CN113807409B - Coastal zone classification method based on discriminant analysis - Google Patents
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Abstract
The invention discloses a coastal zone classification method based on discriminant analysis, which belongs to the technical field of meteorological information. Distinguishing and classifying the selected characteristic climate factors of the coastal zone by utilizing a multivariate statistical analysis method, taking the climate factors with the coastal zone characteristic information of the coastal zone as classification objects, and dynamically quantifying the coastal zone classification by utilizing the historical period and the future predicted climate factor information; the method has the advantages that the weather factors with different precision are referred to, the classification of the coastal zone under the condition of different precision is obtained, the technical problems of low precision and accuracy, difficulty in quantification and the like in the existing classification process of the coastal zone are solved, and the classification result with high precision and good accuracy can be obtained. Meanwhile, the comprehensive classification of the coastal zone is improved, and the uncertainty risk in the classification process is reduced; after data is extracted, a classification result with a prejudgement rate of less than 5% is obtained after the trained data is subjected to multi-element statistical relationship, and the obtained classification is credible. The verification result has higher accuracy and lower misjudgment rate.
Description
Technical Field
The invention belongs to the technical field of meteorological information, and particularly relates to a coastal zone classification method based on discriminant analysis.
Background
Coastal zone, generally refers to a zone where the ocean edge extends to the land within a certain range, and the earth's surface layer, the sea, the gas, and the living multi-turn layer interact within that range, creating a coupling effect. It is broadly defined as the inter-tidal zone affected by tidal fluctuation seawater every day and the land-sea transition zone of the land and shallow sea land frames of a range on both sides thereof. The coastal zone has a high degree of dynamics, complexity and diversity due to the region being deeply affected by the joint effects of climate change and human activity. Meanwhile, the coastal zone is also an ecological system with high productivity, high economic value and special property.
The first step in studying the change in influence of the coastal zone is to separate this particular region from the land and sea systems. However, during development of the coastal zone, wave, tide, crustal movement, rock properties, river-access, and intertidal zone organisms all affect the formation and development of the coastal zone. In this regard, classification of coastal zones using a single factor does not result in a relatively comprehensive classification result. Due to complex terrain and confusion of ground objects, the related features of the coastal zone are difficult to extract, and the coastal zone is difficult to quantitatively, specifically and accurately classify. The existing coastal zone classification technology is mainly based on classification of experience and field investigation, remote sensing data and an image classification system. The conventional classification method is mainly to judge the approximate range of the intertidal zone, the continental shelf and the continental section according to experience judgment and field investigation. In 6 months 2001, the thousand years ecological system assessment project defines the coastal zone as an interface between ocean and land, extends to the ocean to the middle of a continental shelf, and comprises all areas affected by ocean factors in the continental direction; specifically, the area between 50 meters of average sea depth and 50 meters above tide lines or the low land extending from coast to continent within 100 km range comprises coral reefs, areas between high tide lines and low tide lines, estuaries, coastal aquatic work areas and aquatic community. In addition, in the field of integrated management of coastal zones, the definition needs to be determined according to the management purpose and the research needs. The method is fuzzy in classification, is only based on experience and on-site visit, does not quantitatively classify coastal zones, and is difficult to ensure in accuracy. On the other hand, the existing coastal zone classification technology based on remote sensing data and images adopts land utilization grid data and vector data, so that coastal zones can be classified according to different land utilization categories. Typically classified according to land resource utilization purposes. The method can divide the coastal zone into very fine land types, but only can obtain static classification, meanwhile, the considered classification factors are not very complete, no climatic factors and indexes exist, dynamic, comprehensive and coupled classification cannot be carried out under the climatic change background, and the precision and accuracy in the classification process of the coastal zone are low and difficult to quantify; therefore, the precision and accuracy of the obtained classification result need to be further improved.
In addition, in the field of integrated management of coastal zones, the definition needs to be determined according to the management purpose and the research needs. The method is fuzzy in classification, is only based on experience and on-site visit, does not quantitatively classify coastal zones, and is difficult to ensure in accuracy. In addition, the existing coastal zone classification technology based on remote sensing data and images adopts land utilization grid data and vector data, so that coastal zones can be classified according to different land utilization categories. Typically classified according to land resource utilization purposes. The method can divide the coastal zone into very fine land types, but is classified from the viewpoint of human development and utilization, and does not divide the coastal zone into the coastal zone categories according to natural characteristics inherent to the coastal zone. Meanwhile, the climate change leads to significant changes in sea-related factors such as sea level, waves, storm surge, rainfall, etc. The classification of the coastal zone may be more fully performed from a climate factor perspective, and it is considered feasible to apply such classification to the ocean itself and to changes in the sea Liu Jiaohu system in the context of future climate change. Such a classification of coastal dynamics related to climate change has not been studied so far.
Disclosure of Invention
The invention aims to provide a coastal zone classification method based on discriminant analysis, which is characterized in that a multi-element statistical analysis method based on discriminant analysis is utilized to carry out discriminant classification on selected characteristic climate factors of the coastal zone, the climate factors with coastal zone and land characteristic information are adopted as classification objects, and historical period and future predicted climate factor information are utilized to dynamically quantify the coastal zone classification; the weather factors with different precision are referred to, so that the coastal zone classification under the conditions of different precision is obtained, and the classification result with high precision and good accuracy can be obtained; the method specifically comprises the following steps:
step 1, global climate model data in a typical continental climate target area and a marine climate target area are obtained, and the selected areas are dynamically divided into a marine area, a land area and a coastal zone area according to climate factors;
step 2, acquiring global climate model data of 200 km distances from the left side and the right side of a coastline in an interaction area of the coastal zone, and extracting climate factors with coastal zone sea-land characteristic information;
dividing climate factor data in a history period into two parts of training data and verification data aiming at the three target areas selected in the step 1;
step 3, setting a discrimination type:
judging the type I, and setting a datum line of continental category classification according to global climate model data in a typical continental climate target area;
judging the second type, and setting a datum line of ocean class classification according to global climate model data in a typical ocean climate target area;
judging the type III, and setting a datum line for classifying the coastal zone interaction region according to global climate model data in the coastal zone interaction target region;
step 4, training and establishing a multi-element statistical relationship by utilizing three types of weather factor training data, and performing a process of establishing the discrimination relationship based on a discrimination analysis method to obtain a discrimination function; verifying the actual position in the historical period by using verification data, and calculating the misjudgment rate;
step 5, aiming at the climate factor data in the coastal zone interaction target area, taking the data of the forthcoming fifty years as input quantity, and classifying the climate factor data into the category of the area in the corresponding time period in the future through discrimination and statistics, namely land type, ocean type or coastal zone interaction type;
step 6, aiming at the weather factor data in the coastal zone interaction target area and the performance under the condition of different time scales, the method further comprises the steps of adopting the same spatial resolution, selecting the weather factor data in the selected area, selecting the data of different time steps of years, ten years and fifty years, obtaining a plurality of sets of weather factor data of the time scales of the area, and judging whether the category of the target area has obvious change under the different time scales;
step 7, providing a data preprocessing method for climate factor data in a coastal zone interaction target area, wherein the method further comprises the steps of (1) selecting at least three global climate models containing selected parameter data: the same spatial resolution and the same geographic and meteorological parameter data are adopted; (2) And after selecting a model with better simulation performance in the selected region, inputting the result obtained by the set forecast into a discrimination statistical relationship to obtain a set classification result.
The extracted climate factor data is required to be consistent with the choice of the coastal zone group.
The climatic factors of the sea-land characteristic information of the coastal zone comprise boundary height layers and evapotranspiration occurs.
The prediction of the historical period can be used for verifying the classification result and can also be used for comparing with the future classification result; by the change of the correlation factors associated with the weather, the history and future trend of the change of the coastal zone type can be observed.
The method has the beneficial effects that the method utilizes a statistical method to intuitively classify the weather factors with the sea-land characteristic information of the coastal zone in two periods of history and future, and does not need to carry out fuzzy classification through on-site visit or depending on experience; the method has the advantages that the dynamic division is carried out on the coastal zone, so that the technical problems of low precision, low accuracy, difficult quantification and the like in the existing classification process of the coastal zone are solved, meanwhile, the classification comprehensiveness of the coastal zone is improved, and the uncertainty risk in the classification process is reduced; after data is extracted, a classification result with a prejudgement rate of less than 5% is obtained after the trained data is subjected to multi-element statistical relationship, and the obtained classification is credible. The verification result has higher accuracy and lower misjudgment rate.
Drawings
Fig. 1 is a schematic flow chart of coastal zone classification based on discriminant analysis.
Fig. 2 is a schematic flow chart of a coastal zone classification method based on discriminant analysis.
Fig. 3 is a schematic flow chart of a coastal zone classification method based on discriminant analysis.
Detailed Description
The invention provides a coastal zone classification method based on discriminant analysis, which uses a multivariate statistical analysis method to perform discriminant analysis on selected characteristic climate factors of the coastal zone, uses the climate factors with coastal zone sea-land characteristic information as classification objects, and dynamically quantifies the coastal zone classification by using historical period and future predicted climate factor information; the weather factors with different precision are referred to, the coastal zone classification under the conditions of different precision is obtained, and the classification result with high precision and good accuracy can be obtained. The invention is described below with reference to the drawings and examples.
Coastal zone, generally refers to a zone where the ocean edge extends to the land within a certain range, and the earth's surface layer, the sea, the gas, and the living multi-turn layer interact within that range, creating a coupling effect. The sea-land transition zone is broadly defined as the inter-tidal zone affected by tidal fluctuation of the sea and the land and shallow sea land frames on both sides of the inter-tidal zone, which can reach tens of meters to tens of kilometers in width, and is generally divided into an upper zone, a middle zone (inter-tidal zone) and a lower zone. The region can be divided into five parts of coast, shore, inner shore, outer shore and offshore zone. In addition, since the coastal zone has superior natural conditions, human activities are most concentrated in the zone. The world coastline is about 44 kilometers long, and about two thirds of large cities with more than 250 tens of thousands of world population are located near the coastline, and more than half of the population lives within 60 kilometers of the coastline. The area is thus deeply affected by the joint effects of human activity and the accompanying climate change. The interaction, coordination and coupling effects of the coastal zone regions are thoroughly researched, and the method has important significance for comprehensively knowing the influence of climate change and the sustainable development of human economy and society. The present invention will be described in detail below with reference to the drawings and examples.
Fig. 1 is a schematic flow chart for discriminant analysis of classification of coastal zones. The method shown in fig. 1 specifically comprises the following steps:
according to certain space geographic distribution characteristics, acquiring global climate model data related to a coastal zone in a typical continental climate target area, a typical marine climate target area and a target area (a range within 200 km from a coastline) in a coastal zone interaction area, wherein the global climate model data comprises climate factors with coastal zone sea-land characteristic information, typical terrestrial climate data and typical marine climate data;
the global climate model data comprise longitude, latitude, elevation, potential height, longitude and latitude wind, atmosphere boundary layer height, evaporation and the like, and the parameters selected by the three types of areas are the same.
Specifically, the acquired data is month scale data. It should be understood that, unless otherwise specified, the atmospheric boundary layer height data is the month scale data in this example.
The two obtained data are classified after being sorted according to time, wherein the former part is used as training data, and the other part is used as verification data.
Specifically, the extracted atmospheric boundary layer height data are divided into training data and 10-15 years data as verification data according to 15-20 years data for classification; the training data is used for establishing a model relationship, and the verification data is used for verifying whether the established discriminant relationship is reliable.
Alternatively, the length of time that the data is classified may be case-specific, with the training data generally spanning longer than the verification data.
The atmospheric boundary layer height data within a typical continental climate target zone is classified as discrimination type one. Accordingly, classifying the atmospheric boundary layer height data in the typical marine climate target zone as discrimination type two; and classifying the atmospheric boundary layer height data in the coastal zone interaction target area into a discrimination type III.
Specifically, the first discrimination type is a datum line of continental classification, the second discrimination type is a datum line of ocean classification, and the third discrimination type is a datum line of coastal zone interaction region classification.
The discriminant analysis (Discriminant Analysis) is a multivariate statistical analysis method for discriminating the type of the attribution problem according to various characteristic values of a certain study object under the condition of classification and determination. The method establishes a discriminant function according to a certain discriminant criterion, uses a sample to judge which category belongs to, and calculates the misjudgment rate.
The correlation distribution P (x|y=k) of the conventional linear discriminant analysis model for each class k can be obtained by bayesian theorem:
wherein: k is a category to be determined, X is a known category, l is a category obtained by dividing X into l categories, P (X) is a probability of occurrence of the known category, P (x|y=k) is a probability of occurrence of X under a condition that k occurs, and P (x|y=l) is a probability of occurrence of X under a condition that l occurs.
During the discrimination process, the samples are classified into class k that maximizes the conditional probability. That is, for linear discriminant analysis, P (x|y=k) is modeled as a density multi-variance gaussian distribution:
wherein: k is the class, μ is the mean variance, and d is the dimension.
Aiming at the three types of weather factor training data, based on a discriminant analysis method, carrying out a classification process to find out multi-element statistical relationship verification data between the height of an atmospheric boundary layer and the three types of weather factor training data; and verifying the actual position in the historical period and calculating the misjudgment rate system.
Specifically, the sample area is classified into a land type, a sea type, and a coastal zone interaction type according to three types known at present.
Aiming at the climate factor data in the coastal zone interaction target area, the data of the next fifty years are used as input quantity, and the category of the area in the corresponding time interval in the future is obtained through the established discriminant statistical relationship, namely the land type, the ocean type or the coastal zone interaction type.
Specifically, future estimated data of the global climate model is selected, and research on land type change of the future coastal zone area is conducted based on the established statistical relationship.
Fig. 2 is a schematic flow chart of a coastal zone classification method based on discriminant analysis. The embodiment is based on the embodiment shown in fig. 1, and describes in detail the simulation operation based on different time scales; as shown in fig. 2, data on longer time scales (years, ten years, fifty years, etc.) were obtained using the average method and subjected to discriminant analysis.
The average value method processes the atmospheric boundary layer height data of the target area to obtain data of different time scales including years, ten years, fifty years and the like, and the method comprises the following steps:
typically, month scale data of the atmospheric boundary layer height is acquired. And processing the target region by adopting an average value method based on the same spatial resolution to obtain a plurality of time scale atmosphere boundary layer height data of the target region.
Specifically, taking month scale data processing as year scale data as an example, it is necessary to calculate a weighted average of data points of all months in the current year as year average data in the current year. The annual average data obtained by the average method represent the average level of the height of the boundary layer of the atmosphere in the year, and can reflect the general rule under the annual scale.
Alternatively, when the atmospheric boundary layer height data in the target area is processed to ten years, fifty years, or the like, the basic principle of the processing is consistent with the processing to the annual scale from the monthly scale. The average value method can be used for averaging data, and can be used for analysis to obtain general rules of ten-year scale, century scale and the like.
The remaining steps in this embodiment are the same as those in the embodiment of fig. 1, and will not be described again here.
The embodiment provides a method for processing original data under different time scales. The average value data of the atmospheric boundary layer height can be obtained by using an average value method and used for representing the corresponding boundary layer height data under a certain time scale, and can be used for researching a general rule. The method for processing the original data into other multiple time scales is provided on the basis of the method, and a reliable thought is provided for researching the coastal zone type change under different time scale conditions.
Fig. 3 is a schematic flow chart of a coastal zone classification method based on discriminant analysis. Based on the embodiment shown in fig. 1, at least three (and more) global climate models containing the selected parameter data are selected, future coastal zone classification change research is performed, and the result of global climate model set prediction is adopted to obtain more reliable and real classification. The classification result after the collection forecasting treatment adopted by the embodiment is more authentic and reliable.
Specifically, this example is exemplified by HadCM3 developed by the Hadley center of the weather department of England, GFDL-CM2.1 of the national marine and atmospheric administration, and CanCM4 of the North America multimode collection forecast dataset. The historical period (1980-2020) and future period (2021-2099) of each model are extracted, geographical and meteorological parameter data of a target area are collected and forecast, and the obtained result is used as an input value of a multivariate statistical relationship obtained by discriminant analysis, so that a classification result is obtained.
Alternatively, the method of aggregate forecast may be selected from, but not limited to, any one of the following methods: monte carlo method (Monte Carlo method, MCF), singular vector method (SVs), conditional nonlinear optimal perturbation method (Conditional nonlinear optimal perturbation, CNOP), mean method, etc. Preferably, the present invention is not based on the development of the aggregate forecasting method, and because the method is simple and rapid and can obtain better results, the method of the present embodiment adopts the mean value method to conduct aggregate forecasting, and the other methods will not be described again.
The remaining steps in this embodiment are the same as those in the embodiment of fig. 1, and will not be described again here.
The present embodiment provides a method of data preprocessing. At least three sets of data with good performance and same parameters are selected for the target area, and comprehensive classification results are obtained after the set forecast analysis. The embodiment improves the prediction accuracy and reduces the uncertainty on the basis of the method, and is a thought of data post-processing, so that a classification result with high precision and good accuracy can be obtained.
Claims (4)
1. A coastal zone classification method based on discriminant analysis is characterized in that a multi-element statistical analysis method based on discriminant analysis is utilized to perform discriminant classification on selected coastal zone characteristic climate factors, the climate factors with coastal zone sea-land characteristic information are adopted as classification objects, and historical period and future predicted climate factor information are utilized to dynamically quantify coastal zone classification; the weather factors with different precision are referred to, so that the coastal zone classification under the conditions of different precision is obtained, and the classification result with high precision and good accuracy can be obtained; the method specifically comprises the following steps:
step 1, global climate model data in a typical continental climate target area and a marine climate target area are obtained, and the selected areas are dynamically divided into a marine area, a land area and a coastal zone area according to climate factors;
step 2, acquiring global climate model data of 200 km distances from the left side and the right side of a coastline in an interaction area of the coastal zone, and extracting climate factors with coastal zone sea-land characteristic information;
dividing climate factor data in a history period into two parts of training data and verification data aiming at the three target areas selected in the step 1;
step 3, setting a discrimination type:
judging the type I, and setting a datum line of continental category classification according to global climate model data in a typical continental climate target area;
judging the second type, and setting a datum line of ocean class classification according to global climate model data in a typical ocean climate target area;
judging the type III, and setting a datum line for classifying the coastal zone interaction region according to global climate model data in the coastal zone interaction target region;
step 4, training and establishing a multi-element statistical relationship by utilizing three types of weather factor training data, and performing a process of establishing the discrimination relationship based on a discrimination analysis method to obtain a discrimination function; verifying the actual position in the historical period by using verification data, and calculating the misjudgment rate;
step 5, aiming at the climate factor data in the coastal zone interaction target area, taking the data of the forthcoming fifty years as input quantity, and classifying the climate factor data into the category of the area in the corresponding time period in the future through discrimination and statistics, namely land type, ocean type or coastal zone interaction type;
step 6, aiming at the weather factor data in the coastal zone interaction target area and the performance under the condition of different time scales, the method further comprises the steps of adopting the same spatial resolution, selecting the weather factor data in the selected area, selecting the data of different time steps of years, ten years and fifty years, obtaining a plurality of sets of weather factor data of the time scales of the area, and judging whether the category of the target area has obvious change under the different time scales;
step 7, providing a data preprocessing method for climate factor data in a coastal zone interaction target area, wherein the method further comprises the steps of (1) selecting at least three global climate models containing selected parameter data: the same spatial resolution and the same geographic and meteorological parameter data are adopted; (2) And after selecting a model with better simulation performance in the selected region, inputting the result obtained by the set forecast into a discrimination statistical relationship to obtain a set classification result.
2. The method for classifying a coastal zone based on discriminant analysis of claim 1, wherein the extracted climate factor data is selected to be consistent with the coastal zone.
3. The method for classifying a coastal zone based on discriminant analysis according to claim 1, wherein the climatic factors of the coastal zone sea-land characteristic information include boundary height layers, and the evapotranspiration occurs.
4. The method for classifying a coastal zone based on discriminant analysis of claim 1, wherein the prediction of the historical period can be used to verify the classification results and can also be used to compare with future classification results; by means of the change of weather factors with the characteristic information of the sea and land of the coastal zone in weather connection, the history and future change trend of the coastal zone type are observed.
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