CN112183607A - Southeast coastal region flood classification method based on fuzzy theory - Google Patents
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Abstract
The invention discloses a fuzzy theory-based southeast coastal region flood classification method, aiming at improving the objectivity and accuracy of the southeast coastal region flood classification. The method comprises the following steps: 1) collecting rainfall and runoff data of a research area, and processing the data; 2) identifying and extracting the flood in the research period by using an automatic flood extraction method; 3) selecting flood indexes for flood classification; 4) and classifying the extracted field flood by using a fuzzy theory-based method for classifying the flood in the southeast coastal region according to flood indexes. The fuzzy theory-based flood classification method considers the uncertainty of the flood process identification, namely, one flood can be a product combining multiple flood types, so that the flood classification result based on the fuzzy theory is more objective and more accurate.
Description
Technical Field
The invention relates to the fields of flood forecasting, disaster prevention and reduction, in particular to a method for classifying floods in a southeast coastal region based on a fuzzy theory.
Background
Flood is one of the most serious natural disasters in the world. Along with the rapid development of world economy and the aggravation of climate change, natural disasters such as flood occur more and more frequently, the development of social economy and the safety of lives and properties of people are seriously threatened, and the accurate simulation of the flood process by using a hydrological model is an important prerequisite for flood forecasting, disaster prevention and reduction.
Improving knowledge of the flood process is a necessary condition for accurately simulating and forecasting the flood process. Different flood types have different flood characteristics. Flood classification is a prerequisite for accurately simulating and forecasting different types of flood processes. Therefore, flood classification has very important significance for management, evaluation, decision and the like of flood risks. The watershed hydrological process is complicated and complicated, and a certain flood may be composed of multiple flood types. Therefore, the absolute flood classification method is not objective enough.
Disclosure of Invention
In order to solve the problem that the flood classification method is not objective enough, the invention aims to provide the method for classifying the flood in the southeast coastal region based on the fuzzy theory so as to improve the objectivity and the accuracy of the flood classification.
In order to achieve the aim, the invention adopts the following technical scheme:
a method for classifying floods in southeast coastal areas based on a fuzzy theory comprises the following steps:
1) collecting rainfall and runoff data of a research area, and processing the data; direct elimination of rainfall data missing for more than 5 time steps; aiming at the condition that rainfall data is missing within 5 time steps, reverse distance interpolation completion can be carried out through an adjacent meteorological station, and the rainfall of the research area surface is calculated by using a Thiessen polygon; the time step can be selected according to actual conditions, and the shortest time interval can be selected as the time step according to the existing data;
2) identifying and extracting the flood in the research period by using an automatic flood extraction method, and numbering each flood according to the initial time (YYYMMDD) of the flood;
3) determining flood indexes for flood classification, wherein the flood indexes comprise the season of flood occurrence, the duration of rainfall, rainfall intensity, basin wettability and the like;
4) and (3) according to flood indexes, carrying out flood classification on the flood field extracted in the step 2) by using a fuzzy theory-based method for classifying the flood in the southeast coastal region, and obtaining a flood classification result of the research area.
In the above technical solution, further, the fuzzy theory has the following specific meaning: the fuzzy is an exact opposite surface, namely, the object is considered to have uncertainty, and the object is quantitatively expressed by adopting the membership degree.
Furthermore, the method is used for classifying the floods in the southeast coastal region, the classification method is based on the flood generation process, and the similarity of relevant indexes of the hydrological process rather than observed data is emphasized. The method not only focuses on dynamic changes of the watershed and weather input, but also does not preset in advance the possibility of which process occurs.
Because the southeast coastal region has less snowfall, the method separates the southeast coastal region flood into three process-related types, including Fast Flood (FF), Short duration rainfall flood (SRF), and Long duration rainfall flood (LRF). Fast Floods (FF) occur mainly in typhoon seasons, due to short duration, high intensity rainfall; the flood type is usually local, so such floods mostly occur in small or sub-basins. Short duration rainfall floods (SRF) are due to short duration, high intensity rainfall, which is distinguished from fast flooding in that this type involves a more extensive, regional or larger range of local floods for a relatively long duration. Long-term rainfall floods (LRFs), which are often area-wide or all-over watershed, are caused by low intensity rainfall lasting several days or weeks.
Further, the automatic field flood extraction method in the step 2) specifically comprises the following steps:
(1) finding peak flow (Q) in runoff seriesmax)；
(2) Determining flood start (stop) time, i.e. runoff in the current (latter) time step is below a flow threshold (Q)0)；
(3) And when rainfall exists corresponding to the flood starting time, moving the starting time forward to the moment without rainfall. The flow threshold is calculated as follows:
Q0＝max(0.25·Qmax,Qmin+0.05·(Qmax-Qmin))
wherein QmaxRepresenting peak flow, QminAnd the lowest flow rate of ten days before (after) the flood peak basin is represented and is used for calculating a flow rate threshold value for determining the starting and stopping time of flood.
(4) The flood of the field extracted according to the above process can not be used if the flood meets the following certain condition that the flow rate is increased or decreased for less than three hours; if the flood period of a certain field overlaps with the previous field, only one field is selected; thirdly, if the total rainfall amount before the peak flood flow appears is less than 2.5 mm; if the extracted flood runoff of the field is basically kept unchanged (the maximum runoff difference value corresponding to two adjacent time steps is less than 10% according to the following two limiting conditions, and the flood peak runoff is less than 1.3 times of the average runoff); if there is data missing before and after flood peak, which lasts more than 10% of flood time, etc.
Further, the flood index adopted in step 3) specifically includes:
(1) season of flood occurrence: namely the stage of flood occurrence, generally defining 5 months and 1 day to 9 months and 30 days as an interval which is full of rainfall and comprises plum rainy seasons and typhoon seasons;
(2) and (3) rainfall for a period of time: the rainfall time from one moment to another in the process of one rainfall, wherein the unit of day is used as the rainfall time;
(3) rainfall intensity: namely the rainfall in unit time, and the unit is mm/h;
(4) basin wettability: namely the wetting degree of the watershed, can be obtained by calculation through a hydrological model, and is dimensionless;
(5) other flood indicators, etc.;
the user can reasonably change the selection and the threshold value of the flood index according to the specific research area and the research purpose.
Further, the flood classification method in the step 4) is a decision tree method, wherein the decision tree method is a method for analyzing by using a tree diagram as an analysis tool and adopting a top-down recursive mode; the method comprises the following specific steps:
the decision tree method for flood classification is characterized in that attribute values (index intervals) are compared at internal nodes (flood indexes) of the decision tree, downward branches from the nodes are judged according to comparison results, and results are finally obtained at leaf nodes (flood types) of the decision tree.
Further, the step 4) is specifically: setting the attribute value of the flood index into a soft interval, namely adding a threshold value (Thr) and a variation interval (Thr +/-R); for the flood season, R is 10 days; for other flood indicators, R is 25% of the threshold Thr; the range of the membership degree is [0,1], and the flood index value and the membership degree are linearly changed in the interval; when the flood index value is equal to the threshold value (Thr), the membership degree is 0.5; when the flood index value is more than or equal to Thr + R, the membership degree is 1.0; when the flood index value is less than or equal to Thr-R, the membership degree is 0; recursion is carried out from top to bottom to finish comparison of all internal nodes and obtain the membership degree of each internal node; fourthly, the membership degree of a certain field of flood to a certain leaf node (flood type) is the multiplication of the membership degrees of all the internal nodes on the path; adding the membership degrees of all the same flood types (the same flood type may have a plurality of leaf nodes), namely the final membership degree of the flood of the field to a certain flood type; judging the flood type of the flood according to the membership degree of the flood of a certain field to the three flood types; the type with the highest degree of membership is the final type of flood for the field.
The invention has the beneficial effects that:
(1) by introducing the fuzzy theory into the flood classification method, the uncertainty in the flood classification process is considered, and the objectivity and the accuracy of flood classification are improved.
(2) By adopting the method, more accurate flood classification results can be obtained, and the more accurate flood classification results are beneficial to more accurately simulating different flood processes, and have very important significance on aspects of flood risk management, evaluation and the like.
Drawings
FIG. 1 is a flood classification decision tree;
FIG. 2 is a diagram of soft threshold and membership assignment;
FIG. 3 is a result of field flood membership based on fuzzy theory.
Detailed Description
The invention is further described with reference to the following figures and detailed description. In order to highlight the advantages of the invention, the Jinhuajiang river basin in Zhejiang province, coastal in southeast is taken as a case for specific implementation.
As shown in fig. 1 and fig. 2, the method for classifying the floods in the southeast coastal region based on the fuzzy theory of the present invention includes the following steps:
collecting rainfall data of a meteorological station in the small scale of the Jinhua river basin and runoff data of a Jinhua hydrological station, checking for leakage and filling up the data, and filling up the missing of a small amount of rainfall data through reverse distance interpolation of an adjacent meteorological station. And calculating the surface rainfall of the Jinhuajiang river basin by using the Thiessen polygon according to the rainfall data of the weather station.
All flood times in the research period 04/12/2006-07/01/2013 are identified and extracted by using an automatic flood time extraction method, each flood time is numbered according to the initial time (YYYMMDD) of the flood, and the extraction result shows that 36 flood times are extracted in the research period.
The specific process of the automatic field flood extraction method is as follows:
(1) finding peak flow (Q) in runoff seriesmax)；
(2) Determining flood start (stop) time, i.e. runoff in the current (latter) time step is below a flow threshold (Q)0)；
(3) And when rainfall exists corresponding to the flood starting time, moving the starting time forward to the moment without rainfall. The flow threshold is calculated as follows:
Q0＝max(0.25·Qmax,Qmin+0.05·(Qmax-Qmin))
wherein QmaxRepresenting peak flow, QminAnd the lowest flow rate of ten days before (after) the flood peak basin is represented and is used for calculating a flow rate threshold value for determining the starting and stopping time of flood.
(4) The flood of the field extracted according to the above process can not be used if the flood meets the following certain condition that the flow rate is increased or decreased for less than three hours; if the flood generation time interval of a certain field is overlapped with the previous field, only one field is selected; thirdly, if the total rainfall amount before the peak flood flow appears is less than 2.5 mm; if the extracted flood runoff of the field is basically kept unchanged (the maximum runoff difference value corresponding to two adjacent time steps is less than 10% according to the following two limiting conditions, and the flood peak runoff is less than 1.3 times of the average runoff); if there is data missing before and after flood peak, which lasts more than 10% of flood time, etc.
Determining and calculating an index for flood classification, specifically comprising: the flood season: namely the stage of flood occurrence, usually defining 5 months and 1 day to 9 months and 30 days as an interval which is full of rainfall and basically comprises plum rainy seasons and typhoon seasons in the Jinhua river basin; secondly, rainfall duration: the rainfall time from one moment to another in the process of one rainfall takes days as a unit; ③ intensity of rainfall: namely the rainfall in unit time, and the unit is mm/h; basin wettability: namely the wetting degree of the watershed, can be obtained by calculation through a hydrological model, and is dimensionless.
According to the extracted indexes of 36 floods, a fuzzy theory-based method for classifying the floods in the southeast coastal region is used for distributing membership degrees, firstly, a soft threshold value is set for the attribute value of the flood index, namely the threshold value (Thr) is added with a variation interval (Thr +/-R); for the flood season, R is 10 days; for other flood indicators, R is 25% of the threshold Thr; the range of the membership degree is [0,1], and the flood index value and the membership degree are linearly changed in the interval; when the flood index value is equal to the threshold value (Thr), the membership degree is 0.5; when the flood index value is more than or equal to Thr + R, the membership degree is 1.0; when the flood index value is less than or equal to Thr-R, the membership degree is 0; recursion is carried out from top to bottom to finish comparison of all internal nodes and obtain the membership degree of each internal node; fourthly, the membership degree of a certain field of flood to a certain leaf node (flood type) is the multiplication of the membership degrees of all the internal nodes on the path; adding the membership degrees of all the same flood types (the same flood type may have a plurality of leaf nodes), namely the final membership degree of the flood of the field to a certain flood type; judging the flood type of the flood according to the membership degree of the flood of a certain field to the three flood types; the type with the highest degree of membership is the final type of flood for the field.
As shown in fig. 3, it can be seen that half of the floods in the field are classified as mixed flood types, that is, the mixed flood types are composed of a plurality of flood types, which shows that the method for classifying the floods in the southeast coastal region based on the fuzzy theory considers the uncertainty in the process of classifying the floods practically, and the classification result is more objective and accurate.
The foregoing description is of exemplary embodiments of the invention only and is not intended to limit the invention, which may be modified or varied by those skilled in the art. All changes, equivalents, modifications and the like which come within the scope of the invention as defined by the appended claims are intended to be embraced therein.
Claims (7)
1. A method for classifying floods in a southeast coastal region based on a fuzzy theory is characterized by comprising the following steps:
1) collecting rainfall and runoff data of a research area, and processing the data;
2) identifying and extracting the flood in the research period by using an automatic flood extraction method, and numbering each flood according to the initial time of the flood;
3) selecting flood indexes for flood classification, wherein the flood indexes comprise the season of flood occurrence, the duration of rainfall, rainfall intensity and basin wettability;
4) and (3) according to flood indexes, carrying out flood classification on the flood field extracted in the step 2) by using a fuzzy theory-based method for classifying the flood in the southeast coastal region, and obtaining a flood classification result of the research area.
2. The method for classifying the floods in the southeast coastal region based on the fuzzy theory as claimed in claim 1, wherein the fuzzy theory considers that the things have uncertainty, and the objects are quantitatively expressed by using the membership degree.
3. The fuzzy-theory-based southeast coastal region flood classification method according to claim 1, wherein the method classifies southeast coastal region floods into three process-related floods, including fast flood FF, short-duration rainfall flood SRF, and long-duration rainfall flood LRF.
4. The fuzzy theory-based southeast coastal region flood classification method according to claim 1, wherein the automatic flood extraction method adopted in the step 2) comprises the following specific steps:
(1) finding peak flow Q in runoff sequencemax；
(2) Determining flood start/stop times, i.e. runoff in current/next time step is below a flow threshold Q0；
(3) When rainfall is present corresponding to the flood starting time, the starting time is moved forward to the moment without rainfall, and the calculation formula of the flow threshold value is as follows:
Q0＝max(0.25·Qmax,Qmin+0.05·(Qmax-Qmin))
wherein QmaxRepresenting peak flow, QminThe minimum flow rate of ten days before/after the flood peak basin is represented and used for calculating and determining the flow rate threshold value of the flood starting and stopping time;
(4) the flood of the field extracted according to the above process can not be used if the flood meets the following certain condition that the flow rate is increased or decreased for less than three hours; if the flood period of a certain field overlaps with the previous field, only one field is selected; thirdly, if the total rainfall amount before the peak flood flow appears is less than 2.5 mm; fourthly, if the extracted flood runoff of the field is basically kept unchanged, judging according to the following two limiting conditions: one is that the difference value of the maximum runoff corresponding to two adjacent time steps is less than 10 percent, and the other is that the peak runoff is less than 1.3 times of the average runoff; if there is data missing before and after flood peak, the flood lasts more than 10%.
5. The fuzzy theory-based southeast coastal region flood classification method according to claim 1, wherein the flood indexes adopted in the step 3) specifically include:
(1) season of flood occurrence: namely, the stage of flood occurrence, defining a section from 1 day in 5 months to 30 days in 9 months, wherein the section is rich in rainfall and comprises plum rainy seasons and typhoon seasons;
(2) and (3) rainfall for a period of time: the rainfall time from one moment to another in the process of one rainfall takes days as a unit;
(3) rainfall intensity: namely the rainfall in unit time, and the unit is mm/h;
(4) basin wettability: namely the wetting degree of the watershed, is obtained by calculation through a hydrological model and is dimensionless.
6. The fuzzy theory-based southeast coastal region flood classification method according to claim 1, wherein the flood classification method in the step 4) is a decision tree method, and the specific steps are as follows:
comparing attribute values of internal nodes of the decision tree, judging downward branches from the nodes according to comparison results, and finally obtaining results at leaf nodes of the decision tree; the internal nodes are flood indexes, the attribute values are index intervals, and the leaf nodes are flood types.
7. The method for classifying the floods in the southeast coastal region based on the fuzzy theory as claimed in claim 6, wherein the step 4) is specifically as follows:
(1) setting a soft interval for the attribute value of the flood index, namely adding a threshold value Thr to a variation interval Thr +/-R; for the flood season, R is 10 days; for other flood indicators, R is 25% of the threshold Thr;
(2) the range of the membership degree is [0,1], and the flood index value and the membership degree are linearly changed in the interval; when the flood index value is equal to the threshold Thr, the membership degree is 0.5; when the flood index value is more than or equal to Thr + R, the membership degree is 1.0; when the flood index value is less than or equal to Thr-R, the membership degree is 0;
(3) recursion is carried out from top to bottom to finish the comparison of all internal nodes and obtain the membership value of each internal node;
(4) the membership degree of a certain field of flood to a certain leaf node is the multiplication of the membership degrees of all internal nodes on the path; adding the membership degrees of all the same flood types to obtain the final membership degree of the flood of the field to a certain flood type; the sum of membership degrees of a certain flood to the three flood types is 1.0;
(5) according to the membership degree of a certain flood to three flood types, the flood type of the flood can be judged; the type with the highest degree of membership is the final type of flood for the field.
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