CN116167486A - Drought prediction method and system based on ARIMA-regression model - Google Patents
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Abstract
The invention provides a drought prediction method and a system based on an ARIMA-regression model, which relate to the technical field of drought prediction and construct a time sequence of a region to be predicted according to drought data of the region to be predicted; obtaining an initial drought index at the next moment according to the time sequence and the ARIMAP, d and q model after the order setting; inputting the initial drought index into a drought index regression model to obtain a predicted drought index at the next moment; and determining the drought degree of the area to be predicted according to the predicted drought index. The ARIMAP, d and q models are subjected to order determination to predict drought data of a to-be-predicted area, and then a drought index regression model is utilized to correct a prediction result, so that the efficiency and the accuracy of drought prediction are improved.
Description
Technical Field
The invention relates to the technical field of drought prediction, in particular to a drought prediction method and a drought prediction system based on an ARIMA-regression model.
Background
At present, the loss evaluation and prediction of drought are mainly realized by obtaining data in the forms of field investigation, expert interviews and statistical data and combining qualitative and quantitative models (economic models such as input-output models, hydrologic economic models and the like, EPIC crop physiological mechanism models and risk loss degree models based on a risk evaluation principle).
However, current research is mainly in agriculture, including the farming and animal industries, and other aspects have less drought damage research, such as municipal domestic water, travel industry, and industry. In addition, the loss caused by drought on social economy and ecology lacks compared with the systematic quantitative research, and although a qualitative and quantitative evaluation combined method is adopted during evaluation and prediction, the quantitative evaluation model is mostly a statistical model, is influenced by factors such as regions, human factors and the like, has poor universality, and cannot evaluate and predict drought conditions in real time, efficiently and objectively.
Disclosure of Invention
The invention aims to provide a drought prediction method and a system based on an ARIMA-regression model, which can improve the accuracy and efficiency of drought prediction.
In order to achieve the above object, the present invention provides the following solutions:
an ARIMA-regression model-based drought prediction method, comprising:
constructing a time sequence of the area to be predicted according to drought data of the area to be predicted;
obtaining an initial drought index at the next moment according to the time sequence and the ARIMAP, d and q model after the order setting; parameters of the ARIMAP, d and q model after the order determination are determined according to the historical time sequence; the parameters comprise an autoregressive order p, a moving average order q and a differential order d;
inputting the initial drought index into a drought index regression model to obtain a predicted drought index at the next moment;
and determining the drought degree of the area to be predicted according to the predicted drought index.
Optionally, before the constructing the time sequence of the area to be predicted according to the drought data of the area to be predicted, the method further includes:
acquiring a historical time sequence of a region to be predicted; the elements in the time sequence are historical drought indexes of the area to be predicted;
performing stationarity processing on the historical time sequence by using a difference algorithm to obtain a difference order d and a stationarity processed historical time sequence;
respectively constructing a moving average model MA (q) and an autoregressive moving average model ARMA (p, q) according to the historical time sequence after the stable treatment;
fitting the moving average model MA (q) and the autoregressive moving average model ARMA (p, q) to obtain an ARIMA (p, d, q) model after the order determination.
Optionally, before the constructing the time sequence of the area to be predicted according to the drought data of the area to be predicted, the method further includes:
acquiring a plurality of historical initial drought indexes; the historical initial drought index is obtained according to a historical time sequence and an ARIMA (p, d, q) model after order determination;
and determining a drought index regression model by using a linear regression algorithm and a least square method by taking the historical initial drought index as an independent variable and the initial drought index as a dependent variable.
Optionally, the constructing a time sequence of the area to be predicted according to the drought data of the area to be predicted specifically includes:
determining any moment as the current moment;
constructing a current judgment matrix according to drought data of the current moment of the area to be predicted; the elements of the current judgment matrix are the importance degree between any two drought indexes; the drought index comprises drought population, drought farmland area, crop yield reduction, livestock loss and economic loss;
according to the current judgment matrix, the formula is utilized
Determining the weight of each index;
judging whether the current judgment matrix passes consistency test or not to obtain a judgment result;
if the judgment result is no, updating the current judgment matrix and returning to the step of utilizing a formula according to the current judgment matrix
Determining the weight of each index;
if the judgment result is yes, utilizing a formula according to the weight of each index
Determining a drought index at the current moment;
determining a drought index in a preset time period before the current moment as a time sequence of a region to be predicted;
wherein w is i The weight of the i index;
and->
The characteristic vector n of the ith index and the jth index is the total number of drought indexes; f (x) is drought index; f (x) i ) Is the actual measurement value of the i-th index.
A drought prediction system based on ARIMA-regression model, comprising:
the time sequence construction module is used for constructing a time sequence of the area to be predicted according to drought data of the area to be predicted;
the initial drought index determining module is used for obtaining an initial drought index at the next moment according to the time sequence and the ARIMA (p, d, q) model after the order setting; parameters of the fixed-order ARIMA (p, d, q) model are determined according to a historical time sequence; the parameters comprise an autoregressive order p, a moving average order q and a differential order d;
the predicted drought index determining module is used for inputting the initial drought index into a drought index regression model to obtain a predicted drought index at the next moment;
and the drought degree determining module is used for determining the drought degree of the area to be predicted according to the predicted drought index.
Optionally, the system further includes:
the historical time sequence acquisition module is used for acquiring a historical time sequence of the area to be predicted; the elements in the time sequence are historical drought indexes of the area to be predicted;
the stationarity processing module is used for carrying out stationarity processing on the historical time sequence by utilizing a difference algorithm to obtain a historical time sequence after the difference order d and the stationarity processing;
the sub-model building module is used for respectively building a moving average model MA (q) and an autoregressive moving average model ARMA (p, q) according to the historical time sequence after the stable treatment;
and the order determining module is used for fitting the moving average model MA (q) and the autoregressive moving average model ARMA (p, q) to obtain an ARIMA (p, d, q) model after order determination.
Optionally, the system further includes:
the historical initial drought index determining module is used for obtaining a plurality of historical initial drought indexes; the historical initial drought index is obtained according to a historical time sequence and an ARIMA (p, d, q) model after order determination;
and the drought index regression model determining module is used for determining a drought index regression model by using a linear regression algorithm and a least square method by taking the historical initial drought index as an independent variable and the initial drought index as a dependent variable.
Optionally, the time sequence construction module specifically includes:
the current time determining unit is used for determining any time as the current time;
the current judgment matrix construction unit is used for constructing a current judgment matrix according to drought data of the current moment of the area to be predicted; the elements of the current judgment matrix are the importance degree between any two drought indexes; the drought index comprises drought population, drought farmland area, crop yield reduction, livestock loss and economic loss;
the weight determining unit is used for utilizing a formula according to the current judgment matrix
Determining the weight of each index;
the consistency check unit is used for judging whether the current judgment matrix passes consistency check or not to obtain a judgment result; if the judgment result is negative, the current judgment matrix updating unit is called; if the judgment result is yes, calling a drought index calculation unit;
the current judgment matrix updating unit is used for updating the current judgment matrix and calling the weight determining unit;
a drought index calculation unit for using a formula according to the weight of each index
Determining a drought index at the current moment;
the time sequence construction unit of the area to be predicted is used for determining that the drought index in a preset time period before the current moment is the time sequence of the area to be predicted;
wherein w is i The weight of the i index; w (W) i And W is j The characteristic vector n of the ith index and the jth index is the total number of drought indexes; f (x) is drought index; f (x) i ) Is the actual measurement value of the i-th index.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a drought prediction method and a system based on an ARIMA-regression model.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a drought prediction method based on an ARIMA-regression model in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a drought prediction method and a system based on an ARIMA-regression model, which can improve the accuracy and efficiency of drought prediction.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in FIG. 1, the invention provides a drought prediction method based on an ARIMA-regression model, which comprises the following steps:
step 101: constructing a time sequence of the area to be predicted according to drought data of the area to be predicted;
step 102: obtaining an initial drought index at the next moment according to the time sequence and the ARIMAP, d and q model after the order determination; parameters of the ARIMAP, d and q model after the order determination are determined according to the historical time sequence; the parameters include an autoregressive order p, a moving average order q and a differential order d;
step 103: inputting the initial drought index into a drought index regression model to obtain a predicted drought index at the next moment;
step 104: and determining the drought degree of the area to be predicted according to the predicted drought index.
In addition, the drought prediction method based on ARIMA-regression model provided by the invention further comprises the following steps before step 101:
acquiring a historical time sequence of a region to be predicted; the elements in the time sequence are the historical drought indexes of the areas to be predicted;
carrying out stationarity treatment on the historical time sequence by utilizing a difference algorithm to obtain a difference order d and a stationarity treated historical time sequence;
respectively constructing a moving average model MA (q) and an autoregressive moving average model ARMA (p, q) according to the historical time sequence after the stable treatment;
fitting the moving average model MA (q) and the autoregressive moving average model ARMA (p, q) to obtain an ARIMA (p, d, q) model after the order determination.
Prior to step 101, further comprising:
acquiring a plurality of historical initial drought indexes; the historical initial drought index is obtained according to a historical time sequence and an ARIMA (p, d, q) model after order determination;
and determining a drought index regression model by using a linear regression algorithm and a least square method by taking the historical initial drought index as an independent variable and the initial drought index as a dependent variable.
Specifically, step 101: the method specifically comprises the following steps:
determining any moment as the current moment;
constructing a current judgment matrix according to drought data of the current moment of the area to be predicted; the elements of the current judgment matrix are the importance degree between any two drought indexes; drought indicators include drought population, drought land area, crop yield loss, livestock loss and economic loss;
according to the current judgment matrix, the formula is utilized
Determining the weight of each index;
judging whether the current judgment matrix passes consistency test or not to obtain a judgment result;
if the judgment result is negative, updating the current judgment matrix and returning to the step of utilizing the formula according to the current judgment matrix
Determining the weight of each index;
if the judgment result is yes, utilizing a formula according to the weight of each index
Determining a drought index at the current moment;
determining a drought index in a preset time period before the current moment as a time sequence of a region to be predicted;
wherein w is i The weight of the i index;
and->
The characteristic vector n of the ith index and the jth index is the total number of drought indexes; f (x) is drought index; f (x) i ) Is the actual measurement value of the i-th index.
Specifically, the method comprises the following steps:
and step one, fusing drought monitoring indexes such as agriculture, ecology, socioeconomic and the like, and acquiring the weights of the drought monitoring indexes such as agriculture, ecology, socioeconomic and the like by adopting a hierarchical analysis method. And comprehensively weighting based on the single monitoring index to obtain a comprehensive drought monitoring index.
And step two, establishing a comprehensive drought monitoring index based on an analytic hierarchy process. The hierarchical structure analysis method for determining the high slope risk index weight comprises the following steps:
and establishing a hierarchical structure. And establishing a multi-level structure, namely a target layer, a criterion layer and a scheme layer, according to the high slope risk evaluation system.
And constructing a judgment matrix. The indexes (drought comprehensive observation indexes can be drought population, drought farmland area, crop yield reduction rate, livestock loss, economic loss and the like, and the actual situation can be adopted here) are compared with each other, the importance among the indexes is analyzed by adopting a 1-9 scale method, and the importance among the indexes is judged mainly according to the 1-9 scale method. 9, performing assignment of the judgment matrix element. All the elements are compared with each other in sequence, and the construction of the judgment matrix is completed. The judgment matrix is shown as formula (1):
A＝(a ij ) m*n (1)
wherein a is ij ＞0，
a ii =1mrow n column. The matrix is m x n. The significance is shown in table 1:
TABLE 1 significance level meaning Table
Weight calculation: calculating the eigenvector W of the judgment matrix A i The calculation process is shown as the formula (2):
the feature vector Wi is normalized to obtain the relative weight w of the evaluation object i i ，w i The calculation process of (2) is shown in the formula (3):
and (5) consistency inspection. After the construction of the judgment matrix is completed, consistency test is needed to judge whether the construction of the judgment matrix is reasonable or not. The method comprises the following steps:
calculating the maximum eigenvalue lambda of the judgment matrix max The calculation process is shown as the formula (4):
the consistency index CI of the judgment matrix is calculated, and the calculation process is shown as a formula (5):
the consistency ratio CR of the judgment matrix is calculated, and the calculation process is shown as the formula (6):
RI represents a random uniformity index.
If CR <0.1, the construction of the judgment matrix is considered reasonable; otherwise, the judgment matrix needs to be reconstructed until the judgment matrix reaches the consistency check standard.
Step three, the comprehensive drought monitoring index calculation process is as follows:
F(x)＝w 1 f(x 1 )+w 2 f(x 2 )+…+w n f(x n ) (7)
wherein w is 1 ,w 2 ,...,w n Weights for each single monitoring indicator; f (x) 1 ),f(x 2 ),...,f(x n ) Is the value of each single monitoring index.
And fourthly, constructing an autoregressive model. Aiming at drought comprehensive monitoring index data of near-N years in arid regions, constructing the regions according to the drought comprehensive monitoring index data of the near-N yearsProvided is a drought comprehensive monitoring index prediction method based on an ARIMA-regression model. First for arbitrary time series { X } t t∈T, the condition that the following is satisfied is called a p-order autoregressive model (AR (p)):
X t ＝φ 0 +φ 1 X t-1 +φ 2 X t-2 +...+φ p X t-p +ε t (8)
wherein phi is 0 ,φ 1 ,...,φ p Is p+1 parameters to be estimated, phi p ≠0，ε t White noise sequence with mean value of 0 and X t-i I=1, 2,3. When phi is 0 When=0, AR (p) is the decentration model.
And fifthly, constructing a moving average model. First for arbitrary time series { X } t t∈T, the condition that the following is satisfied is called a q-order moving average model (MA (q)):
wherein θ 0 ,θ 1 ,...,φ q Is q+1 parameters to be estimated, θ q ≠0，ε t A white noise sequence with a mean value of 0. When the random error μ=0, MA (q) is the decentration model.
Step six, an autoregressive moving average model, namely a combined model of an autoregressive model AR (p) and a moving average model MA (q), is constructed and is called ARMA (p, q). The autoregressive moving average model is expressed as:
wherein when phi is 0 When=0, ARMA (p, q) is the decentration model.
In the drought prediction evaluation, most of time series data are unstable and are non-stable time series. The ARMA (p, q) model set forth above cannot be used normally. A differential approach is taken before fitting so that the non-stationary time series becomes stationary time series.
And step eight, a differential calculation process. First for arbitrary time series { X } t T ε T, the following conversion is performed:
equation (4) is a first order differential operation conversion. From this, d-order differential conversion can be obtained:
the stationary time series is obtained by the d-order differential conversion operation according to the expression (5).
And step nine, constructing an ARIMA (p, d, q) model. Non-stationary time series { X } t t.epsilon.T } satisfies the following conditions:
wherein,,
represents { X ] t T epsilon T, and a stable time sequence obtained after d-level differential transformation, wherein E (x) represents expectations; var represents variance; phi (B) =1-phi 1 B-φ 2 B 2 -...-φ p B p Is an autoregressive coefficient polynomial, where θ (B) =1- θ 1 B-θ 2 B 2 -...-θ q B q Is a moving average coefficient polynomial. The core formula of ARIMA (d, p, q) is the first, the remaining 4 are interpretations of the parameters satisfying the condition in the first formula, and the second is the expected calculation of the white noise sequence sample with the average value of 0. The third is the variance calculation for the white noise sequence samples with a mean of 0. Fourth is the expected calculation of a single sample versus a sequence sample. The fifth is the expected calculation of the individual of the sequence samples and the non-stationary time series.
And step ten, adopting an ARIMA (p, d, q) model to complete comprehensive drought monitoring index data based on the ARIMA (p, d, q) model, and completing the time sequence prediction of the comprehensive drought monitoring index data in recent N years.
And step eleven, establishing a regression model to realize comprehensive drought monitoring index data prediction. The basic idea of linear regression is to build a mathematical model reflecting the correlation of dependent variables with independent variables according to certain criteria. If the regression analysis model contains only one independent variable and dependent variable, and the relationship between the independent variable and the dependent variable can be approximately represented by a straight line, the regression analysis is called as unitary linear regression analysis.
Typically the model formula is as follows:
Y＝β 1 +β 2 X+ε (14)
wherein beta is 1 Is the intercept of function Y and is also the predicted variable; beta 2 Is the slope; x is an independent variable and is an ARIMA model prediction result; epsilon is a random error, and Y is a regression model prediction result, namely a dependent variable.
For the linear regression model, it is assumed that n sets of data are acquired in the sample set of samples respectively (X 1 ,Y 1 )，(X 2 ,Y 2 )，(X 3 ,Y 3 )，...，(X n ,Y n ). For these n sets of data in a plane, a myriad of curves can be used to fit. Since the sample regression function needs to fit the n sets of data as well as possible, it is reasonable to choose the center position for the fitted line. While the fit line is chosen, care is also taken to minimize the total fit error (i.e., total residual), so the least squares method (Ordinary Least Square, OLS) is used herein to minimize the residual squared value for all observations of the selected regression model.
The residual square sum formula is as follows:
wherein X is i And Y is equal to i The values of the independent variables and dependent variables, respectively.
To solve for the smallest sum of squares of residuals Q, β is calculated separately 1 ，β 2 Performing deflection determination andlet its partial derivative value be 0, the system of equations is as follows:
therefore, when the sum of squares of the residuals reaches the minimum value, the beta is calculated independently when the two partial derivatives are 0 1 And beta 2 The formula is as follows:
beta obtained by the formula (17) 1 And beta 2 Substituting into formula (14) to obtain the optimal value of Y, namely the final prediction result
In addition, the invention also provides a drought prediction system based on ARIMA-regression model, comprising:
the time sequence construction module is used for constructing a time sequence of the area to be predicted according to drought data of the area to be predicted;
the system comprises an initial drought index determining module, a fixed-order ARIMA (p, d, q) model, a historical time sequence determining module and a differential order determining module, wherein the initial drought index determining module is used for obtaining an initial drought index at the next moment according to the time sequence and the fixed-order ARIMA (p, d, q) model;
the predicted drought index determining module is used for inputting the initial drought index into the drought index regression model to obtain a predicted drought index at the next moment;
and the drought degree determining module is used for determining the drought degree of the area to be predicted according to the predicted drought index.
The historical time sequence acquisition module is used for acquiring a historical time sequence of the area to be predicted; the elements in the time sequence are the historical drought indexes of the areas to be predicted;
the stationarity processing module is used for carrying out stationarity processing on the historical time sequence by utilizing a difference algorithm to obtain a difference order d and a stationarity processed historical time sequence;
the sub-model building module is used for respectively building a moving average model MA (q) and an autoregressive moving average model ARMA (p, q) according to the historical time sequence after the stable treatment;
the order determining module is used for fitting the moving average model MA (q) and the autoregressive moving average model ARMA (p, q) to obtain an ARIMA (p, d, q) model after order determination.
The historical initial drought index determining module is used for obtaining a plurality of historical initial drought indexes; the historical initial drought index is obtained according to a historical time sequence and an ARIMA (p, d, q) model after order determination;
and the drought index regression model determining module is used for determining a drought index regression model by using a linear regression algorithm and a least square method by taking the historical initial drought index as an independent variable and the initial drought index as a dependent variable.
Preferably, the time sequence construction module specifically includes:
the current time determining unit is used for determining any time as the current time;
the current judgment matrix construction unit is used for constructing a current judgment matrix according to drought data of the current moment of the area to be predicted; the elements of the current judgment matrix are the importance degree between any two drought indexes; drought indicators include drought population, drought land area, crop yield loss, livestock loss and economic loss;
the weight determining unit is used for utilizing a formula according to the current judgment matrix
j= (1, 2, 3..n) determining the weight of each index;
the consistency check unit is used for judging whether the current judgment matrix passes consistency check or not to obtain a judgment result; if the judgment result is negative, the current judgment matrix updating unit is called; if the judgment result is yes, calling a drought index calculation unit;
the current judgment matrix updating unit is used for updating the current judgment matrix and calling the weight determining unit;
a drought index calculation unit for calculating the drought index of the plant,for using the formula according to the weight of each index
Determining a drought index at the current moment;
the time sequence construction unit of the area to be predicted is used for determining that the drought index in a preset time period before the current moment is the time sequence of the area to be predicted;
wherein w is i The weight of the i index;
and->
The characteristic vector n of the ith index and the jth index is the total number of drought indexes; f (x) is drought index; f (x) i ) Is the actual measurement value of the i-th index.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. A drought prediction method based on an ARIMA-regression model, the method comprising:
constructing a time sequence of the area to be predicted according to drought data of the area to be predicted;
obtaining an initial drought index at the next moment according to the time sequence and the ARIMA (p, d, q) model after the order setting; parameters of the fixed-order ARIMA (p, d, q) model are determined according to a historical time sequence; the parameters comprise an autoregressive order p, a moving average order q and a differential order d;
inputting the initial drought index into a drought index regression model to obtain a predicted drought index at the next moment;
and determining the drought degree of the area to be predicted according to the predicted drought index.
2. The ARIMA-regression model-based drought prediction method according to claim 1, further comprising, before the constructing the time series of the region to be predicted from the drought data of the region to be predicted:
acquiring a historical time sequence of a region to be predicted; the elements in the time sequence are historical drought indexes of the area to be predicted;
performing stationarity processing on the historical time sequence by using a difference algorithm to obtain a difference order d and a stationarity processed historical time sequence;
respectively constructing a moving average model MA (q) and an autoregressive moving average model ARMA (p, q) according to the historical time sequence after the stable treatment;
fitting the moving average model MA (q) and the autoregressive moving average model ARMA (p, q) to obtain an ARIMA (p, d, q) model after the order determination.
3. The ARIMA-regression model-based drought prediction method according to claim 1, further comprising, before the constructing the time series of the region to be predicted from the drought data of the region to be predicted:
acquiring a plurality of historical initial drought indexes; the historical initial drought index is obtained according to a historical time sequence and an ARIMA (p, d, q) model after order determination;
and determining a drought index regression model by using a linear regression algorithm and a least square method by taking the historical initial drought index as an independent variable and the initial drought index as a dependent variable.
4. The arid condition prediction method based on ARIMA-regression model according to claim 1, wherein the constructing a time series of the region to be predicted according to the arid data of the region to be predicted specifically comprises:
determining any moment as the current moment;
constructing a current judgment matrix according to drought data of the current moment of the area to be predicted; the elements of the current judgment matrix are the importance degree between any two drought indexes; the drought index comprises drought population, drought farmland area, crop yield reduction, livestock loss and economic loss;
according to the current judgment matrix, the formula is utilized
Determining the weight of each index;
judging whether the current judgment matrix passes consistency test or not to obtain a judgment result;
if the judgment result is no, updating the current judgment matrix and returning to the step of utilizing a formula according to the current judgment matrix
Determining the weight of each index; />
If the judgment result is yes, utilizing a formula according to the weight of each index
Determining a drought index at the current moment;
determining a drought index in a preset time period before the current moment as a time sequence of a region to be predicted;
wherein w is i The weight of the i index;
and->
The characteristic vector n of the ith index and the jth index is the total number of drought indexes; f (x) is drought index; f (x) i ) Is the actual measurement value of the i-th index.
5. A drought prediction system based on an ARIMA-regression model, the system comprising:
the time sequence construction module is used for constructing a time sequence of the area to be predicted according to drought data of the area to be predicted;
the initial drought index determining module is used for obtaining an initial drought index at the next moment according to the time sequence and the ARIMA (p, d, q) model after the order setting; parameters of the fixed-order ARIMA (p, d, q) model are determined according to a historical time sequence; the parameters comprise an autoregressive order p, a moving average order q and a differential order d;
the predicted drought index determining module is used for inputting the initial drought index into a drought index regression model to obtain a predicted drought index at the next moment;
and the drought degree determining module is used for determining the drought degree of the area to be predicted according to the predicted drought index.
6. The ARIMA-regression model based drought prediction system of claim 5, further comprising:
the historical time sequence acquisition module is used for acquiring a historical time sequence of the area to be predicted; the elements in the time sequence are historical drought indexes of the area to be predicted;
the stationarity processing module is used for carrying out stationarity processing on the historical time sequence by utilizing a difference algorithm to obtain a historical time sequence after the difference order d and the stationarity processing;
the sub-model building module is used for respectively building a moving average model MA (q) and an autoregressive moving average model ARMA (p, q) according to the historical time sequence after the stable treatment;
and the order determining module is used for fitting the moving average model MA (q) and the autoregressive moving average model ARMA (p, q) to obtain an ARIMA (p, d, q) model after order determination.
7. The ARIMA-regression model based drought prediction system of claim 5, further comprising:
the historical initial drought index determining module is used for obtaining a plurality of historical initial drought indexes; the historical initial drought index is obtained according to a historical time sequence and an ARIMA (p, d, q) model after order determination;
and the drought index regression model determining module is used for determining a drought index regression model by using a linear regression algorithm and a least square method by taking the historical initial drought index as an independent variable and the initial drought index as a dependent variable.
8. The arid prediction system based on ARIMA-regression model according to claim 5, wherein the time series construction module specifically comprises:
the current time determining unit is used for determining any time as the current time;
the current judgment matrix construction unit is used for constructing a current judgment matrix according to drought data of the current moment of the area to be predicted; the elements of the current judgment matrix are the importance degree between any two drought indexes; the drought index comprises drought population, drought farmland area, crop yield reduction, livestock loss and economic loss;
the weight determining unit is used for utilizing a formula according to the current judgment matrix
Determining the weight of each index;
the consistency check unit is used for judging whether the current judgment matrix passes consistency check or not to obtain a judgment result; if the judgment result is negative, the current judgment matrix updating unit is called; if the judgment result is yes, calling a drought index calculation unit;
the current judgment matrix updating unit is used for updating the current judgment matrix and calling the weight determining unit;
a drought index calculation unit for using a formula according to the weight of each index
Determining a drought index at the current moment;
the time sequence construction unit of the area to be predicted is used for determining that the drought index in a preset time period before the current moment is the time sequence of the area to be predicted;
wherein w is i The weight of the i index;
and->
The characteristic vector n of the ith index and the jth index is the total number of drought indexes; f (x) is drought index; f (x) i ) Is the actual measurement value of the i-th index. />
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