Evaluation of soil erosion vulnerability in Hubei Province of China using RUSLE model and combination weighting method

Soil erosion has been recognized as a critical environmental issue worldwide.While previous studies have primarily focused on watershed-scale soil erosion vulnerability from a natural factor perspective,there is a notable gap in understanding the intricate interplay between natural and socio-economic factors,especially in the context of spatial heterogeneity and nonlinear impacts of human-land interactions.To address this,our study evaluates the soil erosion vulnerability at a provincial scale,taking Hubei Province as a case study to explore the combined effects of natural and socio-economic factors.We developed an evaluation index system based on 15 indicators of soil erosion vulnerability:exposure,sensitivity,and adaptability.In addition,the combination weighting method was applied to determine index weights,and the spatial interaction was analyzed using spatial autocorrelation,geographical temporally weighted regression and geographical detector.The results showed an overall decreasing soil erosion intensity in Hubei Province during 2000 and 2020.The soil erosion vulnerability increased before 2000 and then.The areas with high soil erosion vulnerability were mainly confined in the central and southern regions of Hubei Province(Xiantao,Tianmen,Qianjiang and Ezhou)with obvious spatial aggregation that intensified over time.Natural factors(habitat quality index)had negative impacts on soil erosion vulnerability,whereas socio-economic factors(population density)showed substantial spatial variability in their influences.There was a positive correlation between soil erosion vulnerability and erosion intensity,with the correlation coefficients ranging from-0.41 and 0.93.The increase of slope was found to enhance the positive correlation between soil erosion vulnerability and intensity.

Application of combination weighting method in contract risk's evaluation of third party logistics

First,the analytical hierarchy process(AHP),which stands for the subjective weighting method,and the entropy method,which stands for the objective weighting method,are chosen to calculate the index weights of the contract risks of third party logistics(TPL),respectively.Then,they can determine the combination weights using the combination weighting method.Second,using the combination weights,the contract risks of TPL are evaluated through the fuzzy comprehensive evaluation method.According to the combination weights,the most important risk factor of the contract risks of TPL is choosing sub-contractors.The results are basically consistent with the facts and show that the weights determined by the combination weighting method can avoid the man-made deviations of the subjective weighting method on the one hand,and prevent results opposite to the reality brought about by the objective weighting method on the other hand.Meanwhile,the results of the fuzzy comprehensive evaluation are that the contract risks of TPL are at a high risk level.Roughly this matches real situations,and it indicates that the combination weighting method can generate the comprehensive assessment more scientifically and more reasonably as well.

Fracture propagation and evolution law of indirect fracturing in the roof of broken soft coal seams

Indirect fracturing in the roof of broken soft coal seams has been demonstrated to be a feasible technology.In this work,the No.5 coal seam in the Hancheng block was taken as the research object.Based on the findings of true triaxial hydraulic fracturing experiments and field pilot under this technology and the cohesive element method,a 3D numerical model of indirect fracturing in the roof of broken soft coal seams was established,the fracture morphology propagation and evolution law under different conditions was investigated,and analysis of main controlling factors of fracture parameters was conducted with the combination weight method,which was based on grey incidence,analytic hierarchy process and entropy weight method.The results show that“士”-shaped fractures,T-shaped fractures,cross fractures,H-shaped fractures,and“干”-shaped fractures dominated by horizontal fractures were formed.Different parameter combinations can form different fracture morphologies.When the coal seam permeability is lower and the minimum horizontal principal stress difference between layers and fracturing fluid injection rate are both larger,it tends to form“士”-shaped fractures.When the coal seam permeability and minimum horizontal principal stress between layers and perforation position are moderate,cross fractures are easily generated.Different fracture parameters have different main controlling factors.Engineering factors of perforation location,fracturing fluid injection rate and viscosity are the dominant factors of hydraulic fracture shape parameters.This study can provide a reference for the design of indirect fracturing in the roof of broken soft coal seams.

Comprehensive evaluation of the transformer oil-paper insulation state based on RF-combination weighting and an improved TOPSIS method

The accurate identification of the oil-paper insulation state of a transformer is crucial for most maintenance strategies.This paper presents a multi-feature comprehensive evaluation model based on combination weighting and an improved technique for order of preference by similarity to ideal solution(TOPSIS)method to perform an objective and scientific evaluation of the transformer oil-paper insulation state.Firstly,multiple aging features are extracted from the recovery voltage polarization spectrum and the extended Debye equivalent circuit owing to the limitations of using a single feature for evaluation.A standard evaluation index system is then established by using the collected time-domain dielectric spectrum data.Secondly,this study implements the per-unit value concept to integrate the dimension of the index matrix and calculates the objective weight by using the random forest algorithm.Furthermore,it combines the weighting model to overcome the drawbacks of the single weighting method by using the indicators and considering the subjective experience of experts and the random forest algorithm.Lastly,the enhanced TOPSIS approach is used to determine the insulation quality of an oil-paper transformer.A verification example demonstrates that the evaluation model developed in this study can efficiently and accurately diagnose the insulation status of transformers.Essentially,this study presents a novel approach for the assessment of transformer oil-paper insulation.

AModel for the Optimization of the Shale Gas HorizontalWell Section Based on the Combination of Different Weighting Methods in the Frame of the Game Theory

Existing“evaluation indicators”are selected and combined to build a model to support the optimization of shale gas horizontal wells.Towards this end,different“weighting methods”,including AHP and the so-called entropy method,are combined in the frame of the game theory.Using a relevant test case for the implementation of the model,it is shown that the horizontal section of the considered well is in the middle sweet spot area with good physical properties and fracturing ability.In comparison with the FSI(flow scanner Image)gas production profile,the new model seems to display better abilities for the optimization of horizontal wells.

The Optimal Weighted Combinational Forecasting with Constant Terms

We propose a model based on the optimal weighted combinational forecasting with constant terms, give formulae of the weights and the average errors as well as a relation of the model and the corresponding model without constant terms, and compare these models. Finally an example was given, which showed that the fitting precision has been enhanced.

Engineering Geological Zoning of Soft-Soil Foundation Based on Combination Weighting and Extension Methods

Different criteria and factors are used in different methods of soft soil foundation settlement calculation and engineering geological zoning.The methods used are not universally suitable for complex geological environments.The post-construction settlement of soft soil foundations are especially large and difficult to calculate.In addition,there are many deficiencies in the current methods used for engineering geological zoning.Focusing on the need of establishing engineering geological zoning for areas with soft soil foundations in the Tianjin Marine Economic Area,combination weighting and extension methods were introduced.An evaluation model for the settlement of soft soil foundations was established using multiple factors and large amounts of data.This evaluation model is accurate and objective for delineating engineering geological zoning.These methods eliminate deficiencies by considering both objective and subjective factors,and help obtain an objective and accurate result.

Optimizing selection of bridge raft based on fuzzy matter-element model and combination weighting

It＇ s a necessary selection to support the maneuver across Yangtze River by floating bridge constructed by portable steel bridge and civilian ships. It is a comprehensive index for the scheme of bridge raft, containing a variety of technical factors and uncertainties. The optimization is the selection in the constructing time, quantity of equipments and man power. Based on the calculation result of bridge rafts, an evaluating system is established, consisting of index of spacing between interior bays, raft length, truss numbers, operation difficulty and maximal bending stress. A fuzzy matter element model of optimizing selection of bridge rafts was built up by combining quantitative analysis with qualitative analysis. The method of combination weighting was used to calculate the value of weights index to reduce the subjective randomness. The sequence of schemes and the optimization resuh were gained finally based on euclid approach degree. The application result shows that it is simple and practical.

Evaluating the Derivative Value of Smart Grid Investment under Dual Carbon Target: A Hybrid Multi-Criteria Decision-Making Analysis

With the goal of“carbon peaking and carbon neutralization”,it is an inevitable trend for investing smart grid to promote the large-scale grid connection of renewable energy.Smart grid investment has a significant driving effect(derivative value),and evaluating this value can help to more accurately grasp the external effects of smart grid investment and support the realization of industrial linkage value with power grid investment as the core.Therefore,by analyzing the characterization of the derivative value of smart grid driven by investment,this paper constructs the evaluation index system of the derivative value of smart grid investment including 11 indicators.Then,the hybrid evaluation model of the derivative value of smart grid investment is developed based on anti-entropy weight(AEW),level based weight assessment(LBWA),and measurement alternatives and ranking according to the compromise solution(MARCOS)techniques.The results of case analysis show that for SG investment,the value of sustainable development can better reflect its derivative value,and when smart grid performs poorly in promoting renewable energy consumption,improving primary energy efficiency,and improving its own fault resistance,the driving force of its investment for future sustainable development will decline significantly,making the grid investment lack derivative value.In addition,smart grid investment needs to pay attention to the economy of investment,which is an important guarantee to ensure that the power grid has sufficient and stable sources of investment funds.Finally,compared with three comparison models,the proposed hybrid multi-criteria decision-making(MCDM)model can better improve the decision-making efficiency on the premise of ensuring robustness.

Investigation on the Long Term Operational Stability of Underground Energy Storage in Salt Rock

Underground energy storage is an important function of all energy supply systems,and especially concerning the seemingly eternal imbalance between production and demand.Salt rock underground energy storage,for one,is widely applied in both traditional and renewable energy fields;and this particular technique can be used to store natural gas,hydrogen,and compressed air.However,resource diversification and structural complexity make the supply system increasingly uncertain with the passing years,leading to great challenges for energy storage facilities in the present,and perhaps going into the future as well.Hence,it is necessary to research the operation stability of underground energy storage further.In this paper,a stability evaluation index system of Underground Gas Storage(UGS)is constructed with natural gas as the main medium,according to FLAC 3D cavity creep simulation software,along with fuzzy membership function to comprehensively determine the impact factor scoring model;the subjective weight is calculated based on the improved Analytic Hierarchy Process(AHP),the objective weight is calculated by the Entropy Weight Method(EWM),the combined constant weight is obtained by combining the variance maximization theory,and introducing the variable weight theory to obtain a more accurate combined variable weight.Finally,with this all being considered and accounted for,and with the four different conditions designed for UGS deployment case analysis and verification taken into consideration,the combined variable weight evaluation achieved excellent results;compared with the traditional constant weight method,in fact,the new evaluation results are more rigorous and objective.

Evaluation model of venture investment based on fuzzy matter-element method of combined weight

An evaluation model of an international venture investment project on the basis of fuzzy matter-element and combined weight methods is introduced. First, the compound fuzzy matter-element of optimal subordinate degree is constructed on the principle of the bigger-more-optimal or the less-more-optimal depending on the actual evaluation indicators, and combined with standard fuzzy matter-element to form a difference-square fuzzy matter-element. Secondly, a combined weight is calculated by both information entropy and the expert grading method. Finally, the compound fuzzy matter-element of Euclidian approach degree by M(·,+)method is constituted and used to classify venture investment projects. Based on the model above, six venture investment projects in a company are evaluated, and the results show that the projects are all good, which is demonstrated by the good income of the projects. Therefore, the coincidence of evaluation results and actual operation status indicates that the model is of great value in practical application.

Landslides susceptibility mapping in Guizhou province based on fuzzy theory

The purpose of this study was to assess the susceptibility of landslides around the area of Guizhou province based on fuzzy theory.In first instance, slope, elevation, lithology, proximity to tectonic lines, proximity to drainage and annual precipitation were taken as independent, causal factors in this study.A landslide hazard evaluation factor system was established by classifying these factors into more subclasses according to some rules.Secondly, a trapezoidal fuzzy number weighting(TFNW) approach was used to assess the importance of six causal factors to landslides in an ArcGIS environment.Thirdly, a landslide susceptibility map was created based on a weighted linear combination model.According to this susceptibility map, the study area was classified into four categories of landslide susceptibility:low, moderate, high and very high.Finally, in order to verify the results obtained, the susceptibility map and the landslide inventory map were combined in the GIS.In addition, the weighting procedure showed that TFNW is an efficient method for weighting causal landslide factors.

医院季度收入预测的比较研究

目的 探讨具有季度波动和长期趋势特征事物预测问题的方法论,为卫生领域同类预测问题提供定量决策方法论依据。方法 以某所医院季度收入预测作为实证算例载体,分别用乘积季节SARIMA法、X11-ARIMA法、X11-曲线函数外推法建立简单组合模型以及二次加权组合模型。结果 乘积季节SARIMA模型、X11-ARIMA模型、X11-二次多项式的平均相对误差分别为1.28%、0.96%、1.11%。对以上简单组合模型继续进行深度加权组合,预测误差为0.70%,模型预测性能更优。结论 SARIMA模型、X11-ARIMA模型、X11-二次多项式以及二次组合模型对于局部周期性波动、全局稳定性趋势的时序数据资料拟合效果不错;对于卫生管理领域预测问题有适用性,鉴于多种匹配方法原理不同、信息利用差异大,基于多种方法的二次组合模型取得更优性能。

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.

Land use suitability analysis for town development planning in Nanjing hilly areas: A case study of Tangshan new town, China

Land use suitability analysis plays an important role in sustainable land use and solving environmental problems caused by rapid urban development.A land use suitability mapping approach for town development planning in hilly areas was constructed based on two multi-criteria evaluation methods:Weighted Linear Combination(WLC)and Ordered Weighted Averaging(OWA),to comparatively evaluate and map land use suitability of Tangshan new town in Nanjing,China.Fourteen evaluation factors related to topographic,environmental,socio-economics and historical sites data were used as suitability criteria.The analytic hierarchy process(AHP)method and GIS techniques were integrated into the evaluation models to create the land use suitability map for town development planning.The results of WLC approach showed that 11.4%of the total area is highly suitable while the 48.6%is unsuitable.The results of WLC and OWA approach showed the distribution of degree of land use suitability is almost the same.The areas located at the southern and eastern flat regions are highly suitable for land use,whereas the areas close to the mountain forests,steep slopes,waters,and hot springs,have lower suitability for land use.Sensitivity analysis indicated that the suitability results of the two proposed methods are robust.Indirect validation was achieved by mutual comparison of suitability maps derived from the WLC and OWA methods.It demonstrated that the overall agreement is 90.81%and kappa coefficient is 0.81,indicating that both methods provide very similar spatial suitability distributions.By overlaying the resultant map with the previous master plan map of Tangshan new town,the overlay map once again indicated a satisfactory ecological fit between the two maps.At last,several recommendations are proposed aiming at improving the long-term town development plan for Tangshan new town.

Landfill Siting Using GIS and AHP （Analytical Hierarchy Process）： A Case Study AI-Qasim Qadhaa, Babylon, Iraq

The selection of a landfill site is considered as a complicated task because this process is based on many factors and restrictions. For Al-Qasim Qadhaa, which is situated in the southern part of the Babylon Governorate, Iraq, there is no landfill site in that area that conforms to the scientific criteria for selecting sites for landfill. For this reason, 15 criteria were adopted in this study （groundwater depth, rivers, soil types, agriculture lands use, land use, elevation, slope, gas pipelines, oil pipelines, power lines, roads, railways, urban centers, villages and archaeological sites） using GIS （geographic information system）, which has a large ability to manage input data. In addition, the AHP （analytical hierarchy process） method was used to derive the relative weightings for each criterion using pair-wise comparison. To obtain the suitability index for candidate landfill sites, a weighted linear combination method was used. After combining these methods, two suitable candidate landfill sites, with areas of 2.766 km2 and 2.055 km2, respectively, were found to satisfy the scientific and environmental requirements. The area of these sites can accommodate solid waste from 2020 until 2030 based on the required area, which was 0.702 km2.

Neural Network Compact Ensemble and Its Applications

There has been many methods in constructing neural network （NN） ensembles, where the method of simultaneous training has succeed in generalization performance and efficiency. But just like regular methods of constructing NN ensembles, it follows the two steps, first training component networks, and then combining them. As the two steps being independent, an assumption is used to facilitate interactions among NNs during the training stage. This paper presents a compact ensemble method which integrates the two steps of ensemble construction into one step by attempting to train individual NNs in an ensemble and weigh the individual members adaptively according to their individual performance in the same learning process. This provides an opportunity for the individual NNs to interact with each other based on their real contributions to the ensemble. The classification performance of NN compact ensemble （NNCE） was validated through some benchmark problems in machine learning, including Australian credit card assessment, pima Indians diabetes, heart disease, breast cancer and glass. Compared with other ensembles, the classification error rate of NNCE can be decreased by 0.45% to 68%. In addition, the NNCE was applied to fault diagnosis for rolling element bearing. The 11 time-domain statistical features are extracted as the properties of data, and the NNCE is employed to classify the data. With the results of several experiments, the compact ensemble method is shown to give good generalization performance. The compact ensemble method can recognize the different fault types and various fault degrees of the same fault type.

Sustainability Intelligent Evaluation of Regional Microgrid Interconnection System Based on Combination Entropy Weight Rank Order-TOPSIS and NILA-KELM

Sustainability evaluation of regional microgrid interconnection system is conducive to a profound and comprehensive understanding of the impact of interconnection system projects.In order to realize the comprehensive and scientific intelligent evaluation of the system,this paper proposes an evaluation model based on combination entropy weight rank order-technique for order preference by similarity to an ideal solution(TOPSIS)and Niche Immune Lion Algorithm-Extreme Learning Machine with Kernel(NILAKELM).Firstly,the sustainability evaluation indicator system of the regional microgrid interconnection system is constructed fromfour aspects of economic,environmental,social,and technical characteristics,and the evaluation indicators are explained.Then,the classical evaluationmodel based on TOPSIS is constructed,and the entropy weight method and rank order method(RO)are coupled to obtain the indicator weight.The niche immune algorithm is used to improve the lion algorithm,and the improved lion algorithm is used to optimize the parameters of KELM,and the intelligent evaluation model based on NILA-KELM is obtained to realize fast real-time calculation.Finally,the scientificity and accuracy of themodel proposed in this paper are verified.The model proposed in this paper has the lowest RMSE,MAE and RE values,indicating that its intelligent evaluation results are the most accurate.This study is conducive to the horizontal comparison of the overall performance of regional microgrid interconnection system projects,helps investors to choose the most promising project scheme,and helps the government to find feasible project.

Calculation of Water Pollution Control Performance Index:A Case Study of Beijing City

To explore water pollution control level in Beijing,performance evaluation index system of water pollution control in Beijing was constructed based on DPSR framework.Taking driving force,pressure,state and response as primary indexes,25 secondary indexes were established.The combined weighting of the index system was conducted by entropy weight method,coefficient of variation method and CRITIC method.The performance index of water pollution control in Beijing from 2010 to 2018 was calculated,and its dynamic change trend was analyzed.The results showed that the performance index of water pollution control in Beijing was low from 2010 to 2014,but increased year by year from 2015 to 2018,with a good trend.

A Weighted Combination Forecasting Model for Power Load Based on Forecasting Model Selection and Fuzzy Scale Joint Evaluation

To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.