CN112183607B - Flood classification method for southeast coastal areas based on fuzzy theory - Google Patents
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Abstract
The invention discloses a flood classification method in southeast coastal areas based on a fuzzy theory, aiming at improving the objectivity and the accuracy of the flood classification in the southeast coastal areas. The method comprises the following steps: 1) Collecting rainfall and runoff data of a research area, and performing data processing; 2) Identifying and extracting the field flood in the research period by using an automatic field flood extraction method; 3) Selecting a flood indicator for flood classification; 4) And classifying the extracted field flood by using a flood classification method based on a fuzzy theory in the southeast coastal region according to flood indexes. The flood classification method based on the fuzzy theory considers the uncertainty existing in the flood process identification, namely, one flood is possibly the product of combination of a plurality of flood types, so that the flood classification result based on the fuzzy theory is more objective and accurate.
Description
Technical Field
The invention relates to the fields of flood forecasting, disaster prevention and reduction, in particular to a method for classifying flood in southeast coastal areas based on fuzzy theory.
Background
Flood is one of the most serious natural disasters in the world. With the rapid development of world economy and the aggravation of climate change, natural disasters such as flood and the like are more and more frequent, 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 hydrologic model is an important precondition for flood forecasting, disaster prevention and disaster reduction.
Improving awareness 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 precondition for accurately simulating and forecasting different types of flood processes. Thus, flood classification is of great importance for management, assessment, decision-making, etc. of flood risks. The watershed hydrologic process is intricate, and a certain scene of flood may be composed of a plurality of flood types. Therefore, absolute flood classification methods are 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 flood classification method based on the fuzzy theory in southeast coastal areas so as to improve the objectivity and accuracy of flood classification.
In order to achieve the above object, the present invention adopts the following technical scheme:
a flood classification method in southeast coastal areas based on fuzzy theory comprises the following steps:
1) Collecting rainfall and runoff data of a research area, and performing data processing; direct rejection of rainfall data missing more than 5 time steps; aiming at rainfall data loss within 5 time steps, the rainfall data loss can be complemented by reverse distance interpolation of adjacent weather stations, and the rainfall of the area is calculated by utilizing Thiessen polygons; 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 field floods in a research period by using an automatic field flood extraction method, and numbering each field flood according to the starting time (YYYMDD) of the floods;
3) Determining flood indicators for flood classification, including season, duration of rainfall, intensity of rainfall, and wettability of the basin, etc. of flood occurrence;
4) And (3) carrying out flood classification on the flood orders extracted in the step (2) by using a flood classification method based on a fuzzy theory in southeast coastal areas according to flood indexes, and obtaining a flood classification result of a research area.
In the above technical solution, further, the specific meaning of the fuzzy theory is: ambiguity is the exact opposite, i.e., what is considered to be uncertainty, expressed quantitatively by using membership.
Further, the method is used for classifying flood in coastal areas of southeast, and the classification method is based on flood occurrence process and focuses on hydrologic process related indexes rather than similarity of observed data. The method not only focuses on dynamic changes and meteorological inputs of the watershed, but also does not preset in advance the probability of which process occurs.
Since southeast coastal regions are less snowfall, this approach classifies southeast coastal region flood water into three process-related types, including Fast Flood (FF), short duration rainfall flood (Short rainfall flood, SRF) and long duration rainfall flood (Long rainfall flood, LRF). Rapid flooding (FF) occurs mainly in typhoons due to short duration, high intensity rainfall; the flood type is typically localized, so such floods mostly occur in small or sub-basins. Short duration rainfall floods (SRFs) are due to rainfall of a shorter duration and higher intensity, which are distinguished from fast floods in that such types involve a larger range, are regional or larger range of local floods, and are relatively long duration. Long duration rainfall floods (LRFs) are caused by low intensity rainfall lasting for days or weeks, such floods are typically regional-wide or basin-wide.
Further, the automatic extraction method of the field flood in the step 2) specifically comprises the following steps:
(1) Finding flood peak flow (Q) in runoff sequence max )；
(2) Determining flood start (stop) time, i.e. current (following) time step runoff below flow threshold (Q) 0 )；
(3) And when rainfall exists corresponding to the flood start time, the start time is moved forward to the moment without rainfall. The flow threshold is calculated as follows:
Q 0 ＝max(0.25·Q max ,Q min +0.05·(Q max -Q min ))
wherein Q is max Represents the flood peak flow, Q min Representing the minimum flow ten days before (after) the flood peak basin, for calculating a flow threshold that determines the start-stop time of the flood.
(4) The field flood extracted according to the above procedure is unusable if it meets a certain condition (1) if the duration of the flow rise or fall is less than three hours; (2) if the occurrence period of a certain field flood overlaps with the previous field, only one field is selected; (3) if the total amount of rainfall is less than 2.5 mm before peak flood occurs; (4) if the extracted field flood runoffs are basically unchanged (judged according to the following two limiting conditions, one is that the maximum runoff difference value corresponding to two adjacent time steps is less than 10 percent, and the other is that the flood peak runoffs are less than 1.3 times of the average runoffs); (5) data missing for more than 10% of its flood duration, etc. if there is a flood before and after the occurrence of the flood peak.
Further, the flood index adopted in the step 3) specifically includes:
(1) Season of flood occurrence: i.e. the stage in which the flood occurs, usually the period from 5 months 1 day to 9 months 30 is defined as a period with abundant rainfall, including plum rainy season and typhoon season;
(2) Duration of rainfall: i.e., the rainfall time from one moment to another moment in the primary rainfall process, wherein the unit is day;
(3) Rainfall intensity: i.e., the rainfall per unit time, in mm/h;
(4) Watershed wettability: namely, the wetting degree of the river basin can be calculated through a hydrological model, and the river basin is dimensionless;
(5) Other flood indicators, etc.;
the user can reasonably change the selection and threshold of flood indexes according to specific research areas and research purposes.
Further, the flood classification method in the step 4) is a decision tree method, and the decision tree method is a method for analyzing by using a top-down recursion mode by using a tree diagram as an analysis tool; the method comprises the following specific steps:
the decision tree method for flood classification is that the attribute value (index interval) is compared at the internal node (flood index) of the decision tree, the branch from the node downwards is judged according to the comparison result, and finally the result is obtained at the leaf node (flood type) of the decision tree.
Further, the step 4) specifically includes: (1) setting a soft interval, namely a threshold (Thr) plus a change interval (Thr+/-R), of the attribute value of the flood index; for the season of flood occurrence, R is 10 days; for other flood indicators, R is 25% of the threshold Thr; (2) the range of membership is [0,1], and in the interval, the flood index value and membership are in linear change; 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; (3) recursion is carried out from top to bottom, and comparison of all internal nodes is completed, so that membership degrees of all the internal nodes are obtained; (4) the membership degree of a certain field flood to a certain leaf node (flood type) is multiplied by the membership degree of all internal nodes on the path; adding membership of all the same flood types (the same flood type possibly has a plurality of leaf nodes), namely, final membership of the field flood to a certain flood type; (5) according to the membership degree of a certain field flood to three flood types, judging which flood type the field flood belongs to; the type with the highest membership is the final type of the field flood.
The beneficial effects of the invention are as follows:
(1) By introducing the fuzzy theory into the flood classification method, uncertainty in the flood classification process is considered, and objectivity and accuracy of flood classification are improved.
(2) The method can obtain more accurate flood classification results, and the more accurate flood classification results are helpful for more accurately simulating different flood processes, and have very important significance in aspects such as management and evaluation of flood risks.
Drawings
FIG. 1 is a flood classification decision tree method;
FIG. 2 is a soft threshold and membership assignment;
fig. 3 is a fuzzy theory based scene flood membership result.
Detailed Description
The invention is further described below with reference to the drawings and the detailed description. In order to highlight the advantages of the invention, the invention is implemented by taking the Jinhua river basin of Zhejiang province located in the coastal of southeast as a case.
As shown in fig. 1 and 2, the method for classifying flood in southeast coastal areas based on fuzzy theory of the invention comprises the following steps:
and collecting rainfall data of a weather station in the hour scale of the Jinhua river basin and runoff data of a Jinhua hydrologic station, and carrying out leak detection and repair on the data, wherein a small amount of rainfall data is subjected to reverse distance interpolation and repair on the adjacent weather station. And according to rainfall data of the weather station, calculating to obtain the surface rainfall of the Jinhua river basin by using the Thiessen polygon.
All field floods in the study period 04/12/2006-07/01/2013 are identified and extracted by using an automatic field flood extraction method, each field flood is numbered according to the starting time (YYYMMDD) of the flood, and the extraction result shows that 36 field floods are extracted in total in the study period.
The specific process of the automatic field flood extraction method comprises the following steps:
(1) Finding flood peak flow (Q) in runoff sequence max )；
(2) Determining flood start (stop) time, i.e. current (following) time step runoff below flow threshold (Q) 0 )；
(3) And when rainfall exists corresponding to the flood start time, the start time is moved forward to the moment without rainfall. The flow threshold is calculated as follows:
Q 0 ＝max(0.25·Q max ,Q min +0.05·(Q max -Q min ))
wherein Q is max Represents the flood peak flow, Q min Representing the minimum flow ten days before (after) the flood peak basin for calculating and determining the floodFlow threshold for water start-stop time.
(4) The field flood extracted according to the above procedure is unusable if it meets a certain condition (1) if the duration of the flow rise or fall is less than three hours; (2) if the occurrence period of a flood of a certain field overlaps with the previous field, only one field is selected; (3) if the total amount of rainfall is less than 2.5 mm before peak flood occurs; (4) if the extracted field flood runoffs are basically unchanged (judged according to the following two limiting conditions, one is that the maximum runoff difference value corresponding to two adjacent time steps is less than 10 percent, and the other is that the flood peak runoffs are less than 1.3 times of the average runoffs); (5) data missing for more than 10% of its flood duration, etc. if there is a flood before and after the occurrence of the flood peak.
Determining and calculating an index for flood classification, specifically comprising: (1) season of flood occurrence: namely, the stage of flood occurrence is usually defined as a 5 month 1 day to 9 month 30 period, and the period is full of rainfall and basically comprises a Jin Huajiang river basin plum rainy season and a typhoon season; (2) duration of rainfall: namely, the rainfall time from one moment to the other moment in the primary rainfall process takes days as a unit; (3) rainfall intensity: i.e., the rainfall per unit time, in mm/h; (4) watershed wettability: i.e. the wetting degree of the basin, can be calculated by a hydrological model, and is dimensionless.
According to the extracted index of 36 fields of floods, a flood classification method based on a fuzzy theory is applied to carry out membership degree distribution, (1) the attribute value of the flood index is set as a soft threshold, namely a threshold (Thr) plus a change interval (Thr+/-R); for the season of flood occurrence, R is 10 days; for other flood indicators, R is 25% of the threshold Thr; (2) the range of membership is [0,1], and in the interval, the flood index value and membership are in linear change; 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; (3) recursion is carried out from top to bottom, and comparison of all internal nodes is completed, so that membership degrees of all the internal nodes are obtained; (4) the membership degree of a certain field flood to a certain leaf node (flood type) is multiplied by the membership degree of all internal nodes on the path; adding membership of all the same flood types (the same flood type possibly has a plurality of leaf nodes), namely, final membership of the field flood to a certain flood type; (5) according to the membership degree of a certain field flood to three flood types, judging which flood type the field flood belongs to; the type with the highest membership is the final type of the field flood.
As shown in fig. 3, it can be seen that half of the field floods are classified into mixed floods, i.e. are composed of a plurality of floods, which illustrates that the method for classifying floods in southeast coastal areas based on fuzzy theory practically considers the uncertainty in the flood classification process, and the classification result is more objective and accurate.
The above description is only exemplary of the present invention and is not intended to limit the present invention, and various modifications and variations may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the scope of the claims of the present invention should fall within the protection scope of the present invention.
Claims (6)
1. The method for classifying the flood in the southeast coastal region based on the fuzzy theory is characterized by comprising the following steps of:
1) Collecting rainfall and runoff data of a research area, and performing data processing;
2) Identifying and extracting the field floods in the research period by using an automatic field flood extraction method, and numbering each field flood according to the starting time of the floods;
3) Selecting flood indicators for flood classification, including season, duration of rainfall, intensity of rainfall, and basin wettability of the flood;
4) According to flood indexes, carrying out flood classification on the flood orders extracted in the step 2) by using a flood classification method based on a fuzzy theory in southeast coastal areas, and obtaining a flood classification result of a research area;
the step 4) is specifically as follows:
(1) Setting a soft interval of the attribute value of the flood index, namely adding a threshold Thr to a change interval Thr+/-R; for the season of flood occurrence, R is 10 days; for other flood indicators, R is 25% of the threshold Thr;
(2) The range of membership is [0,1], and in the interval, the flood index value and membership are in linear change; 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;
(3) Recursion is carried out from top to bottom, and comparison of all internal nodes is completed, so that membership values of all the internal nodes are obtained;
(4) The membership degree of a certain field flood to a certain leaf node is multiplied by the membership degree of all internal nodes on the path; adding membership degrees of all the same flood types to obtain final membership degrees of the field flood to a certain flood type; the sum of membership of a certain field flood to three flood types is 1.0;
(5) According to the membership degree of a certain field flood to three flood types, judging which flood type the field flood belongs to; the type with the highest membership is the final type of the field flood.
2. The method for classifying flood in southeast coastal areas based on fuzzy theory according to claim 1, wherein the fuzzy theory considers that things have uncertainty and adopts membership to quantitatively express the things.
3. The method of classification of southeast coastal zone floods based on fuzzy theory of claim 1, wherein the method classifies southeast coastal zone floods into three process related floods, including fast flood FF, short duration rainfall flood SRF and long duration rainfall flood LRF.
4. The method for classifying flood in southeast coastal areas based on fuzzy theory according to claim 1, wherein the automatic extraction method of field flood adopted in the step 2) comprises the following specific processes:
(1) Finding flood peak flow Q in runoff sequence max ；
(2) Determining flood start/stop time, i.e. runoff of current/subsequent time step below flow threshold Q 0 ；
(3) When rainfall exists corresponding to the flood starting time, the starting time is moved forward to the moment without rainfall, and the flow threshold is calculated according to the following formula:
Q 0 ＝max(0.25·Q max ,Q min +0.05·(Q max -Q min ))
wherein Q is max Represents the flood peak flow, Q min Representing the minimum flow of ten days before/after the flood peak basin, and calculating a flow threshold value for determining the start-stop time of flood;
(4) The field flood extracted according to the above procedure is unusable if it meets a certain condition (1) if the duration of the flow rise or fall is less than three hours; (2) if the occurrence period of a certain field flood overlaps with the previous field, only one field is selected; (3) if the total amount of rainfall is less than 2.5 mm before peak flood occurs; (4) if the extracted field flood runoff remains basically unchanged, judging according to the following two limiting conditions: one is that the maximum runoff difference value corresponding to two adjacent time steps is less than 10%, and the other is that the flood peak runoff is less than 1.3 times of the average runoff; (5) if there is a data loss of more than 10% of its flood duration before and after the occurrence of a flood peak.
5. The method for classifying flood in southeast coastal areas based on fuzzy theory according to claim 1, wherein the flood index adopted in the step 3) specifically comprises:
(1) Season of flood occurrence: namely, the stage of flood occurrence is defined as a section from 5 months 1 day to 9 months 30 days, and the section is full of rainfall and comprises a plum rainy season and a typhoon season;
(2) Duration of rainfall: namely, the rainfall time from one moment to the other moment in the primary rainfall process takes days as a unit;
(3) Rainfall intensity: i.e., the rainfall per unit time, in mm/h;
(4) Watershed wettability: i.e. the wetting degree of the watershed, is calculated by a hydrological model, and is dimensionless.
6. The method for classifying flood in southeast coastal areas based on fuzzy theory according to claim 1, wherein the method for classifying flood in step 4) is a decision tree method, and the specific steps are as follows:
comparing attribute values at 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.
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