CN110457374B - Method for identifying typical rainstorm process in time period - Google Patents
Method for identifying typical rainstorm process in time period Download PDFInfo
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- CN110457374B CN110457374B CN201910770804.2A CN201910770804A CN110457374B CN 110457374 B CN110457374 B CN 110457374B CN 201910770804 A CN201910770804 A CN 201910770804A CN 110457374 B CN110457374 B CN 110457374B
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
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- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
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- G—PHYSICS
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- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Abstract
The invention discloses a method for identifying a typical rainstorm process in a time period, which comprises the following steps: step 1, determining a time interval length T; step 2, calculating the accumulated rainfall within the time interval length T; step 3, extracting a typical rainfall process sample; and 4, step 4: determining a typical class; step 5, establishing a time period typical rainfall process characteristic index system; step 6, calculating a weighted average value of characteristic index values of a typical rainfall process in a time period; step 7, determining a typical rainfall process sequence in a time period; the method solves the problems of subjective randomness and large workload of workers in the traditional method, enables the selection and identification work in the typical rainstorm process to have objectivity, quantification and automation, and improves the work efficiency.
Description
Technical Field
The invention belongs to a rainstorm identification technology, and particularly relates to a method for identifying a typical rainstorm process in a time period.
Background
The rainstorm process is the change and distribution process of the rainstorm amount with time, and the design of the rainstorm process is deduced through a typical rainstorm process, and is one of important contents and methods for analyzing and calculating the design of rainstorm flood. Whether the typical rainstorm selection identification is reasonable directly influences the accuracy of designed flood, further influences the scale of hydraulic engineering design and the safety of operation management, and influences the accuracy safety of related flood risk assessment and engineering operation scheduling simulation. In practical application, the traditional method is to select typical rainstorm from actually measured rainfall sequence data according to the qualitative principle that the selection has certain representativeness, the rainstorm intensity and the total rainfall amount are large, and the flood control is not favorable for the rainstorm process after the main rain peak is deviated. The traditional method mainly selects typical rainstorm through a qualitative method, and has the defects of large subjective randomness and large manual workload.
The invention content is as follows:
the technical problem to be solved by the invention is as follows: the method for identifying the typical rainstorm process of the time interval is provided, and the problems that the subjective randomness is large and the manual workload is large due to the fact that the typical rainstorm is selected through a qualitative method in the prior art for identifying the typical rainstorm are solved.
The technical scheme of the invention is as follows:
a method of identifying a period of a typical storm event comprising:
step 1, determining a time interval length T;
step 2, calculating the accumulated rainfall in the time period length T;
step 3, extracting a typical rainfall process sample;
and 4, step 4: determining a typical class;
step 5, establishing a typical rainfall process characteristic index system in the time period;
step 6, calculating a weighted average value of characteristic index values of a typical rainfall process in a time period;
and 7, determining a typical rainfall process sequence in a time period.
The period length T is T =1h, 3h, 6h, 12h, 24h, 3d or any integral multiple of the time interval of the measured rainfall data.
The method for calculating the accumulated rainfall in the time period length T comprises the following steps: under a standard rainwater condition database system platform, rainfall sequence data P is actually measured according to history y，i(y＝year；i＝0，1，2，…n) Sliding calculation of cumulative precipitation P of time period T year by year y，T 。
The method for extracting the typical rainfall process sample in the step 3 comprises the following steps: cumulative precipitation P according to time period T y，T Value, extract annual P y，T The precipitation series with the largest value; in the remaining continuous sequence data, P is calculated by sliding y，T Extracting the corresponding P y，T The precipitation series with the largest value;
by the same logic method, 8 precipitation series are respectively extracted from historical actual rainfall sequence data of each year to be used as typical rainfall process samples in time period.
The method for determining the typical class in the step 4 comprises the following steps: and performing cluster analysis on the extracted typical rainfall process samples by adopting a K-means unsupervised cluster analysis method, determining the class as 3 classes, and screening the class with the largest number of samples as a typical class sample.
Step 5, the method for establishing the time period typical rainfall process characteristic index system comprises the following steps: at maximum rain intensity S within time interval T m，p_max1 (m =1,2,3.. N), second maximum rain intensity S m，p_max2 Third rain intensity S m，p_max3 Total amount of rainfall P m, total Rain peak coefficient r and rainfall total amount P of the latter half period m, last 1/2 Total rainfall P of later 1/4 period m, last 1/4 The weight coefficients of the characteristic index values are W respectively i(i＝1，2，3...7) ；
Step 6, the method for calculating the weighted average of the characteristic index values of the typical rainfall process in the time period comprises the following steps: normalizing each characteristic index value by adopting a maximum value normalization method, and performing weighted average calculation on the normalized characteristic values to obtain a weighted average value W m (m＝1，2，3...n)。
Step 7, the method for determining the typical rainfall process sequence in the time period comprises the following steps: and sorting the weighted average values from large to small, and taking a rainfall sequence of the field corresponding to the maximum value as a time interval typical rainfall process sequence.
The invention has the beneficial effects that:
the method has simple calculation process, and can realize automatic quantitative analysis and identification of typical rainstorm process with any period length from a large amount of original sequence data of historical rainfall process through simple programming; compared with the traditional discrimination method, the method solves the problems of subjective randomness and large workload of workers in the traditional discrimination method, enables the typical rainstorm process selection and identification work to have objectivity, quantification and automation, and improves the work efficiency.
The specific implementation mode is as follows:
a method of identifying a period of a typical storm event comprising the steps of:
step 1: determining a time interval length T; typical time interval lengths T =1h, 3h, 6h, 12h, 24h, 3d and the like, and can be determined by any integral multiple of the time interval of the measured rainfall data according to requirements;
step 2: calculating the cumulative rainfall in the time interval T(ii) a Under a standard rainwater condition database system platform, rainfall sequence data py is actually measured according to history ，i(y＝year；i＝0，1，2，...n) (ii) a (wherein y is year, i is serial number of measured rainfall data value) year by year sliding calculation of accumulated rainfall P of time interval T y，T ；
Step 3, extracting a typical rainfall process sample;
step 3, the method for extracting the typical rainfall process sample comprises the following steps: cumulative precipitation P according to time period T y，T Value, extract P in year y，T The precipitation series with the largest value; in the remaining two continuous sequence data, P is calculated by sliding y，T Extracting the corresponding P y，T The precipitation series with the largest value; by the same logic method, the rainfall series with the maximum cumulative rainfall in 8 fields are respectively extracted from the historical actual rainfall sequence data of each year to be used as typical rainfall process samples in the 8 fields.
And 4, step 4: determining a typical class;
the method for determining the typical class in the step 4 comprises the following steps: and performing cluster analysis on the extracted typical rainfall process samples by adopting a K-means unsupervised cluster analysis method, determining the category as 3, and screening the category with the maximum sample number n as a typical category sample.
Step 5, establishing a typical rainfall process characteristic index system in the time period;
step 5, the method for establishing the typical rainfall process characteristic index system in the time period comprises the following steps: according to the principle of 'strong rainstorm intensity, large total precipitation' and 'partial main rain peak', the maximum rain intensity S in the time interval T is used m，p_max1 (m =1,2,3.. N) (m is a sample number), second maximum rain intensity S m，p_max2 Third highest rain strength S m，p_max3 Total amount of rainfall P m, total Rain peak coefficient r (ratio of peak time to duration of rainfall), and rainfall total amount P in the latter half (1/2) m, last 1/2 Total rainfall of the last 1/4 period m, last 1/4 The weight coefficients of the characteristic index values are W respectively i(i＝1，2，3...7) (ii) a (typical w) i(i＝1，2，3...7) ＝0.2、0.1、0.06、0.18、0.14、0.14、0.18)；
Step 6, calculating a weighted average value of characteristic index values of a typical rainfall process in a time period;
step 6, the method for calculating the weighted average of the characteristic index values of the typical rainfall process in the time period comprises the following steps: normalizing each characteristic index value by adopting a maximum normalization method, and performing weighted average calculation on the normalized characteristic values to obtain a weighted average value W m (m＝1，2，3...n)。
And 7, determining a typical rainfall process sequence in a time period.
The method for determining the typical rainfall process sequence in the time period in the step 7 comprises the following steps: to weighted average by value W m (m =1,2,3.. N) are ordered from large to small, taking the maximum Max (W) m (m =1,2,3 … n)) is a time-of-day typical rainfall process sequence.
By adopting the method for identifying the typical rainstorm process of the time period, the typical rainstorm process corresponding to the time period length can be automatically analyzed and identified from the historical sequence data only by determining the time period length T; the identification of the representativeness of the obtained typical rainfall process results depends on the accumulated years of the historical sequence data, and the more years, the better the representativeness is.
Claims (2)
1. A method of identifying a period of a typical storm event comprising:
step 1, determining a time interval length T;
step 2, calculating the accumulated rainfall within the time interval length T;
the method for calculating the accumulated rainfall in the time interval length T comprises the following steps: under a standard rainwater condition database system platform, rainfall sequence data p is actually measured according to history y,i(y＝year；i＝0,1,2,…n) Sliding the cumulative precipitation P of the calculation period T year by year y,T ；
Step 3, extracting a typical rainfall process sample;
the method for extracting the typical rainfall process sample comprises the following steps: cumulative precipitation P according to time period T y,T Value, extract annual P y,T The precipitation series with the largest value; in the remaining two continuous sequence data, P is calculated by sliding y,T Extracting the corresponding P y,T The precipitation series with the largest value; respectively extracting 8 rainfall series from historical actual rainfall sequence data of each year by adopting the same logic method as a typical rainfall process sample in a time period;
and 4, step 4: determining a typical class;
the method for determining the typical class comprises the following steps: performing cluster analysis on the extracted typical rainfall process samples by adopting a K-means unsupervised cluster analysis method, determining the category to be 3, and screening out the category with the largest number of samples as a typical category sample;
step 5, establishing a typical rainfall process characteristic index system in the time period;
the method for establishing the typical rainfall process characteristic index system in the time period comprises the following steps: at maximum rain intensity S within time interval T m,p_max1 (m =1,2,3 … n), second maximum rain intensity S m,p_max2 Third rain intensity S m,p_max3 Total amount of rainfall P m, total Rain peak coefficient r and rainfall total amount P of the latter half period m, last 1/2 Total rainfall P of later 1/4 period m, last 1/4 The weight coefficients of the characteristic index values are w respectively in the typical rainfall process i(i＝1,2,3…7) ；
Step 6, calculating a weighted average value of characteristic index values of a typical rainfall process in a time period;
the method for calculating the weighted average of the characteristic index values of the typical rainfall process in the time period comprises the following steps: normalizing each characteristic index value by adopting a maximum normalization method, and performing weighted average calculation on the normalized characteristic values to obtain a weighted average value W m(m＝1,2,3…y*8) ；
Step 7, determining a typical rainfall process sequence in a time period;
the method for determining the time period typical rainfall process sequence comprises the following steps: and sequencing the weighted average values from large to small, and taking a field rainfall sequence corresponding to the maximum value as a typical rainfall process sequence in a time period.
2. A method of identifying a period of a typical storm event according to claim 1 wherein: the time interval length T is T =1h, 3h, 6h, 12h, 24h, 3d or any integral multiple of the time interval of the measured rainfall data.
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