中国地质人才需求预测

The Requirement Forecast of Geological Talents in China Based on Advantage of Grey System TheoryLei YalinSchool of Humanities and Economic Management China University of GeosciencesBeijing, Chinaleiyalin@Zhong BingSchool of Humanities and Economic Management China University of GeosciencesBeijing, Chinazhong_bing@Abstract— A reasonable prediction of the demand for geological talents has become a pressing issue in order to meet the requirement of geological talent caused by resources shortage and environment deterioration, and prevent blindness in the higher education expansion. According to the survey of relevant literature, there wasn’t a recognized method of geological talents demand forecasting at present. In this paper, we constructed a grey prediction model of the demand for geological talents in China based on grey modeling theory and the related data from 2000 to 2005. Meanwhile, the reliability of grey forecasting model was verified that it had higher prediction accuracy in the field of the short-term forecast of the quantity of geological talent, and a set of proposals for further modification of the model were put forward.Keywords-Geological Talents; Requirement Forecast; Grey System TheoryI.INTRODUCTIONNowadays, China needs more geological talents because of resources exhaustion and environment deterioration. Facing this situation, some colleges and universities respond quickly, and expand the enrollment scale in the field of geosciences. According to the enrollment status of geological major in other countries, the enrollment scale is pretty larger in China. In this case, what is the appropriate scale? This requires a reasonable talent demand forecasting. Some experts in China Geological Survey suggest that the quantity of geological talents should be estimated in accordance with land area or the level of national economic development. Other experts recommend a detailed forecasting by traditional quantitative methods.At present, the traditional means of talent forecasting can be divided into two categories, namely, time series method and correlation analysis method. [1] These methods have a common characteristic of large sample and multi-data.Time series method arranges the historical data of talents in chronological order, analyzes the trend over time, and consequently infers the demand for talents in a certain period in future. One of the most common and simplest methods is trend extrapolation. This prediction method has been used to forecast the demand for geological talents. However, the geological environment has become increasingly complicated, and the technology develops so quickly, the reliability of this method is dubious.Correlation analysis studies statistical relationships between the quantity of talents and the factors of society, economy, education, and technology through historical data analysis, calculates the model parameters, and then predicts the talent demand according to identified socio-economic goals. Regression model is the most popular correlation analysis model. Regression analysis is perfectly logical and simple; meanwhile, the prediction accuracy is much higher. But sometimes it’s difficult to find the primary factors that impact on the dependent variable, or necessary statistical data even the primary factors are determined. For instance, we assumed that the geological workload was the primary factor that effected geological talents demand, while the progress in technology had a huge impact on talents demand. The extent of the impact would be difficult to measure by relevant statistical data. Furthermore, the regression model has a relatively poor ability to adapt to the change of environment.Therefore, we tried to explore a more reasonable prediction method with the requirement of less data. According to the survey of relevant literature, we attempt to apply grey system theory to the prediction of geological talents.II.ADVANTAGE OF GREY SYSTEM THEORYGrey system theory, abbreviated to GST, was pioneered by Professor Deng Julong in 1982. [2] Grey system (abbreviated to GS) refers to the system that partial information is known, while partial information is unknown. Grey system theory is about the analysis, modeling, forecasting, decision-making and controlling theory of grey system. This theory extends the views and methods of general system theory, information theory and cybernetics to the abstract system such as society, economy and ecosystem.Grey system theory is a link between social sciences and natural sciences. The analysis of GST can materialize and quantify the abstract system. [3] The main task of grey system modeling is to take full advantage of significant information and hidden information of limited data and seek for the mathematical relationships between factors according to the data of behavioral characteristic of grey system. Its essence is to transfer disordering original data into monotone increasing generating sequence by accumulated generating operation (abbreviated to AGO), which provided grey modeling with reasonable base data. The values calculated from the greySupported by Research Fund of China Geological Survey Bureau andR&E Management Lab of China University of Geosciences, Beijing.978-1-4244-4639-1/09/$25.00 ©2009 IEEEmodel can generate predictive value through inverse accumulated generating operation which is also called accumulated subtracting operation. In geological talents system, the quantity and structure of talents and other information are known, but the flow of talent and the status of talents employment is unknown. Moreover, the geological talents system, which is influenced by the political, the level of economic development and technology, the social environment,geological natural conditions and other factors, is a typical graysystem. Therefore, we argue that gray prediction method can estimate the demand for geological talents more accurately in this paper.The methods of grey forecasting weaken the effect of random factors by AGO, and then discover the variation of the system from generating series to set up grey prediction model. The research object of grey forecasting is the uncertain system with “small samples and poor information”. [4]The differential equation model of grey theory is called grey model (abbreviated to GM). GM (1, N) expresses the first-order differential equation model with N variables. It’s suitable for the state model of the system and dynamic relational analysis of multivariable, while is not suitable for forecasting. Therefore, the grey model mentioned in this article refers to the first-order differential equation model with one variable.III. GREY FORECASTING MODELING The original series X (0) for the modeling is given as follow:())(,),2(),1()0()0()0()0(n x x x X"=.A. Class Ratio Test• Let σ(k) be the class ratio of the original series X (0) atpoint k.)()1()(k x k x k −=σ, k=1, 2, ..., n (1) •σ is the class ratio series of X (0), written as:)](,),3(),2([n σσσσ"=• According to (1), calculate the class ratio of X (0). •According to (2), check if the interval of σ is⎥⎦⎤⎢⎣⎡∈−1212,)(n n e e k σ.(2) •If the series don’t pass the class ratio test, it must process the original data by logarithmic transformation, or translation transformation.B. Generation of AGO Series •)1(X is said to be AGO series of X (0), written as:())(,),2(),1()1()1()1()1(n x x x X "=. • According to (3), generate the series X (1). )()1()()0()1()1(k x k x k x +−= (3) C. Calculation of Model Parameters According to (4), calculate model parameters by least square method.N T T T y B B B u a a1)(],[ˆ−== (4)⎥⎥⎥⎥⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎢⎢⎢⎢⎣⎡+−−+−+−=1 )]()1([211 )]3()2([211 )]2()1([21)1()1()1()1()1()1(n x n x x x x x B """, ⎥⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎢⎣⎡=)()3()2()0()0()0(n x x x y N #D. Differential Equation of GM (1, 1) • The differential equation model isu ax dk dx =+)1()1(. (5)•The solution of the equation isa u e a u x k x ak +⎦⎤⎢⎣⎡−=+−)1()1()0()1(. (6) •According to (7), calculate the predictive value of X (0)by inverse accumulated generating operation.)()1()1()1()1()0(k x k x k x −+=+. (7)E. Residual Error Test• Calculate the absolute error (8) and relative error (9) inorder to estimate the model accuracy.)(ˆ)0(k x is the solution of differential equation of GM (1, 1).)(ˆ)()()0()0()0(k xk x k −=ε (8)%100)()0()()0()(×=k k xk q ε (9)F. Grey Relational Analysis• Relational grade “r ” (10) used to describe therelational extent between original series and generated series.∑==nk k nr 1)(1η(10)as }max{}max{}min{)0()0()()0()0()(ερεερεη++=k k• ρ is the distinguishing coefficient, 0<ρ<1, usually, ρ=0.5.•When ρ=0.5, if r >0.6, then the accuracy of model is acceptable.G. Analysis of Posterior Variance• Equation (11) describes variance of the original series.∑=−=n k k x x n S 12)0()(21)(1,∑==n k k x n x 1)0()(1 (11)•Equation (12) describes variance of the residual error series.∑=−=nk k nS12)0()(22)(1εε,∑==n k k n 1)0()(1εε (12)•Equation (13) describes the ratio of posterior variance.12S S c =. (13)•Equation (14) describes the error frequency of greymodel.{}1)0()(6745.0S P k <−=εε. (14)•Generally speaking, if the residual error, relational grade and posterior variance of the model all pass the test (TABLE I), then the accuracy of the model is acceptable and the predicting value is reliable, otherwise the model should be revised.IV. CASE STUDY OF GREY FORECASTING MODEL We reckoned the quantity of geological talents in China byusing GM (1, 1) in order to estimate the reliability of themodel. We selected the statistical data of geological talents from 2000 to 2005 to keep the integrality and consistency of the data (TABLE II).TABLE I.ACCURACY GRADE TABLE OF GREY MODELAccuracy GradeGrade 1aGrade 2b Grade 3c Grade 4d P >0.95 >0.8 >0.7 ≤0.7 C<0.35 <0.5 <0.65 ≥0.65a. A model is in grade 1 that means the accuracy of the model is excellent.b. A model is in grade 2 that means the accuracy of the model is good.c. A model is in grade 3 that means the accuracy of the model is qualified. d. A model is in grade 4 that means the accuracy of the model is unqualified.TABLE II. DATA OF GEOLOGICAL TALENTS IN CHINANo.Year Geological Talents 12000 258713 2 2001 240148 3 2002 221524 4 2003 209398 5 2004 224978 62005 234582Source: China Land and Resources Statistic Yearbook, 2006.Based on the statistical data in table II, the original series is given as follow:)0(X =(258713，240148，221524，209398，224978，234582)A. Class Ratio TestWe should do the class ratio test of the original data which is listed in table II before grey modeling. According to (1), the class ratio series is)96.0,93.0,06.1,08.1,08.1(=σ.According to (2), []33.1,75.0)(∈k σ. That is to say, the data series of geological talents is suitable for GM (1, 1).B. Generation of Grey Forecasting Model According to (3), the AGO series of )1(X is)1(X = (258713，498861，720385，929783，1154761，1389343).According to (4), calculate semi-parameters and parameters of the model, that is:.a=0.0035，u =229036 u/a =64976947 According to (6), the grey forecasting model of geologicaltalents is6497694764718234)1(ˆ0035.0)1(+−=+−k e k xC. Residual Error Test of the ModelAccording to (8) ~ (10), estimate the model value, predicted value, residual error and relational grade of the forecasting model, as listed in TABLE III.TABLE III.ACCURACY GRADE TABLE OF GREY MODELYear K Original data Model value Predicted value Absolute errorRelativeerrorη(k)2000 1 258713 258713258713 0 0.00% 1 2001 2 240148 484831226118 14030 5.84% 0.352002 3 221524 710159225328 3804 1.72% 0.672003 4 209398 934699224541 15143 7.23% 0.332004 5 224978 1158455223756 1222 0.54% 0.862005 6 234582 1381430222974 11608 4.95% 0.39According to (10), the relational grade “r ” of the forecasting model is 0.6008 when ρ=0.5, so r >0.6, that is to say the forecasting model is acceptable and the accuracy of the model is satisfactory. Fig.1 describes the fitting of the model.Figure 1. Comparison diagram of original value and predicted valueD. Posterior Variance Test of the ModelAccording to (11) ~ (14), we estimate the posterior variance of the forecasting model. Variance of the original series is S 1=15574.39. Variance of the residual error series is S 2=6152.81. The ratio of posterior variance is C=0.39. The error frequency P is P=1, because εε−)0(=（7634，6396，3830，7509，6412，3974），0.6745S 1=10505 and all εε−)0( < 0.6745S 1.According to TABLE I, the forecasting model is in grade 2, that is to say, the accuracy of the model is good and the predicted value of the model is satisfactory.V.CONCLUSION AND DISCUSSIONA. Research ConclusionGrey system theory is more applicable to forecast the demand for geological talents on the basis of analysis of several prediction methods in this article. From the point of view of grey system, some disordered series of data are not elusive. On the contrary, the theory treats these random quantities as grey variables that variety in a certain range. The quantization foundation of grey system theory is generated series by AGO not the original series, so it breaks the limitations of probability statistics and the model value was not estimated from statistical rules by experience. According to comparison of the predicted value and original value, it’s clear that the error of the forecasting model is acceptable. Therefore, the application of grey system theory to the field of geological talents requirement forecasting is reliable.B. Discussion and SuggestionGrey forecasting has the advantage of small sample, convenient operation, and high accuracy of short-term prediction, so the method has been widely used in social, economic, ecological and other fields. The forecasting model of geological talent achieved satisfactory results, but there are also some limitations of the method. It is worth further research work in the next step. •The method of parameter estimation in grey model uses discrete form of equation, while the fitting and prediction of model use continuous form of equation. The data of talents presents as the form of discrete. Moreover, there are subtle differences between continuous form and discrete forms in the structure of equation. The model error caused by the differences can’t be neglected. How we can eliminate the error to overcome the deficiencies of grey model in talents prediction need further research.•Grey forecasting model has an advantage in evolution rule analysis of historical data, but it is deficient in exploring the relationships between variables. Therefore, it is reliable to predict the quantity of geological talent by using grey prediction model. If it needs to forecast the structure of geological talents, we should consider some other prediction methods. Various forecasting methods provide different useful information, and the prediction accuracy is also different.REFERENCES[1] Ye Qiang, “Study on Method for Predicting the Demands for MarineScience and Technology Talents”, Coastal Engineering, vol 25, pp. 77-85, March 2006.[2] Deng Julong, The Primary Methods of Grey System Theory, 2nd ed.,Wuhan: HUST Press, 2005, pp.2-85.[3] Deng Julong, Grey Forecasting and Grey Decision Making, Wuhan:HUST Press, 2002, pp.45-130.[4] Xu Rong; Cao Anzhao, “Demand prediction for Science andTechnology Talents based on the Grey System Theory”, Journal of Anhui University of Technology and Science, vol 21, pp.71-74, September 2006.。