system for coal and gas outburst risks is established, consisting of primary indicators such as coal rock properties and secondary indicators including 13 factors. Secondly, the improved Analytic Hierarchy Process (IAHP) based on the 3-scale method and the improved CRITIC based on indicator correlation weight determination method are employed to determine subjective and objective weights of evaluation indicators respectively. Additionally, the Lagrange multiplier method is introduced to fuse these weights in order to obtain optimal weights. Subsequently, a prominent danger assessment model is developed based on cloud theory. Finally, using a mine in Hebei Province as an example, the results obtained from IAHP combined with improved CRITIC weighting method are compared with those from traditional AHP method and AHP-CRITIC combination weighting method. The findings demonstrate that among all methods considered, IAHP combined with improved CRITIC exhibits superior performance in terms of distribution expectation Ex, entropy value En, and super entropy He within cloud digital features; thus indicating that the risk level of coal and gas outbursts in this particular mine can be classified as general risk. These evaluation results align well with actual observations thereby validating the effectiveness of this approach. Consequently, this constructed model enables rapid yet accurate determination of coal and gas outburst risks within mines.

Rock Burst Intensity-Grade Prediction Based on Comprehensive Weighting Method and Bayesian Optimization Algorithm–Improved-Support Vector Machine Model

In order to accurately judge the tendency of rock burst disaster and effectively guide the prevention and control of rock burst disaster, a rock burst intensity-grade prediction model based on the comprehensive weighting of prediction indicators and Bayesian optimization algorithm–improved-support vector machine (BOA-SVM) is proposed for the first time. According to the main factors affecting the occurrence and intensity of rock burst, the rock stress coefficient (σθ/σc), brittleness coefficient (σc/σt) and elastic energy index (Wet) are selected to construct the rock burst prediction indicator system. On the basis of the research of other scholars, according to the main performance and characteristics of rock burst, rock burst is divided into four intensity levels. The collected and sorted 120 sets of rock burst case data at home and abroad are taken as learning samples, and the T-SNE algorithm is used to perform dimensionality-reduction visualization processing on the sample data, visually display the distribution of samples of different grades, evaluate the representativeness of the sample data and prejudge the feasibility of the machine learning algorithm to distinguish different rock burst intensity levels. The combined improved analytic hierarchy process (IAHP) and Delphi method determine the subjective weight of the indicators; the combined entropy weight method and CRITIC method determine the objective weight of the indicator, and use the harmonic mean criterion of information theory to synthesize the subjective weight and objective weight of the indicator to obtain the comprehensive weight of the indicators. After weighted prediction indicators, a rock burst intensity-grade prediction model is constructed based on the support vector machine, and the hyperparameters of three types of support vector machines are improved by using the Bayesian optimization algorithm. Then, the prediction accuracy of different models is calculated by the random cross-validation method, and the feasibility and effectiveness of the rock burst intensity-grade prediction model is verified. In order to evaluate the generalization and engineering applicability of the proposed model, 20 groups of rock burst case data from the Maluping mine and Daxiangling tunnel are introduced to predict the rock burst intensity grade. The results show that the accuracy of the rock burst intensity-grade prediction model based on comprehensive weighting and BOA-SVM is as high as 93.30%, which is of higher accuracy and better effect than the ordinary model, and can provide warning information with a higher fault tolerance rate, which provides a new way of thinking for rock burst intensity-grade prediction.

Risk assessment for hurricane-induced pluvial flooding in urban areas using a GIS-based multi-criteria approach: A case study of Hurricane Harvey in Houston, USA

As one of the most destructive nature hazards, hurricane-induced flooding generates serious adverse impacts on populations, infrastructure, and the environment globally. In urban areas, complex characteristics such as high population and infrastructure densities increase flood disaster risks. Consequently, the assessment of flood risks is becoming increasingly important for understanding potential impacts on an urban area and proposing disaster risk mitigation strategies. After conducting a comprehensive literature review, this study finds that most urban flood risk assessments often overlook urban ecosystem elements, focusing more on social and economic aspects. Hence, the role of urban ecosystems cannot be fully understood. To address this gap, this study proposes a social-ecological systems (SES) flood risk assessment framework for urban areas. Based on this framework, a comprehensive list of indicators collected through a literature review is provided for urban flood risk assessments. A comparative study of flood risk during Hurricane Harvey (2017) in Houston, Texas, USA, is carried out using the improved analytic hierarchy process (IAHP) weighting method and the equal weighting method for indicator weighting. Results are then compared with the damage data of Hurricane Harvey published by the U.S. Federal Emergency Management Agency (FEMA). The analysis identifies that the western part of Houston had the highest flood risks, while the center of Houston was at lower flood risk. Comparisons between the results from the IAHP and equal weighting methods show that the latter produces a broader range of high flood risk areas than the former. This study also highlights the role of urban ecosystems in mitigating flood risks and advocates for more holistic, social-ecological assessments of flood risk. Such assessments could utilize the proposed framework and the indicator list but contextualize these to the specific urban area's contexts being investigated.

Evaluation and Application of Surrounding Rock Stability Based on an Improved Fuzzy Comprehensive Evaluation Method

Ensuring the stability of surrounding rock is crucial for the safety of underground engineering projects. In this study, an improved fuzzy comprehensive evaluation method is proposed to accurately predict the stability of surrounding rock. Five key factors, namely, rock quality designation, uniaxial compressive strength, integrality coefficient of the rock mass, strength coefficient of the structural surface, and groundwater seepage, are selected as evaluation indicators, and a five-grade evaluation system is established. An improved analytic hierarchy process (IAHP) is proposed to enhance the accuracy of the evaluation. Using interval numbers rather than real numbers in constructing an interval judgment matrix can better account for the subjective fuzziness and uncertainty of expert judgment. Subjective and objective weights are obtained through IAHP and coefficient of variation, and the comprehensive weight is calculated on the basis of game theory principles. In addition, trapezoidal and triangular membership functions are employed to determine the membership degree, and an improved fuzzy comprehensive evaluation model is constructed. The model is then used to determine the stability of the surrounding rock based on the improved criterion. It is applied to six samples from an actual underground project in China to validate its effectiveness. Results show that the proposed model accurately and effectively predicts the stability of surrounding rock, which aligns with the findings from field investigations. The proposed method provides a valuable reference for evaluating surrounding rock stability and controlling construction risks.

Quantitative Evaluation Method for the Structural Safety Status of Traditional Courtyard-Style Timber Buildings

ABSTRACT Traditional courtyard-style timber building is a type of typical architectural timber heritage with special structures, which is different from traditional timber building unit. This paper proposes a scientific and effective assessment method for the structural safety status of traditional courtyard-style timber buildings. Firstly, based on the Improved Analytic Hierarchy Process (IAHP) which introduces the calculation method of component importance, the quantitative weights of components in traditional courtyard-style timber buildings are obtained. Secondly, the structural safety status evaluation system, the evaluation indexes and the evaluation weights are established based on the consideration of the structural characteristics of courtyard-style timber buildings. Finally, through on-site investigation and structural analysis, the structural safety status of a typical traditional courtyard-style timber-frame building — the Chu Hsi’s Public Granary, is studied to provide a basis for the subsequent conservation of the building. In addition, a quantitative automated security assessment software for courtyard-style timber buildings is developed. The study results can provide a reference for the safety assessment and preventive conservation of traditional courtyard-style timber building.

Multi‐sensor data fusion for swine house environment evaluation and control

AbstractData fusion from various sensors can obtain more accurate and reliable results than a single monitoring sensor. The data of air quality in the swine breeding environment comes from various sensors; therefore, the data must be fused. In this study, statistical criteria, batch estimation theory, a fuzzy comprehensive evaluation (FCE) method, as well as an improved analytic hierarchy process (IAHP), were combined to provide accurate data for multi‐sensor data fusion modeling. To make a fair comparison, four statistical criteria were employed to process abnormal data gross errors. Besides, an improved batch estimation adaptive weighted fusion method was adopted to, respectively, integrate the data from temperature, humidity, and concentrations of ammonia (NH3), carbon dioxide (CO2), and hydrogen sulfide (H2S) of air quality at the data level. The predetermined weights were given using IAHP combined with the knowledge of experts. To decide the fuzzy membership values of each environmental parameter, we presented a method to establish fuzzy membership functions and devised a calculation method for related parameters. The gaussmf type membership function was employed to build a global fusion model that fits well with IAHP–FCE. The IAHP–FCE method was exploited to comprehensively integrate multi‐sensor data at the decision level. Experimental results show that the presented method can improve the data accuracy and provide decision results and scientific basis for the precision control of swine breeding environment.

Air quality monitoring of sow houses with the fuzzy comprehensive evaluation method

AbstractThe comfort of sow house environments is dependent on several aspects. Therefore, it is difficult to accurately assess and control the environment by applying a single factor. In this study, Shannon's entropy theory, the fuzzy comprehensive evaluation (FCE) method, as well as the improved analytic hierarchy process (IAHP) were combined to provide reasonable subjective and objective weights for environmental comfort evaluation modeling. In addition, an evaluation index system including five environmental factors was established. To decide the membership degree of each environmental factor, we proposed a method to establish fuzzy membership functions (MFs) and devised a calculation method for related parameters. Three basic types of MFs, including the trapezoidal membership function (trapmf), pi‐shaped membership function (pimf), and ridge membership function (ridgemf), were adopted to establish an evaluation model that fit well with the entropy‐IAHP‐FCE model. The proposed method was verified using real measurements of spatiotemporal data, and results show that multiple environmental parameters were successfully integrated into the FCE for sow house environment evaluation. The FCE method delivered higher correlation coefficients of 0.8785, 0.9512, and 0.9719 compared with the temperature‐humidity index, single‐factor evaluation, and least squares support vector regression methods. The FCE method enhances sensitivity and performs an effective evaluation of environmental comfort. It can be integrated into environmental control and early‐warning systems for environmental management optimization, thereby enhancing sow production and welfare.

An Integrated Multiattribute Group Decision-Making Approach for Risk Assessment in Aviation Emergency Rescue

An effective risk assessment approach for assessing the risk level of aviation emergency rescue is essential to aviation emergency management. Therefore, this study is aimed at extending a novel integrated multiattribute group decision-making (MAGDM) approach to conduct risk assessment in aviation emergency rescue. First, a combined improved Swiss cheese model and fault tree analysis method are used to build the index system for risk assessment. Next, the weight vector for risk assessment is calculated using the consensus-based improved analytic hierarchy process (IAHP). Then, based on the traditional technique for order preference by similarity to the ideal solution (TOPSIS) method, we improve the definition express of ideal solution and the standardized formula of indexes, thereby providing a consensus-based improved TOPSIS method to compute the evaluation matrix for risk assessment. Finally, the weight vector and evaluation matrix are combined to estimate the risk level of aviation emergency rescue. As aviation emergency rescue in a sudden natural disaster an example, we illustrate the feasibility of the proposed method and demonstrate its effectiveness by comparative analysis with some similar approaches. The proposed approach can evaluate the safety status of aviation emergency rescue operations, which provides a starting point for the improvement of aviation emergency rescue.

Safety Evaluation of Subway Tunnel Construction under Extreme Rainfall Weather Conditions Based on Combination Weighting–Set Pair Analysis Model

Regional extreme rainfall events have occurred frequently in China, and subway tunnel construction faces possible threats under extreme weather conditions. Thus, in this study, we used the set pair analysis (SPA) approach to the construction safety evaluation of subway tunnels and developed a construction safety evaluation model under extreme rainfall circumstances. Firstly, based on careful consideration of the complex construction environment of subway tunnels under extreme rainfall weather conditions, a construction safety evaluation system of subway tunnels was developed considering four aspects: rainfall, hydrogeology, construction design, and management. Moreover, the weighting analysis of each index factor was carried out using the improved analytic hierarchy process (IAHP) method, the entropy weight method (EWM), and the linear weighting method. Secondly, considering the uncertainty of subway tunnels’ construction safety evaluation system and the fuzzy nature of evaluation-level classification, a construction safety evaluation system of subway tunnels based on the multivariate linkage number and set pair analysis theory was established. Finally, we applied the model to a subway tunnel construction case. The results show that the evaluation results are consistent with the actual engineering survey results, which verifies the practicality and effectiveness of the model in evaluating subway tunnel safety. We also determined the primary factors and risk development trends that affect the safety of subway tunnel construction under extreme rainfall weather conditions to guide the safety risk management of subway tunnel construction.

Risk Evaluation of Navigation Environment Based on Dynamic Weight Model and Its Application

In order to scientifically and reasonably evaluate the safety risk of ship navigation, to and better solve the problems of the poor sensitivity of static evaluation and insufficient ability to grasp the overall dynamic situation, in this paper, representative safety evaluation indexes for ship navigation are screened and the initial weight of each index is confirmed in combination with the improved analytic hierarchy process (IAHP), in order to learn the changes of navigation environment and accident samples in the waters in the jurisdiction. Finally, the dynamic risk evaluation is carried out by constructing a dynamic weight evaluation model for the safety risk of the navigation environment based on the weight fusion of subjective and objective impact factors. The waters under the jurisdiction of Sanya, China were selected for the study, and the navigation risk of the waters in the jurisdiction was calculated by using the dynamic weight evaluation model based on navigation risk. The calculation results are highly consistent with the results based on the statistics of historical accidents and the analysis of the characteristics of the navigation environment in the jurisdiction. The navigation risk in this water area is the greatest from May to September every year. The dynamic weight evaluation model can not only overcome the subjective evaluation distortion in the traditional risk evaluation theory of navigation environment in practical applications, but can also provide a scientific theoretical basis for the dynamic evaluation and early warning of the risk of ship navigation environments through continuous sample learning.

Assessment of the environmental comfort of lactating sows via improved analytic hierarchy process and fuzzy comprehensive evaluation

Since there are many interacting influence factors of the comfortable degree of lactating sows, a method that combines improved analytic hierarchy process (IAHP) and fuzzy comprehensive evaluation (FCE) was introduced to conduct a quantitative evaluation of the comfortable degree. Besides, an evaluation index system was established, and the weights of different indicators were determined by using IAHP method, including temperature, relative humidity, concentrations of carbon dioxide (CO2), ammonia (NH3), hydrogen sulfide (H2S), and air speed. The construction method of fuzzy membership function and the calculation method of the parameters were proposed following the principle that the summation of membership degrees is equal to 1. Three basic types of membership functions (MFs), i.e., ridgemf, gaussmf, and trimf were used to build an evaluation model which fitted IAHP-FCE well. The proposed method was verified and applied based on the environmental data in different seasons obtained from a pig farm in Zhenjiang City, Jiangsu Province, China. It is demonstrated that the proposed IAHP-FCE model with various types of MFs has drawn a unique and consistent conclusion. Moreover, the IAHP-FCE model has a higher correlation coefficient of 0.874 compared with the single-factor evaluation (SFE) model. The IAHP-FCE model could be served as a beneficial strategy for the precise regulation and early warning of environmental conditions to improve sow welfare. Keywords: lactating sow house, environmental comfort, analytic hierarchy process, fuzzy comprehensive evaluation, assessment DOI: 10.25165/j.ijabe.20221502.6149 Citation: Chen C, Liu X Q, Duan W Y, Liu C J. Assessment of the environmental comfort of lactating sows via improved analytic hierarchy process and fuzzy comprehensive evaluation. Int J Agric & Biol Eng, 2022; 15(2): 58–67.

A comprehensive evaluation method of electric power quality based on improved grey correlation analysis

Comprehensive evaluation of electric power quality is essential to the implementation of effective power quality control. In traditional evaluation methods, the weight of indicators is fixed and lacks flexibility. Aiming at this problem, this paper has proposed a power quality evaluation method based on improved grey correlation method. This method uses improved analytic hierarchy process (IAHP) to calculate subjective weights and introduces the improved entropy weight method (IEWM) that is modified using variable weight theory which calculates variable objective weights. The proposed method also uses intergrated weighting to combine the subjective and objective weights calculated above, obtaining multidimensional indicator weights. Finally, improved gray correlation analysis (IGCA), improved through employing ideal solution theory and logarithmic information aggregation method based on barrel theory, is implemented in this method to comprehensively evaluate electric power quality.

Quality evaluation of multi-path laser cladding coatings based on integrated fuzzy comprehensive evaluation and improved analytical hierarchy process method

Laser cladding processes have a wide range of applications for enhancement and surface modification of material characteristics. However, due to their coating formation's complexity, there is no practical method to value the cladding's quality. In this paper, a fuzzy comprehensive evaluation (FCE) model and the evaluation process are proposed. The quality evaluation index framework is demonstrated from the macroscopic profile, microstructure, and mechanical properties, and the assessment indexes are weighted by an improved analytic hierarchy process (IAHP) approach. Each indicator quantifies at four degrees. A stepwise affiliation function is used to calculate each indicator's affiliation at different degrees, then used to assess the project's quality level. This approach can help to improve surface quality analysis' reliability and optimize processing parameters.

Safety risk control in construction engineering based on the interval analytic hierarchy process and technique for order preference by similarity to ideal solution

AbstractTo improve the safety management level of construction projects, the housing construction project was taken as the research object and the hierarchical structure model by using the improved analytic hierarchy process (IAHP) was established. Based on the four dimensions of human, material, environment, management, the 12 influencing factor evaluation indexes and 34 scoring items were designed. By the redistribution of weight, the value range of scoring items was determined, and then the construction safety management evaluation system was constructed to evaluate the project safety management. When evaluating multiple projects, if the overall evaluation conclusion is poor, the technique for order preference by similarity to an ideal solution (TOPSIS) was used to evaluate the relative advantages and disadvantages of the project by introducing reference interval [0.5,0.6]. By calculating the membership relationship between nearness degree and reference interval , the decision‐making method to improve the level of project safety management was given. The results show that, if ,need external interactive learning, if , internal communication and learning can be organized. If ,the project safety management level can be improved by the combination of external interactive learning and internal experience exchange.

A Comprehensive Weight-Based Severity Evaluation Method of Voltage Sag in Distribution Networks

With the increasing use of sensitive loads in frequency converters and in relays in distribution networks, voltage sag has become a major power quality issue that urgently needs to be solved. For the purpose of improving the understanding of voltage sag severity in distribution networks, a comprehensive weight-based severity evaluation method of voltage sag is presented in this paper. First, a multi-side index system that takes into account the combined influence of the source, network, and the load is established. A comprehensive weight method, which combines the improved analytic hierarchy process (IAHP) and the entropy method, is then adopted to determine the index weight. The weight of each index and the degree of superiority are linearly weighted to obtain the severity of voltage sag at different observation points. Finally, the effectiveness of the proposed method is verified using a distribution network model established in DIgSILENT PowerFactory (15.1.7, Gomaringen, Germany).

Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system

According to the statistics of 160 typical fire and explosion accidents in oil storage areas at home and abroad nearly 50 years, 122 of them occurred the secondary accidents in the emergency responses. Based on 122 accident cases, 21 causal factors leading to secondary accidents are summarized. In order to quantify the influencing degree of these causal factors on the accident consequences, a multiple linear regression model was established between them. In the modeling process, these factors are decomposed into the criterion layer, variable layer, and bottom layer. The improved analytic hierarchy process (IAHP) was used to establish the relationship between the bottom factors and variable factors, and the regression analysis method was used to establish the relational model between variable layer and criterion layer. For 122 cases of the secondary accidents, this study took the year as a statistical dimension, and obtained 40 groups of sample data. The first 34 groups of sample data were used to build the causal factors model, and the last 6 groups of sample data were tested the generalization ability of the model by using the established regression model combined with grey prediction model. The results show that the prediction ability of the established model was better than that of the grey prediction model alone. Moreover, the relative contribution and change trend of the causal factors were evaluated using the mutation progression method, and corresponding preventive countermeasures were proposed. It was found that human professional skills, knowledge and literacy, environmental issues, and firefighting facilities are the main influencing factors that lead to the secondary accidents. These three kinds of factors show a gradual improvement trend, and the existing prevention measures should be maintained and further improved. The problem of inherent objects or equipment factors has not been effectively improved and has a worsening trend, which is the focus of prevention in the future, and the prevention and control efforts need to be moderately increased. The research results have important guiding significance for understanding the quantitative influences of causal factors on the accident consequences, improving emergency response capabilities, reducing accident losses, and avoiding secondary accidents.

Research on the Method of Predicting Fractional Flow Reserve Based on Multiple Independent Risk Factors.

The use of diameter stenosis (DS), as revealed by coronary angiography, for predicting fractional flow reserve (FFR) usually results in a high error rate of detection. In this study, we investigated a method for predicting FFR in patients with coronary stenosis based on multiple independent risk factors. The aim of the study was to improve the accuracy of detection. First, we searched the existing literature to identify multiple independent risk factors and then calculated the corresponding odds ratios. The improved analytic hierarchy process (IAHP) was then used to determine the weighted value of each independent risk factor, based on the corresponding odds ratio. Next, we developed a novel method, based on the top seven independent risk factors with the highest weighted values, to predict FFR. This model was then used to predict the FFR of 253 patients with coronary stenosis, and the results were then compared with previous methods (DS alone and a simplified scoring system). In addition to DS, we identified a range of other independent risk factors, with the highest weighted values, for predicting FFR, including gender, body mass index, location of stenosis, type of coronary artery distribution, left ventricular ejection fraction, and left myocardial mass. The area under the receiver-operating characteristic curve (AUC) for the newly developed method was 84.3% (95% CI: 79.2–89.4%), which was larger than 65.3% (95% CI: 61.5–69.1%) of DS alone and 74.8% (95% CI: 68.4–81.2%) of the existing simplified scoring system. The newly developed method, based on multiple independent risk factors, effectively improves the prediction accuracy for FFR.

Corrosion Risk Assessment Model of Gas Pipeline Based on Improved AHP and Its Engineering Application

The paper is aimed at establishing a corrosion risk assessment model for the buried gas pipeline. To realize the forecast of the gas pipeline corrosion damage risks, the adverse factors of the safe operation were classified, and the index evaluation system of possibility of gas pipeline failure was established based on five first-level indicators and six second-level indicators. According to the index evaluation system, improved analytic hierarchy process (I-AHP) method was used to determine the weight of each indicator, and then the fuzzy comprehensive evaluation was proposed based on improved AHP. The calculation and analysis were carried out, and the membership function of the fuzzy comprehensive evaluation suitable for gas pipeline corrosion risk assessment was obtained, which had improved the practicability. Taking the buried gas pipeline network of Suzhou Industrial Park, China, as an example, the assessment was carried out. Some buried gas pipelines were randomly selected for excavation to verify the degree of conformity between the observation results and the calculation results. The results show that: the evaluation results of this method are consistent with the field detection results. In addition, the evaluation model established in this paper can provide a reference for the establishment of accident database and the management for the pipeline operation enterprise.

Risk evaluation of mine-water inrush based on principal component logistic regression analysis and an improved analytic hierarchy process

A method of evaluating the risk of floor water inrush using principal component logistic regression analysis (PCLRA) and an improved analytic hierarchy process (IAHP) is presented. The approach was validated by a case study at the Yangcheng coal mine in Shandong Province, China. First, the risk assessment index of floor water inrush was determined based on geological and hydrogeological conditions of the study area. Then, a comprehensive evaluation model (CEM), combining PCLRA with IAHP, was constructed to determine the comprehensive weight of each water-inrush evaluation index. Finally, water-inrush risk zoning was determined with GIS. The results show that the CEM, compared with the water-inrush coefficient method (WICM) traditionally and commonly used in China, has higher fitting accuracy and more detailed division of water-inrush risk areas. This method not only follows the observations in reality, but also fully considers the relative importance of water-inrush evaluation indices. The results can provide a theoretical basis for the safe mining of coal seams above confined groundwater.

Evaluation and diagnosis of obstacles to land-based ecological security in resource-based cities: A case study of Xingtai city.

To provide a theoretical basis for sustainable land resource utilization and a reference for areas with similar natural conditions, an evaluation index for land-based ecological security was constructed based on the Driving force-Pressure-State-Impact-Response (DPSIR) model and the improved analytic hierarchy process (IAHP) and entropy methods, and the land-based ecological security status of Xingtai city from 2006 to 2017 was evaluated. Then, the obstacles to land-based ecological security were diagnosed. The results show that the values of the comprehensive evaluation index of land-based ecological security were 0.28–0.66 in the period from 2006 to 2017. The value of the index of land-based ecological security was low in the first seven years and gradually improved in the last five years of the study period. However, the overall situation was grave, and the ecological security conditions were poor. The main obstacles to land-based ecological security were the usage of pesticides, investment in environmental pollution treatments, the degree of machine cultivation, the rate of cultivation and the usage of fertilizer in Xingtai city. Based on the results of the land-based ecological security evaluation and the main obstacles identified in Xingtai city, this paper proposes management strategies and suggestions for improving land-based ecological security in Xingtai city. The specific proposals are as follows: vigorously develop green agriculture, increase investment in environmental pollution control, increase input in science and technology, and strengthen supervision and management of land use.