CN114021829A - Land use pattern prediction and optimization method considering non-point source pollution control - Google Patents

Land use pattern prediction and optimization method considering non-point source pollution control Download PDF

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CN114021829A
CN114021829A CN202111334097.6A CN202111334097A CN114021829A CN 114021829 A CN114021829 A CN 114021829A CN 202111334097 A CN202111334097 A CN 202111334097A CN 114021829 A CN114021829 A CN 114021829A
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荣戗戗
曾靖妮
苏美蓉
岳文淙
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Dongguan University of Technology
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Abstract

The invention discloses a land use pattern prediction and optimization method considering non-point source pollution control, and belongs to the technical field of non-point source pollution control methods. According to the method, a land utilization change system dynamic model is constructed through a dynamic feedback relation of social and economic indexes to land utilization change, land utilization pattern change characteristics under the influence of the social and economic indexes are analyzed and predicted, fuzzy numbers are introduced on the basis of the land utilization change system dynamic model to represent different pollutant output coefficients of different land utilization types in a research area, the uncertainty characteristics of non-point source pollution output are reflected, and the areas of different land utilization and the total load quantity of non-point source pollutants are estimated; the method comprises the steps of coupling an interval linear programming method and a fuzzy parameter programming method, constructing an interval fuzzy linear programming model, setting constraints on the basis of maximization of the economic benefit of a land system, and obtaining a land use pattern optimization scheme which meets the development requirement of regional social economy and achieves a specific non-point source pollution control reduction target.

Description

Land use pattern prediction and optimization method considering non-point source pollution control
Technical Field
The invention relates to the technical field of non-point source pollution control methods, in particular to a land use pattern prediction and optimization method considering non-point source pollution control.
Background
At present, non-point source pollution caused by land utilization and change thereof is one of the main water environment problems at present, while the change of a land utilization pattern influences output load of sediments and nutrients, improper land utilization distribution can possibly cause the nutrient load to exceed the environment bearing capacity, the non-point source pollution degree is continuously deepened, and water resource and water environment safety are seriously threatened; particularly, the land use pattern of the water source area directly influences the water quality of the downstream area, and the land use pattern needs to be optimized by combining the reduction of non-point source pollution and the protection of the water environment;
on one hand, in the actual non-point source pollution control and management and land utilization change process, complex multiple uncertainty problems exist, including uncertainty in system parameters, model methods and interrelations among various elements of the system, and the like; the uncertainty source of the parameters is very wide, for example, under the interaction of multiple factors such as precipitation, air temperature, soil and human activities, the non-point source pollution has the characteristics of randomness, universality, latency and the like, and the actual characteristics and conditions of the non-point source pollution are often difficult to reflect by the pollutant output coefficient represented by a determined empirical value;
on the other hand, multiple uncertainties derived from various socioeconomic factors exist in the optimization process of the land use pattern, for example, the regional financial condition changes differently every year, and the investment preference of people is mainly influenced by subjectivity; meanwhile, parameters such as the output load of non-point source pollution in unit area are influenced by natural conditions and human activities, such as: temperature, rainfall, soil physicochemical properties, planting patterns, population, pavement hardening and the like, and has multiple uncertainties which cannot be reflected by constants; many uncertainty factors complicate the variability of economic output and input costs for various land use types with uncertainty;
complex multiple uncertainties exist in the processes of land utilization change and non-point source pollution output, and the current land utilization pattern prediction and optimization research considering non-point source pollution control has not high enough attention to the uncertainty problem; in addition, many land use pattern prediction studies adopt models and algorithms such as markov chain, fluent and machine learning, or time series remote sensing data such as MODIS, to perform prediction analysis of future land use patterns; however, the time cost of the application of the methods is high, the required data acquisition difficulty is high, and the dynamic feedback relationship between the land utilization change and the social and economic factors is rarely reflected.
Disclosure of Invention
The present invention aims to provide a land use pattern prediction and optimization method considering non-point source pollution control to solve the problems presented in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: a method of land use pattern prediction and optimization that accounts for non-point source pollution control, the method comprising: the land utilization change system dynamic model is based on the system dynamic model, the land utilization change system dynamic model is constructed by determining a linear fitting mathematical relationship between social economic indexes and land utilization transfer probabilities through determining the linear fitting mathematical relationship between the social economic indexes and the land utilization transfer probabilities, land utilization areas under different scenes are obtained and then input into the non-point source pollution estimation module, the non-point source pollution estimation module estimates the total load capacity of land pollutants of different utilization types, the interval fuzzy linear planning model aims at maximizing the economic benefits of the land utilization system, the interval fuzzy linear planning model is solved through a two-step interactive algorithm under the constraint condition, and regional land utilization pattern optimization schemes and decision suggestions under different scenes are obtained.
The land use change system dynamics model comprises a state variable, a rate variable and an auxiliary variable, wherein the state variable is a main output result of the model, the rate variable represents the change rate of the state variable in each time period, and the auxiliary variable influences the rate variable to enable the output value of the state variable to generate corresponding change; the state variable is the area of various land use types at a certain moment, the rate variable is the land use transfer probability, and the auxiliary variable is a social and economic index closely related to the land use change;
a grey correlation degree analysis method, a principal component analysis method and a linear regression method are introduced to screen social and economic indexes, the land utilization transition probability is calculated, a land utilization change system dynamic model is constructed by determining a linear fitting mathematical relationship between the social and economic indexes and the land utilization transition probability, and land utilization areas under different scenes are output.
The land use transition probability is calculated by using a grid calculation tool of ArcGIS, the grid data resolution is 30m, and the land use transition probability calculation formula is as follows:
Figure BDA0003349886950000021
Figure BDA0003349886950000022
p is the land use transition probability; pijIs the transition probability of the ith to jth land utilization type in a certain year; x is the number ofijIs the area of the ith soil utilization type converted into the jth soil utilization type in a certain year; x'iIs the area of the ith land use type before the change in the year.
The social and economic indexes influencing the land use change are many, 59 social and economic indexes are collected in the early stage of case research, a grey correlation degree analysis method is introduced to identify key social and economic indexes greatly influencing the land use change, on the premise of meeting the correlation among variables, the number of the social and economic indexes is reduced, and appropriate variables are selected according to problems to achieve appropriate simplification, so that the model is convenient to understand and analyze, and the modeling cost is reduced;
a grey correlation degree analysis method is introduced to identify key socioeconomic indexes which have large influence on land use change, a standardized comparison sequence is calculated through the screened socioeconomic index comparison sequence, a land use degree comprehensive index is calculated to serve as a reference sequence, a correlation coefficient is obtained through the standardized comparison sequence and the reference sequence, and finally a grey correlation degree is obtained.
The socio-economic indicator is used as a comparison sequence, a standardized comparison sequence of the socio-economic indicator is calculated, and the standardized comparison sequence is calculated according to a formula:
Figure BDA0003349886950000031
dl(k) is a normalized comparison sequence; d'l(k) Is a comparative sequence, and the comparative sequence takes the value of social and economic indexes; l is the number of indices in the comparison sequence;
calculating a comprehensive index of land utilization degree, and calculating a formula:
Figure BDA0003349886950000032
la is a comprehensive index of land utilization degree; b isiIs the grade index of the land utilization degree of the ith grade; ciIs the percentage of the graded area of the i-th level land utilization degree;
the land utilization degree of the invention is divided into 3 grades, which are respectively 1 grade: woodland, water and grass; and 2, stage: ploughing; and 3, level: building land;
calculating a standardized reference sequence of the land utilization degree comprehensive index, and calculating a formula:
Figure BDA0003349886950000041
d0(k) is a standardized reference sequence of the comprehensive index of the land utilization degree; la (k) is a reference sequence, and the reference sequence takes a land utilization degree comprehensive index.
Calculating a correlation coefficient, and calculating a formula:
Figure BDA0003349886950000042
ξ0l(k) is dlTo d0The correlation coefficient at the moment k, p is a resolution coefficient, the value range is 0-1, the larger p is, the smaller resolution is, and vice versa;
calculating the grey correlation degree through the correlation coefficient, and calculating a formula:
Figure BDA0003349886950000043
r0lis the degree of correlation in gray, with a higher numerical value of the degree of correlation indicating a higher degree of correlation between the reference sequence and the comparison sequence.
And (3) carrying out principal component analysis on the selected socioeconomic indexes, wherein the KOM value is 0.786, and the Bartlett's spherical test is 0.000, which shows that the selected indexes are suitable for carrying out principal component analysis, further screening the socioeconomic indexes by using principal component analysis and linear regression methods, and eliminating collinearity in the indexes.
Lookup F1＝(2000，38)，(2005，40)，(2010，42)，(2015，38)，(2020，59)
Lookup F2＝(2000，6.15)，(2005，4.34)，(2010，4.42)，(2015，4.72)，(2020，5.65)
Lookup F3＝(2000，1860)，(2005，3684)，(2010，4658)，(2015，4812)，(2020，3436)
Lookup F4＝(2000，2836)，(2005，2370)，(2010，3317)，(2015，4263)，(2020，5850)
Lookup F5＝(2000，13339)，(2005，12476)，(2010，12063)，(2015，11970)，(2020，8700)
F1-F5 are five socio-economic indexes, and the annual data of the socio-economic indexes come from the statistical yearbook and the national economy and social development statistical bulletin.
A land use change system dynamic model is constructed by determining a linear fitting mathematical relationship between social and economic indicators and land use transition probability, and the mathematical function relationship in the model is as follows:
CRL(t)＝CRL(0)+[T5(t)×FL(0)+T9(t)×GL(0)+T13(t)×WA(0)+T17(t)×COL(0)-(T1(t)+T2(t)+T3(t)+T4(t))×CRL(0)]×dt
FL(t)＝FL(0)+[T1(t)×CRL(0)+T10(t)×GL(0)+T14(t)×WA(0)+T18(t)×COL(0)-(T5(t)+T6(t)+T7(t)+T8(t))×FL(0)]×dt
GL(t)＝GL(0)+[T2×CRL(0)+T6×FL(0)+T15×WA(0)+T19×COL(0)-(T9+T10+T11+T12)×GL(0)]×dt
WA(t)＝WA(0)+[T3×CRL(0)+T7×FL(0)+T11×GL(0)+T20×COL(0)-(T13+T14+T15+T16)×WA(0)]×dt
COL(t)＝COL(0)+[T4×CRL(0)+T8×FL(0)+T12×GL(0)+T16×WA(0)-(T17+T18+T19+T20)×COL(0)]×dt
T1＝0.1785×F2-0.7786
T2＝0.0064×F1-0.2476
T3＝0.0003×F1-0.0116
T4＝0.0022×F1-0.0754
T5＝0.0286×F2-0.1247
T6＝0.0037×F1-0.1428
T7＝0.0001×F1-0.0049
T8＝-7.876×10-7×F5+0.0106
T9＝0.0041×F1-0.1467
T10＝0.0294×F1-1.0583
T11＝0.0001×F1-0.0057
T12＝3.4971×10-6×F4-0.0074
T13＝0.0941×F2-0.4123
T14＝0.1139×F2-0.0001×F3
T15＝0.0051×F1-0.1935
T16＝0.0027×F1-0.0982
T17＝0.2219×F2-0.9736
T18＝0.0476×F2-0.2056
T19＝0.0047×F1-0.1811
Lookup T20＝(2000，0.0010)，(2005，0)，(2010，0.0121)，(2015，0)，(2020，0.0046)
CRL (T), FL (T), GL (T), WA (T) and COL (T) are the areas of cultivated land, forest land, grassland, water area and construction land at time T, respectively, CRL (0), FL (0), GL (0), WA (0) and COL (0) are the initial areas of cultivated land, forest land, grassland, water area and construction land, respectively, and T1-T20 are land use transition probabilities;
the non-point source pollution estimation module introduces fuzzy numbers to express different pollutant output coefficients of different land utilization types in a research area to reflect the uncertainty characteristics of non-point source pollution output based on an output coefficient model, introduces interval numbers to express the areas of different land utilization and the total load capacity of non-point source pollutants, and calculates a formula:
Figure BDA0003349886950000061
Figure BDA0003349886950000062
is the total load of the r-class contaminants; i is a land use type;
Figure BDA0003349886950000063
is the output coefficient of r pollutants in the ith land utilization type;
Figure BDA0003349886950000064
is the area of the i-th land utilization type;
the interval fuzzy linear programming model aims at maximizing the economic benefit of the land utilization system;
the objective function calculation formula:
Figure BDA0003349886950000065
Figure BDA0003349886950000066
is the economic output per unit area of the ith land utilization type in the s sub-area;
Figure BDA0003349886950000067
is the unit area input cost of the ith land utilization type in the s sub-area.
The constraint conditions of the interval fuzzy linear programming model comprise non-point source pollution load constraint, grain yield constraint, land utilization area supply quantity constraint and non-negative constraint;
a non-point source pollution load constraint calculation formula:
Figure BDA0003349886950000068
Figure BDA0003349886950000069
is the r-class pollutant output coefficient;
Figure BDA00033498869500000610
is the total load of the r-class contaminants;
grain yield constraint calculation formula:
Figure BDA0003349886950000071
Figure BDA00033498869500000710
is the yield of grain crops per unit area; OGP±Is the total yield of the grain crops.
Land utilization area supply quantity constraint calculation formula:
Figure BDA0003349886950000073
Figure BDA0003349886950000074
MIAisand MAAisMinimum and maximum deliverables of areas of different land use types, respectively, the values being determined by land use area prediction results and 2020 (reference year) land use status areas; TA (TA)±Is the total area of the study area;
non-negative constraint calculation formula:
Figure BDA0003349886950000075
Figure BDA0003349886950000076
is the area of the ith land utilization type in the s sub-zone.
Solving an interval fuzzy linear programming model through a two-step interactive algorithm, and obtaining an optimization scheme and a decision suggestion of regional land use patterns under different scenes; fuzzy parameters in a model
Figure BDA0003349886950000077
Determined by triangular fuzzy membership function, and calculating triangular fuzzy number
Figure BDA0003349886950000078
Where b is the most likely value and a and c are the minimum and maximum possible values, respectively. The technical proposal converts the fuzzy parameter into an interval value by introducing an alpha intercept set,
Figure BDA0003349886950000079
α -cut of (c):
A(α)＝[AL(α)，AR(α)]，0≤α≤1
AL(α) ═ a + (b-a) × α is the left end of the α -cut; a. theRAnd (α) ═ c- (c-b) × α is the right end of the α -cut.
Compared with the prior art, the invention has the following beneficial effects:
the dynamic feedback relationship of the social and economic indexes to the land utilization change is used for constructing a land utilization change system dynamic model, and the land utilization pattern change characteristics under the influence of the social and economic indexes are analyzed and predicted, so that the simulation and prediction process of the land utilization pattern is more consistent with the regional development reality, the social and economic data required in the model are usually easy to obtain, and the land utilization change system dynamic model is easy to operate and has higher practicability;
the non-point source pollution estimation module introduces fuzzy numbers on the basis of a land utilization change system dynamic model to express different pollutant output coefficients of different land utilization types in a research area so as to reflect the uncertainty characteristics of non-point source pollution output, and introduces interval numbers to express the areas of different land utilization and the total load quantity of non-point source pollutants;
on the basis of a land utilization change system dynamic model, an interval linear programming and fuzzy parameter programming method is coupled, an interval fuzzy linear programming model is constructed, constraints are set on the basis of maximization of economic benefits of a land system, the constraints comprise a non-point source pollution load reduction target, regional grain yield and various land supply areas, and a land utilization pattern optimization scheme acquired by the model can simultaneously meet regional social and economic development requirements and achieve a specific non-point source pollution control reduction target;
the interval fuzzy linear programming model introduces intervals and fuzzy parameters, and effectively reflects the uncertainty characteristics of various variables in the actual case research.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic block diagram of a land use pattern prediction and optimization method of the present invention that considers non-point source pollution control;
fig. 2 is a table of land use transfers according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution:
the implementation method comprises the following steps: a method for land use pattern prediction and optimization in view of non-point source pollution control, the method comprising: the land utilization change system dynamic model is based on the system dynamic model, the land utilization change system dynamic model is constructed by determining a linear fitting mathematical relationship between social economic indexes and land utilization transfer probabilities through determining the linear fitting mathematical relationship between the social economic indexes and the land utilization transfer probabilities, land utilization areas under different scenes are obtained and then input into the non-point source pollution estimation module, the non-point source pollution estimation module estimates the total load quantity of land pollutants of different utilization types, the interval fuzzy linear planning model takes the economic benefit maximization of the land utilization system as a target, the interval fuzzy linear planning model is solved through a two-step interactive algorithm under the constraint condition, and the regional land utilization pattern optimization scheme and the decision suggestion under different scenes are obtained.
The land use change system dynamics model comprises a state variable, a speed variable and an auxiliary variable, wherein the state variable is a main output result of the model, the speed variable represents the change rate of the state variable in each time period, and the auxiliary variable influences the speed variable to enable the output value of the state variable to generate corresponding change; the state variable is the area of various land utilization types at a certain moment, the rate variable is the land utilization transition probability, and the auxiliary variable is a social and economic index closely related to the land utilization change;
a grey correlation degree analysis method, a principal component analysis method and a linear regression method are introduced to screen social and economic indexes, the land utilization transition probability is calculated, a land utilization change system dynamic model is constructed by determining a linear fitting mathematical relationship between the social and economic indexes and the land utilization transition probability, and land utilization areas under different scenes are output.
The land use transition probability is calculated by using a grid calculation tool of ArcGIS, the grid data resolution is 30m, and the land use transition probability calculation formula is as follows:
Figure BDA0003349886950000091
Figure BDA0003349886950000092
p is the land use transition probability; pijIs the transition probability of the ith to jth land utilization type in a certain year; x is the number ofijIs the area of the ith soil utilization type converted into the jth soil utilization type in a certain year; x'iIs the area of the ith land use type before the change in the year.
The social and economic indexes influencing the land use change are many, 59 social and economic indexes are collected in the early stage of case research, a grey correlation degree analysis method is introduced to identify key social and economic indexes greatly influencing the land use change, on the premise of meeting the correlation among variables, the number of the social and economic indexes is reduced, and appropriate variables are selected according to problems to achieve appropriate simplification, so that the model is convenient to understand and analyze, and the modeling cost is reduced;
a grey correlation degree analysis method is introduced to identify key socioeconomic indexes which have large influence on land use change, a standardized comparison sequence is calculated through the screened socioeconomic index comparison sequence, a land use degree comprehensive index is calculated to serve as a reference sequence, a correlation coefficient is obtained through the standardized comparison sequence and the reference sequence, and finally a grey correlation degree is obtained.
The socio-economic indicator is used as a comparison sequence, a standardized comparison sequence of the socio-economic indicator is calculated, and the standardized comparison sequence is calculated according to a formula:
Figure BDA0003349886950000093
dl(k) is a normalized comparison sequence; dl(k) Is a comparative sequence, and the comparative sequence takes the value of social and economic indexes; l is the number of indices in the comparison sequence;
calculating a comprehensive index of land utilization degree, and calculating a formula:
Figure BDA0003349886950000104
la is a comprehensive index of land utilization degree; b isiIs the grade index of the land utilization degree of the ith grade; ciIs the percentage of the graded area of the i-th level land utilization degree; the land utilization degree of the invention is divided into 3 grades, which are respectively 1 grade: woodland, water and grass; and 2, stage: ploughing; and 3, level: building land;
calculating a standardized reference sequence of the land utilization degree comprehensive index, and calculating a formula:
Figure BDA0003349886950000101
d0(k) is a standardized reference sequence of the comprehensive index of the land utilization degree; la (k) is a reference sequence, and the reference sequence takes a land utilization degree comprehensive index.
Calculating a correlation coefficient, and calculating a formula:
Figure BDA0003349886950000102
ξ0l(k) is dlTo d0The correlation coefficient at the moment k, p is a resolution coefficient, the value range is 0-1, the larger p is, the smaller resolution is, and vice versa;
calculating the grey correlation degree through the correlation coefficient, and calculating a formula:
Figure BDA0003349886950000103
r0lis the degree of correlation in gray, with a higher numerical value of the degree of correlation indicating a higher degree of correlation between the reference sequence and the comparison sequence.
And (3) carrying out principal component analysis on the selected socioeconomic indexes, wherein the KOM value is 0.786, and the Bartlett's spherical test is 0.000, which shows that the selected indexes are suitable for carrying out principal component analysis, further screening the socioeconomic indexes by using principal component analysis and linear regression methods, and eliminating collinearity in the indexes.
Lookup F1＝(2000，38)，(2005，40)，(2010，42)，(2015，38)，(2020，59)
Lookup F2＝(2000，6.15)，(2005，4.34)，(2010，4.42)，(2015，4.72)，(2020，5.65)
Lookup F3＝(2000，1860)，(2005，3684)，(2010，4658)，(2015，4812)，(2020，3436)
Lookup F4＝(2000，2836)，(2005，2370)，(2010，3317)，(2015，4263)，(2020，5850)
Lookup F5＝(2000，13339)，(2005，12476)，(2010，12063)，(2015，11970)，(2020，8700)
F1-F5 are five socio-economic indexes, and the annual data of the socio-economic indexes come from the statistical yearbook and the national economy and social development statistical bulletin.
A land use change system dynamic model is constructed by determining a linear fitting mathematical relationship between social and economic indicators and land use transition probability, and the mathematical function relationship in the model is as follows:
CRL(t)＝CRL(0)+[T5(t)×FL(0)+T9(t)×GL(0)+T13(t)×WA(0)+T17(t)×COL(0)-(T1(t)+T2(t)+T3(t)+T4(t))×CRL(0)]×dt
FL(t)＝FL(0)+[T1(t)×CRL(0)+T10(t)×GL(0)+T14(t)×WA(0)+T18(t)×COL(0)-(T5(t)+T6(t)+T7(t)+T8(t))×FL(0)]×dt
GL(t)＝GL(0)+[T2×CRL(0)+T6×FL(0)+T15×WA(0)+T19×COL(0)-(T9+T10+T11+T12)×GL(0)]×dt
WA(t)＝WA(0)+[T3×CRL(0)+T7×FL(0)+T11×GL(0)+T20×COL(0)-(T13+T14+T15+T16)×WA(0)]×dt
COL(t)＝COL(0)+[T4×CRL(0)+T8×FL(0)+T12×GL(0)+T16×WA(0)-(T17+T18+T19+T20)×COL(0)]×dt
T1＝0.1785×F2-0.7786
T2＝0.0064×F1-0.2476
T3＝0.0003×F1-0.0116
T4＝0.0022×F1-0.0754
T5＝0.0286×F2-0.1247
T6＝0.0037×F1-0.1428
T7＝0.0001×F1-0.0049
T8＝-7.876×10-7×F5+0.0106
T9＝0.0041×F1-0.1467
T10＝0.0294×F1-1.0583
T11＝0.0001×F1-0.0057
T12＝3.4971×10-6×F4-0.0074
T13＝0.0941×F2-0.4123
T14＝0.1139×F2-0.0001×F3
T15＝0.0051×F1-0.1935
T16＝0.0027×F1-0.0982
T17＝0.2219×F2-0.9736
T18＝0.0476×F2-0.2056
T19＝0.0047×F1-0.1811
Lookup T20＝(2000，0.0010)，(2005，0)，(2010，0.0121)，(2015，0)，(2020，0.0046)
CRL (T), FL (T), GL (T), WA (T) and COL (T) are the areas of cultivated land, forest land, grassland, water area and construction land at time T, respectively, CRL (0), FL (0), GL (0), WA (0) and COL (0) are the initial areas of cultivated land, forest land, grassland, water area and construction land, respectively, and T1-T20 are land use transition probabilities;
please refer to fig. 2: t1 denotes the transition probability of converting cultivated land into forest land; t2 denotes the transition probability of converting arable land into grassland; t3 denotes the transition probability of converting cultivated land into water area; t4 indicates the transition probability of converting cultivated land into construction land; t5 indicates the transition probability of converting the forest land into cultivated land; t6 denotes the transition probability of converting woodland into grassland; t7 denotes the transition probability of converting woodland into water; t8 denotes a transition probability of converting a woodland into a construction land; t9 denotes the transition probability of converting grass into arable land; t10 indicates the transition probability of grass to woodland; t11 indicates the transition probability of grass to water; t12 denotes the transition probability of grass to construction land; t13 indicates the transition probability of converting a water area into arable land; t14 denotes the transition probability of water area to forest land; t15 denotes the transition probability of a water area to grass; t16 denotes a transition probability of a water area to a construction land; t17 denotes a transition probability of converting a construction land into a cultivated land; t18 denotes a transition probability of converting a construction land into a forest land; t19 denotes the transition probability of the construction land to the grass; t20 indicates the transition probability of the construction site to the water area.
The non-point source pollution estimation module introduces fuzzy numbers to express different pollutant output coefficients of different land utilization types in a research area to reflect the uncertainty characteristics of non-point source pollution output based on an output coefficient model, introduces interval numbers to express the areas of different land utilization and the total load capacity of non-point source pollutants, and calculates a formula:
Figure BDA0003349886950000131
Figure BDA0003349886950000132
is the total load of the r-class contaminants; i is a land use type;
Figure BDA0003349886950000133
is the output coefficient of r pollutants in the ith land utilization type;
Figure BDA0003349886950000134
is the area of the i-th land utilization type;
the interval fuzzy linear programming model aims at maximizing the economic benefit of the land utilization system;
the objective function calculation formula:
Figure BDA0003349886950000135
Figure BDA0003349886950000136
is the economic output per unit area of the ith land utilization type in the s sub-area;
Figure BDA0003349886950000137
is the unit area input cost of the ith land utilization type in the s sub-area.
The constraint conditions of the interval fuzzy linear programming model comprise non-point source pollution load constraint, grain yield constraint, land utilization area supply quantity constraint and non-negative constraint;
a non-point source pollution load constraint calculation formula:
Figure BDA0003349886950000138
Figure BDA0003349886950000139
is the r-class pollutant output coefficient;
Figure BDA00033498869500001310
is the total load of the r-class contaminants;
grain yield constraint calculation formula:
Figure BDA00033498869500001311
Figure BDA00033498869500001312
is the yield of grain crops per unit area; OGP±Is the total yield of the grain crops.
Land utilization area supply quantity constraint calculation formula:
Figure BDA00033498869500001313
Figure BDA00033498869500001314
MIAisand MAAisMinimum and maximum deliverables of areas of different land use types, respectively, the values being determined by land use area prediction results and 2020 (reference year) land use status areas; TA (TA)±Is the total area of the study area;
non-negative constraint calculation formula:
Figure BDA0003349886950000141
Figure BDA0003349886950000142
is the area of the ith land utilization type in the s sub-zone.
Solving an interval fuzzy linear programming model through a two-step interactive algorithm, and obtaining an optimization scheme and a decision suggestion of regional land use patterns under different scenes; fuzzy parameters in a model
Figure BDA0003349886950000143
Determined by triangular fuzzy membership function, and calculating triangular fuzzy number
Figure BDA0003349886950000144
Where b is the most likely value and a and c are the minimum and maximum possible values, respectively. The technical proposal converts the fuzzy parameter into an interval value by introducing an alpha intercept set,
Figure BDA0003349886950000145
α -cut of (c):
A(α)＝[AL(α)，AR(α)]，0≤α≤1
AL(α) ═ a + (b-a) × α is the left end of the α -cut; a. theRAnd (α) ═ c- (c-b) × α is the right end of the α -cut.
The second embodiment: assuming that the minimum possible value a, the maximum possible value b, and the maximum possible value c of the blurring parameter F are determined to be 5, 7, and 10 from the history data or the like, respectively, and the α -cut is set to be 0.3, 0.6, and 0.9, then
When the alpha-cut is 0.3,
AL(α)＝5+(7-5)×0.3＝5.6，AR(α)＝10-(10-7)×0.3＝9.1
when the alpha-cut is 0.6,
AL(α)＝5+(7-5)×0.6＝6.2，AR(α)＝10-(10-7)×0.6＝8.2；
when the alpha-cut is 0.9,
AL(α)＝5+(7-5)×0.9＝6.8，AR(α)＝10-(10-7)×0.9＝7.3.
it is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A land use pattern prediction and optimization method considering non-point source pollution control is characterized by comprising the following steps: the method comprises the following steps: the land utilization change system dynamic model is based on the system dynamic model, the land utilization change system dynamic model is constructed by determining a linear fitting mathematical relationship between social economic indexes and land utilization transfer probabilities through determining the linear fitting mathematical relationship between the social economic indexes and the land utilization transfer probabilities, land utilization areas under different scenes are obtained and then input into the non-point source pollution estimation module, the non-point source pollution estimation module estimates the total load capacity of land pollutants of different utilization types, the interval fuzzy linear planning model aims at maximizing the economic benefits of the land utilization system, the interval fuzzy linear planning model is solved through a two-step interactive algorithm under the constraint condition, and regional land utilization pattern optimization schemes and decision suggestions under different scenes are obtained.
2. A land use pattern prediction and optimization method considering non-point source pollution control as claimed in claim 1, wherein: the land use change system dynamics model comprises a state variable, a rate variable and an auxiliary variable, wherein the state variable is the area of various land use types at a certain moment, the rate variable is land use transfer probability, and the auxiliary variable is a social and economic index closely related to land use change;
a grey correlation degree analysis method, a principal component analysis method and a linear regression method are introduced to screen social and economic indexes, the land utilization transition probability is calculated, a land utilization change system dynamic model is constructed by determining a linear fitting mathematical relationship between the social and economic indexes and the land utilization transition probability, and land utilization areas under different scenes are output.
3. A land use pattern prediction and optimization method considering non-point source pollution control as claimed in claim 2, wherein: the land utilization transfer probability calculation formula is as follows:
Figure FDA0003349886940000011
Figure FDA0003349886940000012
p is the land use transition probability; pijIs the transition probability of the ith to jth land utilization type in a certain year; x is the number ofijIs the area of the ith soil utilization type converted into the jth soil utilization type in a certain year; x is the number ofi' is the area of the ith land use type before a change in a year.
4. A land use pattern prediction and optimization method considering non-point source pollution control according to claim 3, characterized in that: a grey correlation degree analysis method is introduced to identify key socioeconomic indexes which have large influence on land use change, a standardized comparison sequence is calculated through the screened socioeconomic index comparison sequence, a land use degree comprehensive index is calculated to serve as a reference sequence, a correlation coefficient is obtained through the standardized comparison sequence and the reference sequence, and finally a grey correlation degree is obtained.
5. A land use pattern prediction and optimization method considering non-point source pollution control according to claim 4, characterized in that: the socio-economic indicator is used as a comparison sequence, a standardized comparison sequence of the socio-economic indicator is calculated, and the standardized comparison sequence is calculated according to a formula:
Figure FDA0003349886940000021
dl(k) is a standardized comparison sequence of socioeconomic indicators; dl' (k) is a comparative sequence, and the comparative sequence takes social and economic indexes; l is the number of indices in the comparison sequence; calculating a comprehensive index of land utilization degree, and calculating a formula:
Figure FDA0003349886940000022
la is a comprehensive index of land utilization degree; b isiIs the grade index of the land utilization degree of the ith grade; ciIs the percentage of the graded area of the i-th level land utilization degree;
calculating a standardized reference sequence of the land utilization degree comprehensive index, and calculating a formula:
Figure FDA0003349886940000023
d0(k) is a standardized reference sequence of the comprehensive index of the land utilization degree; la (k) is a reference sequence, and the reference sequence takes a land utilization degree comprehensive index.
Calculating a correlation coefficient, and calculating a formula:
Figure FDA0003349886940000031
ξ0l(k) is dlTo d0The correlation coefficient at the moment k, p is a resolution coefficient, the value range is 0-1, the larger p is, the smaller resolution is, and vice versa;
calculating the grey correlation degree through the correlation coefficient, and calculating a formula:
Figure FDA0003349886940000032
r0lis the grey correlation.
6. A land use pattern prediction and optimization method considering non-point source pollution control according to claim 5, characterized in that: the non-point source pollution estimation module introduces fuzzy numbers to express different pollutant output coefficients of different land utilization types in a research area to reflect the uncertainty characteristics of non-point source pollution output based on an output coefficient model, introduces interval numbers to express the areas of different land utilization and the total load capacity of non-point source pollutants, and calculates a formula:
Figure FDA0003349886940000033
Figure FDA0003349886940000034
is the total load of the r-class contaminants; i is a land use type;
Figure FDA0003349886940000035
is the output coefficient of r pollutants in the ith land utilization type;
Figure FDA0003349886940000036
is the area of the i-th land use type.
7. A land use pattern prediction and optimization method considering non-point source pollution control according to claim 6, characterized in that: the interval fuzzy linear programming model aims at maximizing the economic benefit of the land utilization system;
the objective function calculation formula:
Figure FDA0003349886940000037
Figure FDA0003349886940000038
is the economic output per unit area of the ith land utilization type in the s sub-area;
Figure FDA0003349886940000039
is the unit area input cost of the ith land utilization type in the s sub-area.
8. A land use pattern prediction and optimization method considering non-point source pollution control according to claim 7, characterized in that: the constraint conditions of the interval fuzzy linear programming model comprise non-point source pollution load constraint, grain yield constraint, land utilization area supply quantity constraint and non-negative constraint;
a non-point source pollution load constraint calculation formula:
Figure FDA0003349886940000041
Figure FDA0003349886940000042
is the r-class pollutant output coefficient;
Figure FDA0003349886940000043
is the total load of the r-class contaminants;
grain yield constraint calculation formula:
Figure FDA0003349886940000044
Figure FDA0003349886940000045
is the yield of grain crops per unit area; OGP±Is the total yield of the grain crops.
Land utilization area supply quantity constraint calculation formula:
Figure FDA0003349886940000046
Figure FDA0003349886940000047
MIAisand MAAisMinimum and maximum deliverables of areas of different land use types, respectively, the values being determined by land use area prediction results and 2020 land use status quo areas; TA (TA)±Is the total area of the study area;
non-negative constraint calculation formula:
Figure FDA0003349886940000048
Figure FDA0003349886940000049
is the area of the ith land utilization type in the s sub-zone.
9. A land use pattern prediction and optimization method considering non-point source pollution control as claimed in claim 8, wherein: solving an interval fuzzy linear programming model through a two-step interactive algorithm, and obtaining an optimization scheme and a decision suggestion of regional land use patterns under different scenes; fuzzy parameters in a model
Figure FDA00033498869400000410
Determined by triangular fuzzy membership function, and calculating triangular fuzzy number
Figure FDA00033498869400000411
Where b is the most likely value and a and c are the minimum and maximum possible values, respectively. The technical proposal converts the fuzzy parameter into an interval value by introducing an alpha intercept set,
Figure FDA00033498869400000412
α -cut of (c):
A(α)＝[ALα)，ARα)]，0≤α≤1
AL(α) ═ a + (b-a) × α is the left end of the α -cut; a. theRAnd (α) ═ c- (c-b) × α is the right end of the α -cut.
10. A land use pattern prediction and optimization method considering non-point source pollution control as claimed in claim 9, wherein: calculating a relative error between the dynamic model of the land use change system and historical data, and checking the accuracy of the dynamic model of the constructed land use change system, wherein a calculation formula of the relative error is as follows:
Figure FDA0003349886940000051
re is the relative error between the simulated area and the actual area; x is the number ofsThe simulation area of a system dynamics model to a certain land class in a certain year; x is the number ofhIs the historical actual area corresponding to the land type and year.
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