CN115293469A - Urban flood control and drainage risk prediction method - Google Patents

Urban flood control and drainage risk prediction method Download PDF

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CN115293469A
CN115293469A CN202211231275.7A CN202211231275A CN115293469A CN 115293469 A CN115293469 A CN 115293469A CN 202211231275 A CN202211231275 A CN 202211231275A CN 115293469 A CN115293469 A CN 115293469A
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杨牧
吴西贵
杨江骅
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Abstract

The invention relates to the technical field of electric digital data processing, in particular to a method for predicting urban flood control and drainage risks. The method is a digital processing method which is designed by computer assistance and is particularly suitable for flood disaster early warning, the method is characterized in that after the historical water storage variation and the water level variation are compared to obtain the water storage capacity corresponding to the unit water level variation of the current city, the prediction of the flood disaster occurrence probability of the current city at the future moment is completed based on the water storage capacity corresponding to the unit water level variation, then, historical factor correction parameters are additionally constructed according to the flood disaster situation and the water level situation at the same moment in the same year, land factor correction parameters are constructed according to the soil occupation ratio of the city, and the land factor correction parameters are constructed according to the land difference situation and the flood disaster occurrence situation of the current city and other cities, the flood disaster prediction is carried out after the obtained flood disaster occurrence probability is corrected by using the three correction parameters, and the flood prediction accuracy of the flood disaster is improved.

Description

Urban flood control and drainage risk prediction method
Technical Field
The invention relates to the technical field of electric digital data processing, in particular to a method for predicting urban flood control and drainage risks.
Background
In recent years, along with the aggravation of climate change, extreme climate events occur frequently, wherein flood disasters not only bring huge economic losses to cities, but also seriously threaten the safety of the cities and residents. In order to resist the influence of flood disasters on cities, flood control and drainage risks need to be predicted, so that the loss caused by the flood control and drainage risks is reduced.
The conventional flood control and drainage risk prediction method generally realizes flood control and drainage risk prediction of the current city by using historical hydrological data of the current city, the prediction method is simple, and the considered data source is single, so that a space for further improving the prediction accuracy exists.
Disclosure of Invention
The invention provides a method for predicting urban flood control and drainage risks, which is used for improving the accuracy of predicting the urban flood disasters, and adopts the following technical scheme:
the invention discloses a method for predicting urban flood control and drainage risks, which comprises the following steps:
determining the water storage variable quantity of the current city for the set historical days today according to the actual precipitation, daily average sewage discharge and daily drainable quantity of the current city per day historically, and determining the water level variable quantity of the current city for the set historical days today according to the historical water level of the current city per day ending moment historically;
determining the water storage capacity corresponding to the current city unit water level change according to the water storage change and the water level change;
determining the predicted water level of the current city every day after the current day according to the water storage capacity corresponding to the unit water level change of the current city, the predicted precipitation amount of the future every day, the daily drainable amount and the daily average sewage discharge amount, thereby determining the flood occurrence probability at the end time of each day after the current day;
determining a set date section comprising the reference day by taking the number of days corresponding to the predicted flood occurrence probability as the reference day, and determining the flood disaster occurrence condition and the water level condition in the set date section in each year in the set historical number of years to obtain historical factor correction parameters for correcting the flood occurrence probability;
determining a land factor correction parameter for correcting the flood disaster occurrence probability according to the occupation ratio of the soil area in the current city in the total area of the city;
determining a terrain factor correction parameter for correcting the flood occurrence probability according to the terrain difference between the current city and other cities and the flood occurrence conditions of other cities;
and correcting the flood occurrence probability according to the determined historical factor correction parameters, the land factor correction parameters and the relief factor correction parameters, and completing flood prediction according to the corrected flood occurrence probability.
The beneficial effects of the invention are as follows:
according to the method, after the flood occurrence probability at the future moment is predicted based on the water storage capacity corresponding to the unit water level change of the current city, historical factor correction parameters are additionally constructed according to the flood disaster situation and the water level situation at the same moment in the past year, land factor correction parameters are constructed according to the soil proportion of the city, and the land difference situation of the current city and other cities and the flood occurrence situation are constructed, the flood prediction is carried out after the obtained flood occurrence probability is corrected by using the three-aspect correction parameters, and the accuracy of the flood prediction is improved.
Further, the method for determining the water storage capacity corresponding to the current city unit water level change comprises the following steps:
Figure DEST_PATH_IMAGE001
Figure 742759DEST_PATH_IMAGE002
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE003
the water storage capacity corresponding to the current city unit water level change,
Figure 711983DEST_PATH_IMAGE004
indicating the set historical number of days since today,
Figure 977879DEST_PATH_IMAGE005
representing the amount of change in the impounded water for the j day before the current city today,
Figure 180190DEST_PATH_IMAGE006
the historical water level value representing the current city at yesterday's end time,
Figure 822524DEST_PATH_IMAGE007
indicating that the current city is the first to this day
Figure 675948DEST_PATH_IMAGE008
The historical water level value at the end of the day,
Figure 112746DEST_PATH_IMAGE009
representing the actual precipitation of the current city on day j before today,
Figure 802353DEST_PATH_IMAGE010
indicating the daily average sewage discharge amount of the current city,
Figure 717220DEST_PATH_IMAGE011
representing the daily drainable amount of the current city.
Further, the predicted water level of each day after the current city is:
Figure 192194DEST_PATH_IMAGE012
Figure 799893DEST_PATH_IMAGE013
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE014
indicating the predicted water level at the end of day I after the current city,
Figure 445638DEST_PATH_IMAGE015
representing the predicted impounded water change amount of the current city on the ith day after today,
Figure 538097DEST_PATH_IMAGE006
the historical water level value representing the current city at yesterday's end time,
Figure DEST_PATH_IMAGE016
representing the predicted precipitation for the current city on day i after this day,
Figure 585687DEST_PATH_IMAGE010
represents the daily average sewage discharge amount of the current city,
Figure 442916DEST_PATH_IMAGE011
representing the daily drainable amount of the current city.
Further, the flood occurrence probability at the end of each day after today is:
Figure 982482DEST_PATH_IMAGE017
Figure 629364DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 937985DEST_PATH_IMAGE019
the flood probability of the current city at the end of the day I after the current day,
Figure 261388DEST_PATH_IMAGE020
、
Figure DEST_PATH_IMAGE021
and
Figure 881725DEST_PATH_IMAGE022
respectively are the height values of a normal water level line, a warning water level line and a dangerous water level line,
Figure DEST_PATH_IMAGE023
predicting a weight for the flood probability, wherein
Figure 551872DEST_PATH_IMAGE024
，
Figure 715000DEST_PATH_IMAGE014
For the predicted water level at the end of day I after the current city today,
Figure 960037DEST_PATH_IMAGE025
the numerical value in parentheses is 0 when the numerical value in parentheses is not positive, and the numerical value in parentheses is self-value when the numerical value in parentheses is positive.
Further, the method for obtaining the historical factor correction parameter comprises the following steps:
Figure 474195DEST_PATH_IMAGE026
Figure 706288DEST_PATH_IMAGE027
wherein, the first and the second end of the pipe are connected with each other,
Figure 723922DEST_PATH_IMAGE028
and with
Figure 405439DEST_PATH_IMAGE029
Respectively representing a first historical factor correction parameter and a second historical factor correction parameter,
Figure 252566DEST_PATH_IMAGE030
indicating that the number of the historical year copies is set,
Figure 294209DEST_PATH_IMAGE031
indicating that the water level value is less than the predicted water level in the set date section in the set historical year number
Figure 431929DEST_PATH_IMAGE014
And the number of yearly flood disasters occur,
Figure DEST_PATH_IMAGE032
indicating that the water level value is greater than the predicted water level in the set date period in the set historical number of years
Figure 753189DEST_PATH_IMAGE014
And the annual number of flood disasters do not occur.
Further, the method for obtaining the land factor correction parameter comprises the following steps:
and (4) counting the proportion Q of the soil area in the total area in the current city, and taking the proportion Q as a land factor correction parameter.
Further, the method for obtaining the terrain factor correction parameter comprises the following steps:
dividing the periphery of the city into peripheral areas with the number of the set areas, sequentially making a difference between the altitude of the city area and the altitude of each peripheral area to obtain the altitude difference values of the number of the set areas, then performing normalization processing, and sequencing according to the sequence from small to large to obtain a terrain sequence;
determining the terrain sequence of the current city and the terrain sequences of other cities according to a terrain sequence acquisition method, and then calculating the terrain advantages and disadvantages parameters of the current city compared with other cities:
Figure 586147DEST_PATH_IMAGE033
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE034
representing the terrain superiority and inferiority parameters of the current city compared with other cities,
Figure 651055DEST_PATH_IMAGE035
and
Figure 643282DEST_PATH_IMAGE036
respectively representing the current cityThe maximum and minimum values of the elements in the potential matrix,
Figure 667868DEST_PATH_IMAGE037
and
Figure 378335DEST_PATH_IMAGE038
respectively representing the maximum and minimum values of the elements in the terrain matrix of the other cities,
Figure 512513DEST_PATH_IMAGE039
the number of elements whose values fall into the latter half of the value range of the elements in the terrain matrix of the current city,
Figure 359246DEST_PATH_IMAGE040
representing the number of elements with values falling into the latter half of the value range of the elements in the terrain matrix of other cities;
selecting other cities with the set number of Y, and counting the number of Y to obtain each city
Figure 304200DEST_PATH_IMAGE034
The number of other cities which are larger than 1 and corresponding to other cities in flood disaster
Figure DEST_PATH_IMAGE041
And an
Figure 829859DEST_PATH_IMAGE034
The number of the value is less than 1 and corresponds to the number of other cities without flood disasters
Figure 643094DEST_PATH_IMAGE042
To in order to
Figure 983815DEST_PATH_IMAGE043
And
Figure 958724DEST_PATH_IMAGE044
as a relief factor correction parameter for correcting the flood occurrence probability.
Further, the method for completing flood prediction according to the corrected flood occurrence probability comprises:
firstly determining the corrected flood occurrence probability:
Figure 502838DEST_PATH_IMAGE045
wherein the content of the first and second substances,
Figure 119764DEST_PATH_IMAGE046
indicating the flood probability of the revised current city at the end time of the day I after the current day,
Figure 816456DEST_PATH_IMAGE028
and with
Figure 696687DEST_PATH_IMAGE029
Respectively representing a first historical factor correction parameter and a second historical factor correction parameter,
Figure 728097DEST_PATH_IMAGE047
the land factor correction parameter is represented by a land factor correction parameter,
Figure 883135DEST_PATH_IMAGE041
and
Figure 932868DEST_PATH_IMAGE042
respectively indicating the number of other cities which are flat in terrain and are subjected to flood damage compared with the current city and the number of other cities which are not subjected to flood damage compared with the terrain of the current city in the set number Y of other cities;
then judging the flood disaster occurrence probability
Figure 780738DEST_PATH_IMAGE046
And if the probability is greater than the threshold value of the flood occurrence probability, the flood occurs, otherwise, the flood does not occur.
Drawings
Fig. 1 is a flow chart of the urban flood control and drainage risk prediction method of the invention.
Detailed Description
The conception of the invention is as follows:
according to the method, after the historical water storage variation and the water level variation are compared to obtain the water storage capacity corresponding to the unit water level variation of the current city, the flood occurrence probability of the current city at the future time is predicted based on the water storage capacity corresponding to the unit water level variation, and then the flood occurrence probability is corrected by respectively combining three correction parameters obtained by the historical factor, the land factor and the terrain factor, so that the corrected flood occurrence probability capable of more accurately predicting the flood is obtained, the flood judgment is completed, and the accuracy of predicting whether the flood occurs or not is improved.
The following describes a method for predicting risk of flood control and drainage in cities in detail with reference to the accompanying drawings and embodiments.
The method comprises the following steps:
the embodiment of the urban flood control and drainage risk prediction method provided by the invention has the overall flow as shown in figure 1, and the specific process is as follows:
the method comprises the steps of firstly, obtaining the historical daily actual precipitation of the current city, the future daily predicted precipitation, daily drainable quantity, daily average sewage discharge quantity and historical water level of the historical end time of each day.
In order to realize the flood disaster early warning of the current city, the flood control capability condition of the current city needs to be judged by combining historical hydrological information of the current city.
Therefore, the embodiment firstly obtains the actual daily rainfall amount of the current city in history through the data issued by the official departments such as the meteorological office or the hydrological office
Figure 299444DEST_PATH_IMAGE048
And also according to the data issued by the official department of the weather bureau, the forecast value of the precipitation of the current city today and every day thereafter is obtained
Figure 992594DEST_PATH_IMAGE049
. Wherein, the first and the second end of the pipe are connected with each other,
Figure 398298DEST_PATH_IMAGE009
representing the actual precipitation of the current city on day j before today,
Figure 151491DEST_PATH_IMAGE050
representing the predicted precipitation of the current city today,
Figure 504630DEST_PATH_IMAGE016
representing the predicted precipitation for the day i after the current city today. It is easy to understand that today's actual precipitation is not available for the entire day today, but only for prediction of precipitation today by weather prediction, since it is not completely finished today.
Then, the daily drainable quantity of the current city is obtained according to official data
Figure 876837DEST_PATH_IMAGE011
The daily drainable amount is a current embodiment of city drainage capacity and can be determined by planning data of the current city. Meanwhile, the sewage discharge amount of the current city in a certain time is obtained from historical data, and the daily average sewage discharge amount of the current city is determined according to the sewage discharge amount in the certain time
Figure 527261DEST_PATH_IMAGE010
。
Meanwhile, the historical water level of the current city at the end of each day in history is obtained
Figure 575989DEST_PATH_IMAGE051
Wherein, in the step (A),
Figure 944653DEST_PATH_IMAGE052
representing the historical water level value at the end of the jth day before the current city today.
And step two, determining the water storage capacity corresponding to the unit water level change of the current city according to the actual precipitation, daily dischargeable water discharge, daily average sewage discharge and historical water level of the current city in a certain historical time.
It can be understood that, the early warning of the flood disaster for different cities is required, not only because the rainfall conditions of different cities are different due to different geographical locations, but also because the rainfall bearing capacities of different cities are different, specifically, the actual water storage amount corresponding to the unit water level change of each city is different, so even if the same rainfall occurs, the situation that one city can have the flood disaster and the other city can not have the flood disaster is likely to occur for different cities.
In consideration of the above situation, the embodiment first determines the water storage capacity corresponding to the current city unit water level change according to the current city precipitation situation in a certain historical time period.
Specifically, the amount of change in the accumulated water per day in history is first determined:
Figure 353507DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure 858437DEST_PATH_IMAGE005
representing the amount of change in the impounded water for the j day before the current city today,
Figure 78066DEST_PATH_IMAGE009
representing the actual precipitation of the current city on day j before today,
Figure 668447DEST_PATH_IMAGE010
indicating the daily average sewage discharge amount of the current city,
Figure 382457DEST_PATH_IMAGE011
representing the daily drainable capacity of the current city.
Then, selecting the set historical days J before today from the historical data of the current city, thereby determining the water storage capacity corresponding to the unit water level change of the current city:
Figure 7473DEST_PATH_IMAGE053
wherein, the first and the second end of the pipe are connected with each other,
Figure 240746DEST_PATH_IMAGE003
the water storage capacity corresponding to the current city unit water level change,
Figure 193790DEST_PATH_IMAGE004
indicating the set historical number of days since today,
Figure 570544DEST_PATH_IMAGE005
representing the amount of change in the impounded water for the j day before the current city today,
Figure 174701DEST_PATH_IMAGE006
the historical water level value representing the current city at the end of yesterday,
Figure 611498DEST_PATH_IMAGE007
indicating that the current city is the first from today
Figure 544514DEST_PATH_IMAGE008
Historical water level values at the end of day time.
In the water storage capacity calculation formula,
Figure DEST_PATH_IMAGE054
the method is characterized in that the water storage variation from the J th day to the yesterday before today is shown, and the variation is compared with the variation of the water level value in the whole process from the J +1 th end moment before today, namely from the J th start moment to the yesterday end moment before today
Figure 318435DEST_PATH_IMAGE055
And comparing to determine the water storage capacity corresponding to the current city unit water level change.
Therefore, the variable quantity of the water storage capacity corresponding to the whole city after the unit water level change of the current city is determined, and the water storage capacity corresponding to the unit water level change of the current city is also obtained.
And step three, determining the predicted water levels of the current day and the end time of each day in the future according to the water storage capacity corresponding to the current city unit water level change and the predicted precipitation of each day in the future.
The change of the water level is a continuous process related to the water level value at the end time of the previous day, namely the water level value at the end time of a certain day is obtained by the water storage change of the city at the day and the water level value at the end time of the previous day.
Therefore, to achieve the determination of the predicted water level for today and the end of each day in the future, the present embodiment first determines the predicted impounded water variation for the ith day after the present city:
Figure 652464DEST_PATH_IMAGE013
wherein the content of the first and second substances,
Figure 604371DEST_PATH_IMAGE015
representing the predicted impounded water variation of the current city on day i after today,
Figure 515695DEST_PATH_IMAGE016
representing the predicted precipitation for the current city on day i after today,
Figure 499832DEST_PATH_IMAGE010
represents the daily average sewage discharge amount of the current city,
Figure 327848DEST_PATH_IMAGE011
representing the daily drainable amount of the current city.
Then determining the predicted water level of the current city at the end time of day I after today:
Figure 106448DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 505069DEST_PATH_IMAGE014
indicating the predicted water level at the end of day I after the current city,
Figure 292896DEST_PATH_IMAGE015
representing the predicted impounded water change amount of the current city on the ith day after today,
Figure 476884DEST_PATH_IMAGE006
representing the historical water level value of the current city at the yesterday end time.
In the predicted water level calculation formula,
Figure 426385DEST_PATH_IMAGE056
indicating the predicted amount of change in the impounded water during the course of day to predicted day I after day, so
Figure 312302DEST_PATH_IMAGE057
The predicted variation of the water level during the ith day after today is shown, and the predicted variation is summed with the historical water level value at the end time of yesterday to obtain the predicted water level at the end time of ith day after today.
It is understood that I and I are integers and both values are [0, +,infinity ], when I is 0,
Figure 12142DEST_PATH_IMAGE058
indicating that the precipitation is predicted today, when the value of I is 0,
Figure 316216DEST_PATH_IMAGE059
indicating the predicted water level at the end of today.
And step four, determining the flood probability at the end time of today and the end time of each day in the future according to the predicted water levels at the end time of today and the end time of each day in the future.
As can be known by combining with the common knowledge in the field, the water level line of three levels is usually set to mark the city water level state, specifically, the normal water level line and the alarmThe three water level lines respectively correspond to the determined height values thereof, and the heights of the normal water level line, the alarm water level line and the dangerous water level line are respectively recorded as the heights of the normal water level line, the alarm water level line and the dangerous water level line in the embodiment
Figure 436618DEST_PATH_IMAGE020
、
Figure 809831DEST_PATH_IMAGE021
And
Figure 673882DEST_PATH_IMAGE022
then according to the predicted water level of the end time of the day I after the current city today
Figure 330997DEST_PATH_IMAGE014
And finishing the calculation of flood probability at the finishing time of the I day after the current city today according to the size relation among the heights of the three water level lines:
Figure 622301DEST_PATH_IMAGE060
Figure 482810DEST_PATH_IMAGE061
wherein the content of the first and second substances,
Figure 150551DEST_PATH_IMAGE019
the flood probability of the current city at the end of the day I after the current day,
Figure 163638DEST_PATH_IMAGE020
、
Figure 625843DEST_PATH_IMAGE021
and
Figure 973648DEST_PATH_IMAGE022
respectively are the height values of a normal water level line, a warning water level line and a dangerous water level line,
Figure 445081DEST_PATH_IMAGE023
predicting a weight for the flood probability, wherein
Figure 817068DEST_PATH_IMAGE024
，
Figure 450175DEST_PATH_IMAGE014
For the predicted water level at the end of day I after the current city today,
Figure 285276DEST_PATH_IMAGE062
this indicates that the value in the parenthesis is 0 when the number in the parenthesis is not positive, and the value in the parenthesis is self value when the number in the parenthesis is positive.
Prediction weight value calculated in flood occurrence probability
Figure 29241DEST_PATH_IMAGE023
In the determination process, as the predicted water level gradually approaches the normal water level line, the alarm water level line and the dangerous water level line, the values of the first term, the second term and the third term multiplied in the corresponding calculation process are non-linearly reached to 1 by a method that the speed increase is gradually increased from a value larger than zero to a value smaller than 1, so that the predicted weight value is gradually increased along with the gradual increase of the water level
Figure 751340DEST_PATH_IMAGE023
The water level of the flood is correspondingly increased, the increasing speed of the water level is gradually increased, and the characteristics that the increasing amount of the flood probability caused by unit water level change is different when the water level is high compared with the water level is low are reflected.
And step five, determining a set date section comprising the reference day by taking the number of days corresponding to the predicted flood occurrence probability as the reference day, determining the water level condition of each historical year on the set date section and the flood occurrence condition, comparing the water level condition with the predicted water level of the end time of the reference day, and determining historical factor correction parameters for correcting the flood occurrence probability.
In this embodiment, the flood occurrence probability at the end time of the I th day after today is obtained, then the set number of days is taken forward and backward respectively on the basis of the I th day after today in this embodiment, the set number of days is 5 days in this embodiment, in other embodiments, the set number of days may also be taken as other values according to the requirement for the prediction accuracy of the flood occurrence probability, and when the requirement for the prediction accuracy is higher, the value of the set number of days may be taken as larger. After taking the set number of days in front and back, a set date segment comprising a certain number of days is obtained, and the set date segment in this embodiment consists of 11 days. In the embodiment, the set date section is obtained by respectively taking the set days forward and backward on the basis of the day I after the day, and in other embodiments, any means for determining the set date section can be adopted, so that the set date section only needs to be ensured to comprise the day I after the day, namely the reference day.
Then, in the set historical number of years, the situation that flood disasters occur in the set date segment in each year is counted, the set historical number of years is 20 years, and other values can be taken according to the prediction accuracy requirement on the flood occurrence probability for the same set historical number of years.
In each historical year, the daily precipitation and the corresponding situation whether flood occurs in the time period corresponding to the set date segment, specifically, in the 11 days in the history which are the same as the 11 days in the set date segment in the whole year, specifically, if the predicted flood occurrence probability at the end time of the day I after today is specifically the probability of No. 9 and No. 16, the set date segment is formed by 11 days from No. 9 and No. 11 to No. 9 and No. 21, and the range of the searched daily precipitation and the corresponding situation whether flood occurs in the historical year is the daily precipitation and the corresponding situation whether flood occurs in 11 days from No. 9 and No. 11 to No. 9 and No. 21 in the historical year.
In the set historical years, namely 20 years, the water level value is counted to be less than the prediction probability in 11 days from No. 11 month 9 to No. 21 month 9 in each year
Figure 820927DEST_PATH_IMAGE019
The current city corresponding to it is todayPredicted water level at end of the last day I
Figure 877745DEST_PATH_IMAGE014
And the number of years of flood disaster
Figure 690980DEST_PATH_IMAGE031
And the water level value is greater than the predicted probability
Figure 766121DEST_PATH_IMAGE019
The corresponding predicted water level of the ending time of the I day after the current city today
Figure 6610DEST_PATH_IMAGE014
And the number of years of flood disaster
Figure 550724DEST_PATH_IMAGE032
Then determine the prediction probability
Figure 902071DEST_PATH_IMAGE019
The first historical factor correction parameter and the second historical factor correction parameter for correction:
Figure 520134DEST_PATH_IMAGE026
Figure 806890DEST_PATH_IMAGE027
wherein the content of the first and second substances,
Figure 713666DEST_PATH_IMAGE028
and
Figure 993337DEST_PATH_IMAGE029
respectively representing a first historical factor correction parameter and a second historical factor correction parameter,
Figure 403590DEST_PATH_IMAGE030
indicating that the number of years of history is set,
Figure 359783DEST_PATH_IMAGE031
indicating that the water level value is less than the predicted water level in the set date period in the set historical number of years
Figure 753855DEST_PATH_IMAGE014
And the number of yearly flood disasters occur,
Figure 571638DEST_PATH_IMAGE032
indicating that the water level value is greater than the predicted water level in the set date section in the set historical year number
Figure 367556DEST_PATH_IMAGE014
And the number of years of flood disasters does not occur.
In setting the historical year, if the water level value is less than the predicted water level
Figure 730535DEST_PATH_IMAGE014
And the number of years of flood disaster
Figure 611903DEST_PATH_IMAGE031
The more, the more the flood probability based on the predicted water level is indicated
Figure 233378DEST_PATH_IMAGE019
The smaller the size is; if the water level value is greater than the predicted water level
Figure 680540DEST_PATH_IMAGE014
And the number of years of flood disaster
Figure 604633DEST_PATH_IMAGE032
The more, the more the flood probability based on the predicted water level is indicated
Figure 81620DEST_PATH_IMAGE019
The larger the size of the system is, the probability of flood can be correspondingly obtained
Figure 116572DEST_PATH_IMAGE019
The first and second historical factor correction parameters are corrected.
And step six, determining a land factor correction parameter for correcting the flood disaster occurrence probability according to the occupation ratio of the soil area in the current city in the total area of the city.
The daily drainable amount of the city is not only influenced by the delivery performance of the drainage network of the city for delivering precipitation outside the city, but also is substantially influenced by the absorption and storage performance of the land area in the city, and in the daily drainable amount of the city counted by the official, the daily drainable amount of the city actually implies the drainage amount corresponding to the absorption and storage performance of the land area in the city for the precipitation.
However, the water absorption of the soil is limited, and the water level in the city is generated along with the long-term occurrence of rainfall, and actually, the absorption and storage performance of the soil for the rainfall reaches the upper limit at the moment, and the rainfall is accumulated above the ground just because the soil in the city cannot absorb the rainfall any more.
Therefore, in the case of the water level, the actual daily dischargeable quantity of the city is smaller than the daily dischargeable quantity given by the authority, and the larger the area of the urban land is, the more obvious the actual daily dischargeable quantity of the city is reduced compared with the daily dischargeable quantity given by the authority in the case of the water level, and the more the predicted flood occurrence probability should be corrected to a greater extent in the flood occurrence probability prediction process.
In the embodiment, the occupation ratio Q of the urban soil area in the total area is counted, and the occupation ratio Q is used as a land factor correction parameter for correcting the flood occurrence probability.
And step seven, determining a relief factor correction parameter for correcting the flood occurrence probability according to the relief difference between the current city and other cities and the flood occurrence conditions of other cities.
Whether flood disasters occur or not is also influenced by the situation difference from the periphery of the city to the city, and the lower the city is compared with the periphery, the higher the probability of flood disasters is, and the lower the probability is.
Therefore, in the present embodiment, the city area is taken as the center, the periphery of the city is divided into the areas with the set number of areas, and the number of the areas is preferably set to 8, so that the periphery of the city is correspondingly divided into 8 areas, the altitude of each peripheral area and the altitude of the city area are obtained, the altitude of the city area is sequentially differed from the altitude of each peripheral area, the 8 altitude differences are obtained, normalization processing is performed, and the terrain sequence of the current city is obtained by sorting the differences from small to large
Figure 11716DEST_PATH_IMAGE063
。
Then, the water level value in the set date section and the predicted water level of the current city at the end time of the day I after the current city are selected from other cities
Figure 106711DEST_PATH_IMAGE014
The difference value of the current city is smaller than that of the preset water level difference value, and the terrain sequence of the other selected cities is obtained according to the method for obtaining the terrain sequence of the current city
Figure 572458DEST_PATH_IMAGE064
And labeling the selected terrain sequences of other cities, wherein the label is V, when the value of V is 0, the fact that the flood disaster does not occur is shown, and when the value of V is 1, the fact that the flood disaster occurs is shown.
The terrain sequence of the current city is multiplied by itself to obtain a terrain matrix of the current city
Figure 411101DEST_PATH_IMAGE065
Determining the maximum and minimum values of the elements in the matrix
Figure 160751DEST_PATH_IMAGE035
And
Figure 426648DEST_PATH_IMAGE036
then will be composed of
Figure 895804DEST_PATH_IMAGE035
And
Figure 272559DEST_PATH_IMAGE036
the value ranges of the elements in the determined matrix are divided into set number of parts, 8 parts are preferred in the embodiment, and the value of the set number of parts is determined according to the correction accuracy requirement. After the value ranges of the elements in the matrix are divided into the set number of parts, all the elements in the matrix are correspondingly in the set number of parts, if the set number of parts is 8, which is preferred in this embodiment, the number of the elements in the matrix, whose values fall into the second half of the value ranges, that is, the number of the elements whose values fall into the larger of the two front and rear half value ranges, can be obtained through statistics.
Similarly, the terrain sequences of other cities are multiplied to obtain the terrain matrix of other cities
Figure 876716DEST_PATH_IMAGE066
And determining the maximum value and the minimum value of elements in the terrain matrix of other cities according to the same method
Figure 313513DEST_PATH_IMAGE037
And
Figure 753853DEST_PATH_IMAGE038
and correspondingly dividing all elements in the terrain matrixes of other cities into the set parts.
Then, calculating the terrain superiority and inferiority parameters of the current city compared with other cities:
Figure 262195DEST_PATH_IMAGE067
wherein the content of the first and second substances,
Figure 596224DEST_PATH_IMAGE034
representing the terrain superiority and inferiority parameters of the current city compared with other cities,
Figure 577824DEST_PATH_IMAGE035
and with
Figure 285886DEST_PATH_IMAGE036
Respectively representing the maximum value and the minimum value of the elements in the terrain matrix of the current city,
Figure 270023DEST_PATH_IMAGE037
and
Figure 52034DEST_PATH_IMAGE038
respectively representing the maximum and minimum values of the elements in the terrain matrix of the other cities,
Figure 204536DEST_PATH_IMAGE039
the number of elements whose values fall into the latter half of the value range of the elements in the terrain matrix of the current city,
Figure 9681DEST_PATH_IMAGE040
the number of elements whose values fall into the latter half of the value range of the elements in the terrain matrix of other cities is represented.
Terrain quality parameter
Figure 656563DEST_PATH_IMAGE034
The size of (b) represents the magnitude of the terrain difference degree of the current city compared with other cities,
Figure 43813DEST_PATH_IMAGE068
and
Figure 993314DEST_PATH_IMAGE069
respectively showing the ratio of the maximum value of the difference between the current city and the surrounding terrain to the maximum value of the difference between the other cities and the surrounding terrain, and the ratio of the minimum value of the difference between the current city and the surrounding terrain to the minimum value of the difference between the other cities and the surrounding terrain, wherein the larger the two ratios are, the more obvious the difference between the current city and the surrounding terrain is compared with the difference between the other cities and the surrounding terrain,
Figure 816914DEST_PATH_IMAGE070
the difference between the current city and the surrounding areas is larger than that between other cities, and finally, the terrain superiority and inferiority parameters
Figure 1907DEST_PATH_IMAGE034
The larger the size, the more depressed the city is compared to other cities.
When in use
Figure 165035DEST_PATH_IMAGE034
If the number of the urban areas is more than 1, the situation shows that the current city is more prone to flood disasters compared with other cities, and when the number of the urban areas is more than 1, the situation is that the current city is more prone to flood disasters
Figure 930778DEST_PATH_IMAGE034
And if the number of the urban areas is less than 1, the current city is less prone to flood disasters compared with other cities. In this embodiment, the number of other cities is set as the number Y of other cities, and the values of the number Y of other cities are set according to the accuracy requirement of flood prediction, and obviously, the accuracy requirement is correspondingly higher if the values of the number Y of other cities are set to be larger.
Counting each corresponding to the number Y of other cities
Figure 179357DEST_PATH_IMAGE034
The number of other cities which are larger than 1 and corresponding to other cities in flood disaster
Figure 168041DEST_PATH_IMAGE041
And an
Figure 779151DEST_PATH_IMAGE034
The number of other cities which are less than 1 and are not flood-damaged is corresponding to other cities
Figure 70455DEST_PATH_IMAGE042
. To be provided with
Figure 603068DEST_PATH_IMAGE043
And
Figure 395443DEST_PATH_IMAGE044
as a relief factor correction parameter for correcting the flood occurrence probability.
As can be readily appreciated, in
Figure 533164DEST_PATH_IMAGE041
In the corresponding situation, because other cities have flood disasters, and the current city is in a low-lying area compared with other cities, the flood disasters of the current city are more likely to occur; in addition, the
Figure 572533DEST_PATH_IMAGE042
In the corresponding situation, since no flood occurs in other cities, and the current city has a lower depression degree, i.e., a flatter topography compared to other cities, the probability that no flood occurs in the current city is higher.
And step eight, correcting the flood occurrence probability according to the determined historical factor correction parameters, the land factor correction parameters and the relief factor correction parameters, and finishing flood prediction according to the corrected flood occurrence probability.
According to the three correction factors, the embodiment corrects the obtained flood occurrence probability value to obtain the corrected flood occurrence probability:
Figure 920337DEST_PATH_IMAGE045
wherein, the first and the second end of the pipe are connected with each other,
Figure 126191DEST_PATH_IMAGE046
indicating the flood probability of the revised current city at the end time of the I day after the current day,
Figure 993784DEST_PATH_IMAGE028
and
Figure 892470DEST_PATH_IMAGE029
respectively representing a first historical factor correction parameter and a second historical factor correction parameter,
Figure 727571DEST_PATH_IMAGE047
the land factor correction parameter is represented by a land factor correction parameter,
Figure 471536DEST_PATH_IMAGE041
and
Figure 957750DEST_PATH_IMAGE042
in the other-city number Y, the number of other cities in which the terrain is flatter than the current city and flood occurs and the number of other cities in which the terrain is low than the current city and flood does not occur are indicated.
Probability of flood after correction
Figure 761757DEST_PATH_IMAGE046
If the threshold value is larger than the threshold value of the flood occurrence probability, the flood disaster can occur, otherwise, the flood disaster does not occur. In this embodiment, the threshold of the flood occurrence probability is preferably 0.8, and in other embodiments, the threshold of the flood occurrence probability may be set to other values according to the requirement of the flood prevention sensitivity level.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications or substitutions do not cause the essential features of the corresponding technical solutions to depart from the scope of the technical solutions of the embodiments of the present application, and are intended to be included within the scope of the present application.

Claims (8)

1. A city flood control and drainage risk prediction method is characterized by comprising the following steps:
determining the amount of variation of water storage of the current city in the current set historical days today according to the actual precipitation, daily average sewage discharge and daily drainable quantity of the current city in the history of the current city, and determining the amount of variation of water level of the current city in the current set historical days today according to the historical water level of the current city in the end moment of each day in the history of the current city;
determining the water storage capacity corresponding to the current city unit water level change according to the water storage change and the water level change;
determining the predicted water level of the current city every day after the current day according to the water storage capacity corresponding to the unit water level change of the current city, the predicted precipitation amount of the future every day, the daily drainable amount and the daily average sewage discharge amount, thereby determining the flood occurrence probability at the end of each day after the current day;
determining a set date section comprising the reference day by taking the number of days corresponding to the predicted flood occurrence probability as the reference day, and determining the flood disaster occurrence condition and the water level condition in the set date section in each year in the set historical number of years to obtain historical factor correction parameters for correcting the flood occurrence probability;
determining a land factor correction parameter for correcting the flood disaster occurrence probability according to the occupation ratio of the soil area in the current city in the total area of the city;
determining a relief factor correction parameter for correcting the flood occurrence probability according to the relief difference between the current city and other cities and the flood occurrence conditions of other cities;
and correcting the flood occurrence probability according to the determined historical factor correction parameters, the land factor correction parameters and the relief factor correction parameters, and completing flood prediction according to the corrected flood occurrence probability.
2. The urban flood control and flood drainage risk prediction method according to claim 1, wherein the method for determining the water storage capacity corresponding to the current urban unit water level change comprises the following steps:
Figure 126024DEST_PATH_IMAGE002
Figure 656363DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE005
the water storage capacity corresponding to the current city unit water level change,
Figure 488184DEST_PATH_IMAGE006
indicating the set historical number of days since today,
Figure DEST_PATH_IMAGE007
representing the amount of change in the impounded water for the j day before the current city today,
Figure 575744DEST_PATH_IMAGE008
the historical water level value representing the current city at the end of yesterday,
Figure DEST_PATH_IMAGE009
indicating that the current city is the first from today
Figure 400481DEST_PATH_IMAGE010
The historical water level value at the end of the day,
Figure DEST_PATH_IMAGE011
representing the actual precipitation of the current city on day j before today,
Figure 395112DEST_PATH_IMAGE012
represents the daily average sewage discharge amount of the current city,
Figure DEST_PATH_IMAGE013
representing the daily drainable capacity of the current city.
3. The method for predicting the risk of flood control and drainage in cities according to claim 1, wherein the predicted water level of the current city after today every day is:
Figure DEST_PATH_IMAGE015
Figure DEST_PATH_IMAGE017
wherein the content of the first and second substances,
Figure 693107DEST_PATH_IMAGE018
indicating the predicted water level at the end of day I after the current city today,
Figure DEST_PATH_IMAGE019
representing the predicted impounded water variation of the current city on day i after today,
Figure 717564DEST_PATH_IMAGE008
the historical water level value representing the current city at yesterday's end time,
Figure 611571DEST_PATH_IMAGE020
representing the predicted precipitation for the current city on day i after this day,
Figure 382081DEST_PATH_IMAGE012
represents the daily average sewage discharge amount of the current city,
Figure 585398DEST_PATH_IMAGE013
representing the daily drainable capacity of the current city.
4. The method for predicting the urban flood control and flood drainage risk according to claim 1, wherein the flood occurrence probability at the end of each day after the current day is:
Figure 300413DEST_PATH_IMAGE022
Figure 139056DEST_PATH_IMAGE024
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE025
the flood probability of the current city at the end of the day I after the current day,
Figure 842701DEST_PATH_IMAGE026
、
Figure DEST_PATH_IMAGE027
and
Figure 436493DEST_PATH_IMAGE028
respectively are the height values of a normal water level line, a warning water level line and a dangerous water level line,
Figure DEST_PATH_IMAGE029
predicting a weight for the flood probability, wherein
Figure 210109DEST_PATH_IMAGE030
，
Figure 852443DEST_PATH_IMAGE018
For the predicted water level at the end of day I after the current city today,
Figure DEST_PATH_IMAGE031
the numerical value in parentheses is 0 when the numerical value in parentheses is not positive, and the numerical value in parentheses is self-value when the numerical value in parentheses is positive.
5. The urban flood control and drainage risk prediction method according to claim 1, wherein the method for obtaining the historical factor correction parameters comprises:
Figure DEST_PATH_IMAGE033
Figure DEST_PATH_IMAGE035
wherein, the first and the second end of the pipe are connected with each other,
Figure 925441DEST_PATH_IMAGE036
and
Figure DEST_PATH_IMAGE037
respectively representing a first historical factor correction parameter and a second historical factor correction parameter,
Figure 690135DEST_PATH_IMAGE038
indicating that the number of the historical year copies is set,
Figure DEST_PATH_IMAGE039
indicating that the water level value is less than the predicted water level in the set date period in the set historical number of years
Figure 97851DEST_PATH_IMAGE018
And the number of yearly flood disasters occur,
Figure 402930DEST_PATH_IMAGE040
indicating that the water level value is greater than the predicted water level in the set date period in the set historical number of years
Figure 736960DEST_PATH_IMAGE018
And the number of years of flood disasters does not occur.
6. The urban flood control and drainage risk prediction method according to claim 1, wherein the method for obtaining the land factor correction parameter comprises:
and (4) counting the proportion Q of the total soil area in the current city, and taking the proportion Q as a land factor correction parameter.
7. The urban flood control and drainage risk prediction method according to claim 1, wherein the method for obtaining the terrain factor correction parameters comprises:
dividing the periphery of the city into peripheral areas with the number of the set areas, sequentially making a difference between the altitude of the city area and the altitude of each peripheral area to obtain the altitude difference values of the number of the set areas, then performing normalization processing, and sequencing according to the sequence from small to large to obtain a terrain sequence;
determining the terrain sequence of the current city and the terrain sequences of other cities according to a terrain sequence acquisition method, and then calculating the terrain advantages and disadvantages parameters of the current city compared with other cities:
Figure 751183DEST_PATH_IMAGE042
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE043
representing the terrain superiority and inferiority parameters of the current city compared with other cities,
Figure 229219DEST_PATH_IMAGE044
and
Figure DEST_PATH_IMAGE045
respectively representing the maximum value and the minimum value of elements in the terrain matrix of the current city,
Figure 869148DEST_PATH_IMAGE046
and
Figure DEST_PATH_IMAGE047
respectively representing the maximum and minimum values of the elements in the terrain matrix of the other cities,
Figure 306951DEST_PATH_IMAGE048
representing the number of elements whose values fall into the latter half of the value range of the elements in the terrain matrix of the current city,
Figure DEST_PATH_IMAGE049
representing the number of elements of which the values fall into the second half of the value range of the elements in the terrain matrix of other cities;
selecting other cities with the set number of Y, and counting the number of Y to obtain each city
Figure 413447DEST_PATH_IMAGE043
The number of other cities which are larger than 1 and corresponding to other cities in flood disaster
Figure 953013DEST_PATH_IMAGE050
And an
Figure 114742DEST_PATH_IMAGE043
The number of the value is less than 1 and corresponds to the number of other cities without flood disasters
Figure DEST_PATH_IMAGE051
To do so by
Figure 751260DEST_PATH_IMAGE052
And
Figure DEST_PATH_IMAGE053
as a relief factor correction parameter for correcting the flood occurrence probability.
8. The method for predicting the urban flood control and flood drainage risk according to claim 1, wherein the method for completing flood prediction according to the corrected flood occurrence probability comprises the following steps:
firstly determining the corrected flood occurrence probability:
Figure DEST_PATH_IMAGE055
wherein the content of the first and second substances,
Figure 513811DEST_PATH_IMAGE056
indicating the flood probability of the revised current city at the end time of the day I after the current day,
Figure 399727DEST_PATH_IMAGE036
and with
Figure 725666DEST_PATH_IMAGE037
Respectively representing a first historical factor correction parameter and a second historical factor correction parameter,
Figure DEST_PATH_IMAGE057
a land factor-based correction parameter is represented,
Figure 608170DEST_PATH_IMAGE050
and
Figure 728572DEST_PATH_IMAGE051
respectively indicating the number of other cities which are flat in terrain and are subjected to flood damage compared with the current city and the number of other cities which are not subjected to flood damage compared with the terrain of the current city in the set number Y of other cities;
then judging the flood occurrence probability
Figure 118097DEST_PATH_IMAGE056
And if the probability is greater than the threshold value of the flood occurrence probability, the flood occurs, otherwise, the flood does not occur.
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