Two Classes of Models of Granular Computing

This paper reviews a class of important models of granular computing which are induced by equivalence relations,or by general binary relations,or by neighborhood systems,and propose a class of models of granular computing which are induced by coverings of the given universe.

在简化的简单生物圈模式中结合新的雪盖模型(英文)

This paper describes a modified version of SSIB through implementing a new snow model (SAST) in Simplified Simple Biosphere Model SSIB for climate study and presents the evaluation results by testing the scheme based on the field data from Russia and France. The relevant equations in the scheme are given, which describe complicated interactive processes among air-vegetation-snow-soil continuum through mass and heat exchange. An efficient numerical scheme is developed to solve the nonlinear equations successfully. By using the field data from Russia and France, the function of the new scheme is evaluated. The numerical results from the scheme show good agreement with field data. It indicates that the scheme developed here is workable and can be extended for climate study. Key words Snow cover model (SAST) - SSIB - Implementing - Evaluation This work was supported by the foundation from China: 1)NSF Grant 49835010, 2) National key program G1998040900—Part 1, 3) NSF 40075019, 4) NSF 49823002.

有关雪盖模型内部及界面过程的参数化方案的敏感试验(英文)

In order to develop a seasonal snow model of land surface process as accurately as possible for climatic study. it is necessary to fully understand the effects of important snow internal processes and interaction with air and to get an insight into influence of several relevant parameterization schemes with parameters' uncertainty to some degree. Using the snow model (SAST) developed by first author and other one and some useful field observation data, this paper has conducted a series of sensitivity studies on the parameterization schemes. They are relative to compaction process, snow thermal conduction, methodology of layering snow pack and to key parameters such as snow albedo, water holding capacity. Then, based on the results from the sensitivity studies, some useful conclusions for snow cover model improvement are obtained from the analysis of the results.

An estimation method of soil wind erosion in Inner Mongolia of China based on geographic information system and remote sensing

Studies of wind erosion based on Geographic Information System（GIS） and Remote Sensing（RS） have not attracted sufficient attention because they are limited by natural and scientific factors.Few studies have been conducted to estimate the intensity of large-scale wind erosion in Inner Mongolia,China.In the present study,a new model based on five factors including the number of snow cover days,soil erodibility,aridity,vegetation index and wind field intensity was developed to quantitatively estimate the amount of wind erosion.The results showed that wind erosion widely existed in Inner Mongolia.It covers an area of approximately 90×104 km2,accounting for 80% of the study region.During 1985–2011,wind erosion has aggravated over the entire region of Inner Mongolia,which was indicated by enlarged zones of erosion at severe,intensive and mild levels.In Inner Mongolia,a distinct spatial differentiation of wind erosion intensity was noted.The distribution of change intensity exhibited a downward trend that decreased from severe increase in the southwest to mild decrease in the northeast of the region.Zones occupied by barren land or sparse vegetation showed the most severe erosion,followed by land occupied by open shrubbery.Grasslands would have the most dramatic potential for changes in the future because these areas showed the largest fluctuation range of change intensity.In addition,a significantly negative relation was noted between change intensity and land slope.The relation between soil type and change intensity differed with the content of Ca CO3 and the surface composition of sandy,loamy and clayey soils with particle sizes of 0–1 cm.The results have certain significance for understanding the mechanism and change process of wind erosion that has occurred during the study period.Therefore,the present study can provide a scientific basis for the prevention and treatment of wind erosion in Inner Mongolia.

Synergy of remotely sensed data in spatiotemporal dynamic modeling of the crop and cover management factor

Soil erosion is a threat to the water quality constituents of sediments and nutrients and can cause long-term environmental damages.One important parameter to quantify the risk of soil loss from erosion is the crop and cover management factor(C-factor),which represents how cropping and management practices affect the rates and potential risk of soil erosion.We developed remotely sensed data-driven models for dynamic predictions of C-factor by implementing dynamic land cover modeling using the SWAT(Soil and Water Assessment Tool)model on a watershed scale.The remotely sensed processed variables included the enhanced vegetation index(EVI),the fraction of photosynthetically active radiation absorbed by green vegetation(FPAR),leaf area index(LAI),soil available water content(AWC),slope gradient(SG),and ratio of area(AR)of every hydrologic response unit(HRU)to that of the total watershed,comprising unique land cover,soil type,and slope gradient characteristics within the Fish River catchment in Alabama,USA between 2001 and 2014.Linear regressions,spatial trend analysis,correlation matrices,forward stepwise multivariable regression(FSMR),and 2-fold cross-validation were conducted to evaluate whether there were possible associations between the C-factor and EVI with the successive addition of remotely sensed environmental factors.Based on the data analysis and modeling,we found a significant association between the C-factor and EVI with the synergy of the environmental factors FPAR,LAI,AWC,AR,and SG(predicted R^(2)(R^(2)_(pred))=0.51;R^(2)=0.68,n=3220,P<0.15).The results showed that the developed FSMR model constituting the non-conventional factors AWC(R^(2)_(pred)=0.32;R^(2)=0.48,n=3220,P<0.05)and FPAR(R^(2)_(pred)=0.13;R^(2)=0.28,n=3220,P=0.31)was an improved fit for the watershed C-factor.In conclusion,the union of dynamic variables related to vegetation(EVI,FPAR,and LAI),soil(AWC),and topography(AR and SG)can be utilized for spatiotemporal C-factor estimation and to monitor watershed erosion.

Evaluation of CMIP5 Earth System Models in Reproducing Leaf Area Index and Vegetation Cover over the Tibetan Plateau

The abilities of 12 earth system models（ESMs） from the Coupled Model Intercomparison Project Phase5（CMIP5） to reproduce satellite-derived vegetation biological variables over the Tibetan Plateau（TP） were examined.The results show that most of the models tend to overestimate the observed leaf area index（LAI）and vegetation carbon above the ground,with the possible reasons being overestimation of photosynthesis and precipitation.The model simulations show a consistent increasing trend with observed LAI over most of the TP during the reference period of 1986-2005,while they fail to reproduce the downward trend around the headstream of the Yellow River shown in the observation due to their coarse resolutions.Three of the models：CCSM4,CESM1-BGC,and NorESM1-ME,which share the same vegetation model,show some common strengths and weaknesses in their simulations according to our analysis.The model ensemble indicates a reasonable spatial distribution but overestimated land coverage,with a significant decreasing trend（-1.48%per decade） for tree coverage and a slight increasing trend（0.58%per decade） for bare ground during the period 1950-2005.No significant sign of variation is found for grass.To quantify the relative performance of the models in representing the observed mean state,seasonal cycle,and interannual variability,a model ranking method was performed with respect to simulated LAI.INMCM4,bcc-csm-1.1m,MPI-ESM-LR,IPSL CM5A-LR,HadGEM2-ES,and CCSM4 were ranked as the best six models in reproducing vegetation dynamics among the 12 models.

A New Prediction Model for Grain Yield in Northeast China Based on Spring North Atlantic Oscillation and Late-Winter Bering Sea Ice Cover

Accurate estimations of grain output in the agriculturally important region of Northeast China are of great strategic significance for guaranteeing food security.New prediction models for maize and rice yields are built in this paper based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index.The year-to-year increment is first forecasted and then the original yield value is obtained by adding the historical yield of the previous year.The multivariate linear prediction model of maize shows good predictive ability,with a low normalized root-mean-square error（NRMSE）of 13.9%,and the simulated yield accounts for 81%of the total variance of the observation.To improve the performance of the multivariate linear model,a combined forecasting model of rice is built by considering the weight of the predictors.The NRMSE of the model is 12.9%and the predicted rice yield explains 71%of the total variance.The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models.It is inferred that the statistical models established here by applying year-to-year increment approach could make rational prediction for the maize and rice yield in Northeast China before harvest.The present study may shed new light on yield prediction in advance by use of antecedent large-scale climate signals adequately.

A Coevolutionary Algorithm for Many-Objective Optimization Problems with Independent and Harmonious Objectives

Evolutionary algorithm is an effective strategy for solving many-objective optimization problems.At present,most evolutionary many-objective algorithms are designed for solving many-objective optimization problems where the objectives conflict with each other.In some cases,however,the objectives are not always in conflict.It consists of multiple independent objective subsets and the relationship between objectives is unknown in advance.The classical evolutionary many-objective algorithms may not be able to effectively solve such problems.Accordingly,we propose an objective set decomposition strategy based on the partial set covering model.It decomposes the objectives into a collection of objective subsets to preserve the nondominance relationship as much as possible.An optimization subproblem is defined on each objective subset.A coevolutionary algorithm is presented to optimize all subproblems simultaneously,in which a nondominance ranking is presented to interact information among these sub-populations.The proposed algorithm is compared with five popular many-objective evolutionary algorithms and four objective set decomposition based evolutionary algorithms on a series of test problems.Numerical experiments demonstrate that the proposed algorithm can achieve promising results for the many-objective optimization problems with independent and harmonious objectives.

基于SSP-RCP不同情景的京津冀地区土地覆被变化模拟

How to simulate land-cover change,driven by climate change and human activity,is not only a hot issue in the field of land-cover research but also in the field of sustainable urbanization.A surface-modeling method of land cover scenario(SSMLC)driven by the coupling of natural and human factors was developed to overcome limitations in existing land-cover models.Based on the climatic scenario data of CMIP6 SSP1-2.6,SSP2-4.5,and SSP5-8.5 released by IPCC in 2020,which combines shared socioeconomic paths(SSPs)with typical concentration paths(RCPs),observation climatic data concerning meteorological stations,the population,GDP,transportation data,land-cover data from 2020,and related policy refences,are used to simulate scenarios of land-cover change in the Jing-Jin-Ji region using SSP1-2.6,SSP2-4.5,and SSP5-8.5 for the years 2040,2070 and 2100,respectively.The simulation results show that the total accuracy of SSMLC in the Jing-Jin-Ji region attains 93.52%.The change intensity of land cover in the Jing-Jin-Ji region is the highest(plus 3.12%per decade)between 2020 and 2040,gradually decreasing after 2040.Built-up land has the fastest increasing rate(plus 5.07%per decade),and wetland has the fastest decreasing rate(minus 3.10%per decade)between 2020 and 2100.The change intensity of land cover under scenario SSP5-8.5 is the highest among the abovementioned three scenarios in the Jing-Jin-Ji region between 2020 and 2100.The impacts of GDP,population,transportation,and policies on land-cover change are generally greater than those on other land-cover types.The results indicate that the SSMLC method can be used to project the change trend and intensity of land cover under the different scenarios.This will help to optimize the spatial allocation and planning of land cover,and could be used to obtain key data for carrying out eco-environmental conservation measures in the Jing-Jin-Ji region in the future.

Multi-temporal analysis of past and future land cover change in the highly urbanized state of Selangor,Malaysia

Background:This study analysed the multi-temporal trend in land cover,and modelled a future scenario of land cover for the year 2030 in the highly urbanized state of Selangor,Malaysia.The study used a Decision Forest-Markov chain model in the land change modeller(LCM)tool of TerrSet software.Land cover maps of 1999,2006 and 2017 were classified into 5 classes,namely water,natural vegetation,agriculture,built-up land and cleared land.A simulated land cover map of 2017 was validated against the actual land cover map 2017.The Area Under the Curve(AUC)value of 0.84 of Total Operating Characteristics(TOC)and higher percentage of components of agreement(Hits+Correct rejection)compared to components of disagreement(Misses+False alarm+Wrong hits)indicated successful validation of the model.Results:The results showed between the years 1999 to 2017 there was an increase in built-up land cover of 608.8 km^(2)(7.5%),and agricultural land 285.5 km^(2)(3.5%),whereas natural vegetation decreased by 831.8 km^(2)(10.2%).The simulated land cover map of 2030 showed a continuation of this trend,where built-up area is estimated to increase by 723 km^(2)(8.9%),and agricultural land is estimated to increase by 57.2 km^(2)(0.7%),leading to a decrease of natural vegetation by 663.9 km^(2)(8.1%)for the period 2017 to 2030.The spatial trend of land cover change shows built-up areas mostly located in central Selangor where the highly urbanized and populated cities of Kuala Lumpur and Putrajaya and the Klang valley are located.Conclusion:The future land cover modelling indicates that built-up expansion mostly takes place at edges of existing urban boundaries.The results of this study can be used by policy makers,urban planners and other stakeholders for future decision making and city planning.

The abnormal electrostatic discharge of a no-connect metal cover in a ceramic packaging device

The human body model（HBM） stress of a no-connect metal cover is tested to obtain the characteristics of abnormal electrostatic discharge,including current waveforms and peak current under varied stress voltage and device failure voltage.A new discharge model called the ＂sparkover-induced model＂ is proposed based on the results.Then,failure mechanism analysis and model simulation are performed to prove that the transient peak current caused by a sparkover of low arc impedance will result in the devices＇ premature damage when the potential difference between the no-connect metal cover and the chip exceeds the threshold voltage of sparkover.