CN106651004B - Flood disaster prediction method based on rainfall flood disaster time-space database - Google Patents
Flood disaster prediction method based on rainfall flood disaster time-space database Download PDFInfo
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Abstract
The invention relates to a flood disaster prediction method based on a rainfall flood disaster space-time database, which comprises the following steps: 1) constructing a rainfall flood time-space database; 2) according to the rainfall flood time-space database, a dynamic time-space rainfall map is drawn by combining with GIS map data; 3) acquiring space-time data corresponding to all rainstorm paths involved by flood disasters in historical time periods according to a rainfall flood space-time database, and displaying the rainstorm paths on a space-time rainfall map; 4) and acquiring corresponding classification models according to the space-time data corresponding to all the rainstorm paths, and predicting flood disasters according to the classification models. Compared with the prior art, the method has the advantages of space-time connection, dynamic and visual performance, accurate prediction and the like.
Description
Technical Field
The invention relates to the field of flood prediction, in particular to a flood prediction method based on a rainfall flood time-space database.
Background
In summary of previous studies, natural disaster databases can be divided into 5 types, namely statistical, spatial, temporal, elemental and procedural. At present, the research on the establishment and application of the comprehensive natural disaster database in China mainly focuses on the following 3 aspects.
1) And (4) integrating a natural disaster database and time-space law research. The database belongs to a statistical type database,
for example, the Chinese natural disaster database belongs to a statistical database and consists of a Chinese natural disaster-causing factor database, a Chinese newspaper and magazine natural disaster information database, a Chinese historical natural disaster database, a Chinese agricultural disaster database and other sub-databases. The natural disaster information record takes county area as a basic statistical unit, the compiled map can reflect the macroscopic space-time differentiation rule of natural disasters, but due to the multi-source of the data, the data in the statistical unit is incomplete in time, so that the natural disaster process is greatly limited, and due to the homogeneous limitation of the county area statistical unit, the respective natural disaster ranges are not corresponding.
2) Research on natural disaster system database and cause mechanism. Such a database belongs to a spatial relational database. Approaches to the comprehensive expression of pregnant disaster environment, disaster-causing factors, disaster-bearing bodies and disaster situations in a disaster system. However, in the process of superimposing a disaster and an obtained plaque, there is a problem of rationality in selecting a disaster weight in units of administrative counties.
3) And researching a historical natural disaster database and a natural disaster prediction model. For example, the historical flood database of seven rivers in China establishes dynamic flood information in 1736 to 1911 years by taking county areas as basic units, researches the distribution of regional differentiation rules of the flood on time, establishes a flood risk assessment model, and predicts the flood risk level according to the drainage basin. The historical natural disaster database belongs to a time relation type database, disaster dynamic information is established by taking county areas as basic units, the distribution of regional differentiation rules of flood in time is researched, and meanwhile, a risk evaluation model is established to predict the risk level. Because the historical data is limited, only disaster-causing information and disaster-causing information exist, so that the research on the cause mechanism has certain limitation.
Classification of natural disaster databases
Based on the research of predecessors and the complexity and dynamics of a natural disaster system, the natural disaster database can be divided into 5 classes according to the main function of natural disaster data analysis, namely, a statistical database (database based on statistical relationship) which focuses on the research of disaster-causing factor time-space differentiation rules is utilized; exploring natural disaster cause mechanism, focusing on spatial relational database (database based on space relationship) of information space matching; a time-based on time relationship database (database based on time relationship) that focuses on the study of temporal regularity; a database of factor relationships (database based on factor relationships) for in-depth analysis of various factors in a disaster system; aiming at the whole process of natural disaster occurrence, a natural disaster case database of natural disaster temporal-spatial differentiation rule, cause mechanism, natural disaster prediction and defense can be systematically analyzed, and the database is called a process relational database (database based on processing). The databases are various in types, but a summary database is lacked, so that disaster factors, disaster temporal and spatial information and detailed data statistics functions are unified, and certain obstacles are generated to disaster analysis and early warning.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a flood disaster prediction method based on a rainfall flood disaster time-space database.
The purpose of the invention can be realized by the following technical scheme:
a flood disaster prediction method based on a rainfall flood disaster time-space database comprises the following steps:
1) constructing a rainfall flood time-space database;
2) according to the rainfall flood time-space database, a dynamic time-space rainfall map is drawn by combining with GIS map data;
3) acquiring space-time data corresponding to all rainstorm paths involved by flood disasters in historical time periods according to a rainfall flood space-time database, and displaying the rainstorm paths on a space-time rainfall map;
4) and acquiring corresponding classification models according to the space-time data corresponding to all the rainstorm paths, and predicting flood disasters according to the classification models.
The step 1) specifically comprises the following steps:
11) acquiring day-by-day rainfall data of observation fields in each area, and merging the day-by-day rainfall data according to the month to form a rainfall data table, wherein the day-by-day rainfall data comprises area station numbers, time, cumulative rainfall data and longitude, latitude and altitude of the observation fields;
12) acquiring meteorological site detection data of each region, wherein the meteorological site detection data comprise a station number, a name of a province where the meteorological site is located, a station name and longitude, latitude and altitude of the meteorological site;
13) acquiring data of the super-huge flood disasters in historical time periods, wherein the data comprises time, place and disaster situations of flood disasters;
14) and respectively associating and combining the time data in the day-by-day rainfall data with the time data of the occurrence of the flood disaster in the data of the huge flood disasters, the area station number in the day-by-day rainfall data with the area station number in the meteorological station detection data and the province name of the meteorological station in the meteorological station detection data and the location data of the occurrence of the flood disaster in the meteorological station detection data to construct a rainfall flood time-space database.
In the step 14), the data included in the rainfall flood space-time database includes data of time, longitude, latitude and accumulated precipitation amount of occurrence of a flood disaster, a station number of a meteorological station, a name and altitude of a province where the meteorological station is located, and a disaster situation, where the disaster situation includes a disaster crop area, a number of casualties, a number of collapsed and damaged houses, and economic loss.
The step 3) specifically comprises the following steps:
31) extracting data of the area station number, the name of the province, the time of occurrence of the flood disaster, the longitude and the latitude of the weather station and the accumulated precipitation amount from a rainfall flood disaster time-space database;
32) screening according to the accumulated precipitation data to obtain data of the accumulated precipitation data meeting the rainstorm standard;
33) sorting the screened data in an ascending order according to time, sorting the accumulated precipitation data in a descending order, and obtaining space-time data corresponding to all rainstorm paths after sorting;
24) and marking the space-time data corresponding to all the rainstorm paths on a space-time rainfall map by combining with GIS map data.
The step 4) specifically comprises the following steps:
41) taking the name of the province, the month, the date and the accumulated precipitation as selected characteristics of a classification model, and making the classification model for the space-time data corresponding to all rainstorm paths;
42) carrying out data statistics on the classification model to obtain a relation graph between the province name, the month, the accumulated precipitation and the occurrence frequency, and carrying out correlation analysis;
43) acquiring a selected feature with highest correlation with the accumulated precipitation, and selecting space-time data corresponding to the selected feature and the accumulated precipitation from the space-time data corresponding to all rainstorm paths;
44) and acquiring the mean value of the space-time data corresponding to the selected characteristics and the accumulated precipitation, and taking the mean value as a prediction result.
Compared with the prior art, the invention has the following advantages:
firstly, space-time connection: the time-space database is constructed, and replaces the traditional disaster database which is mostly divided into a statistical type, a spatial relation type and a time relation type, and integrates the statistical data, the spatial relation and the time relation into a whole, so that the functions of statistical query and time-space analysis can be achieved;
secondly, dynamic visualization: the study on the rainstorm model is to creatively connect the huge flood disaster with the rainfall data, then to realize the geological display of the rainfall through the design scheme and the actual operation of the database, to realize the space-time display of the data, and to find the rainstorm path through the display of the dynamic map
Thirdly, the prediction is accurate: and (3) researching a prediction scheme, analyzing the rainstorm data and flood disasters day by a rainstorm model and a database, and exporting the data to obtain regularity of rainstorm development by data analysis software SPSS.
Drawings
FIG. 1 is a 1992.6.12 national rainfall profile.
FIG. 2 is a 1992.7.1 national rainfall profile.
Fig. 3 is a schematic diagram of the rainstorm path of 1998.
Fig. 4 is a schematic diagram of the connection of a rainfall meter, a flood meter and a meteorological station.
Fig. 5 is a storm path pattern 1 during a flood of 1975.
Fig. 6 is a storm path pattern 2 during a flood of 1975.
FIG. 7 is a time classification model diagram of a rainstorm region during a flood disaster since the country was built.
Fig. 8 is a frequency chart of a rainstorm occurrence area since the country was built.
Fig. 9 is a diagram of the occurrence time frequency of rainstorms since the country was built.
Fig. 10 is a diagram of the rainfall frequency during a storm since the country construction.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Example (b):
in the embodiment, the rainstorm flood disaster rule detection and prediction is carried out by using the time-space data integrated by the major flood disaster and the day-by-day rainfall data since the country construction, and the method comprises the following steps:
1) the method comprises the steps of establishing a data model by adopting a relational database PostgreSQL, establishing a complete table capable of showing the dynamic change of rainfall day by day, collecting the rainfall data day by day of 194 sites in China in 1951 and 2013, and merging the data of all the sites according to the months (data source: the data in the data set 1951-2010 is developed based on a basic data set of a monthly newspaper data file (A0/A1/A) after the data of the national level ground station in 1951-2010 is corrected, which is filed by a ground basic meteorological data construction project. Data of 1 month to 2012 and 5 months in 2011 are developed based on a ground monthly report data file (A file) reported to a national weather information center by each province. Data of 6-7 months in 2012 are developed based on real-time database data of the national weather information center. The data in the real-time library come from hourly data files (Z files) and daily data files of the ground automatic station uploaded in real time. )
Rainfall data for 1 month and 1 day of 1951 are shown in table 1:
TABLE 11951 rainfall data for 1 month and 1 day
Meteorological site display data are shown in Table 2
TABLE 2 national weather site Table
Importing data of the huge flood disasters from national construction into a time-space database of the invention, wherein the specific data is shown in table 3;
TABLE 3 data sheet of flood disasters since national construction
2) Utilizing SQL sentences to connect the ClimateStation of the database with a rainfall table, performing association and combination, and exporting rainfall data into a csv format, for example, connecting with a table 199807 and searching for the national rainfall conditions of 7 months and 1 days, and connecting rainfall with longitude and latitude points in order to specifically display a map on software, as shown in FIG. 4 and table 4;
table 4 rainfall flood disaster connection data table
3) Data visualization map display
And performing attribute connection on the csv table, the Chinese map and the site data by using geoscience analysis software ArcGIS to obtain a site table of assigned precipitation data, as shown in a table 5.
TABLE 5 exemplary data sheet for attribute linking of Chinese maps and site data
As shown in fig. 1 and 2, the present invention realizes the connection between data and a map, visualizes rainfall data, and can visually display the spatial and temporal distribution difference of rainfall.
4) Analyzing rainstorm paths
From the map, the route of the high-intensity rainfall, which is a process of the 1998 flood storm, is roughly obtained, and the rainfall amount is more than 250 as the mark of the storm, and the route of the storm is found as follows:
jiangsu, Zhejiang, Anhui → Sichuan, Hubei, Jiangxi → Teddy → Hunan, Guizhou → Guangxi → a new round of the intensive rainfall fluctuation range in the whole south → dispersing the momentum of two rains, one to Fujian, Guangdong, one to Yunnan → Teddy → a new round of the intensive rainfall fluctuation range in the whole south → weakening the whole.
We perform path tracing on rainfall data to find out the rainfall pattern in 1998 as shown in fig. 3:
5) rainfall situation of SQL connection flood area
The rainstorm mode can be seen from the rainstorm centroid tracking in space and time, in order to explore the law of the rainstorm mode, the database is used for carrying out rainstorm model analysis on the existing data, firstly, the flooding disaster table and the rainfall table are used for carrying out SQL connection, and sentences are completed.
The lookup table is shown in table 6:
the day-to-day rainfall condition of the flood area is obtained, and the analysis of the rainstorm model at the next stage is facilitated
TABLE 6 rainfall condition table day by day in flood area
6) Storm model analysis
According to the rainfall mode predicted before, the rainfall model is traced to cause by fruits, the law is searched, the rainfall table of the flood area is utilized, the area with 20-20 hours rainfall data of each station in the rainfall table larger than 500mm is further searched, and the data table is exported (32700, 32001 and the like are special weather codes and can be regarded as tiny rainfall):
visually analyzing derived data
The method is characterized in that ArcGIS is used for making a rainstorm path, each rainstorm path in a flood period is subjected to visual statistics, the rainstorm path is shown in figures 5 and 6, through map analysis of the rainstorm path, a rainstorm process which takes Jiangsu, Anhui and Zhejiang as initial rainstorm points and gradually extends to the west to the whole Yangtze river basin is basically obtained, the process is associated with the flood disaster, a development law of the flood disaster can be basically obtained, namely the flood disaster of the Yangtze river basin is caused by the super rainstorm which is started from the middle and lower reaches of the Yangtze river and is generally reduced in 6 and 7 months, the flood disaster is caused by the super rainstorm, the rainstorm path moves to the south of Hubei and the Hunan in the middle and lower reaches of the Yangtze river, the south of the river, the Guizhou and the like, the rainstorm path is mostly divided into two paths, one from north to the north of the river, the south of the Yangtze and the south of the Yangt. Basic flooding disasters also change with the trend of heavy rains.
7) The map analysis results can also be visualized with a more specific database design-based data analysis method:
selecting a Rainfall table of the occurrence time of the Flood disaster by using the Flood disaster table, selecting stations with Rainfall of more than 500mm and directly importing the stations into a new table Flood-rain
Therefore, day-by-day rainfall data of each station during occurrence of a super-flood disaster since the establishment is obtained, in order to better explore a rainstorm mode, a field with the rainfall > of 500mm is selected for special analysis, an updated numerical control data table is shown in a table 7, the date is subjected to ascending sequence, the rainfall is subjected to descending sequence arrangement, a path of the rainstorm can be reflected from the data, and space-time representation of the data is realized in a database.
TABLE 7 rainfall >500mm corresponding spatio-temporal data
8) Classification model using SPSS
And importing the csv data table generated in the last step into data analysis software SPSS, making a classification model for variables, as shown in FIG. 7, and obtaining a relation graph between province names, months, accumulated precipitation and occurrence frequency according to the classification model, as shown in FIGS. 8-10, wherein the characteristics of rainstorm from the country of construction can be seen through the prediction model, the ultra-large rainstorm is mainly concentrated in Guangxi, Anhui, Jiangxi, the rainstorm months are mainly 6 and 7 months, and the rainstorm amount is more than 1000mm in 500-.
The results of the correlation analysis of rainfall and provincial names are shown in table 8:
TABLE 8 correlation analysis results
The obvious correlation shows that the names of the provinces are closely related to rainfall, which means that the rainfall of each province and city over the years does not change greatly, and the law is helpful for predicting the rainfall of the rainstorm in advance.
The average value of each province and the accumulated rainfall data in the superflood disasters in the past is calculated as prediction data, and the prediction result is shown in table 9.
TABLE 9 prediction of rainfall for each province under storm mode
Claims (1)
1. A flood disaster prediction method based on a rainfall flood disaster time-space database is characterized by comprising the following steps:
1) the method comprises the following steps of constructing a rainfall flood space-time database, and specifically comprising the following steps:
11) acquiring day-by-day rainfall data of observation fields in each area, and merging the day-by-day rainfall data according to the month to form a rainfall data table, wherein the day-by-day rainfall data comprises area station numbers, time, cumulative rainfall data and longitude, latitude and altitude of the observation fields;
12) acquiring meteorological site detection data of each region, wherein the meteorological site detection data comprise a station number, a name of a province where the meteorological site is located, a station name and longitude, latitude and altitude of the meteorological site;
13) acquiring data of the super-huge flood disasters in historical time periods, wherein the data comprises time, place and disaster situations of flood disasters;
14) respectively associating and merging time data in the day-by-day rainfall data with time data of flood disasters in the superflood disaster data, area station numbers in the day-by-day rainfall data with area station numbers in the meteorological station detection data and area station numbers in the meteorological station detection data, and data of provinces where the meteorological stations are located and places where the flood disasters occur in the meteorological station detection data, and constructing a rainfall flood time and altitude database, wherein the data contained in the rainfall flood time and altitude database comprises time, longitude, latitude and accumulated precipitation data of the flood disasters, the area station numbers of the meteorological stations, names of the provinces and altitude and disaster conditions, and the disaster conditions comprise the area of damaged crops, the number of casualties, the number of collapsed damaged houses and economic losses;
2) according to the rainfall flood time-space database, a dynamic time-space rainfall map is drawn by combining with GIS map data;
3) the method comprises the following steps of obtaining time-space data corresponding to all rainstorm paths involved by flood disasters in historical time periods according to a rainfall flood time-space database, and displaying the rainstorm paths on a time-space rainfall map, wherein the time-space data comprises the following steps:
31) extracting data of the area station number, the name of the province, the time of occurrence of the flood disaster, the longitude and the latitude of the weather station and the accumulated precipitation amount from a rainfall flood disaster time-space database;
32) screening according to the accumulated precipitation data to obtain data of the accumulated precipitation data meeting the rainstorm standard;
33) sorting the screened data in an ascending order according to time, sorting the accumulated precipitation data in a descending order, and obtaining space-time data corresponding to all rainstorm paths after sorting;
24) marking the time-space data corresponding to all rainstorm paths on a time-space rainfall map by combining with GIS map data;
4) the method comprises the following steps of obtaining corresponding classification models according to space-time data corresponding to all rainstorm paths, and predicting flood disasters according to the classification models, wherein the method specifically comprises the following steps:
41) taking the name of the province, the month, the date and the accumulated precipitation as selected characteristics of a classification model, and making the classification model for the space-time data corresponding to all rainstorm paths;
42) carrying out data statistics on the classification model to obtain a relation graph between the province name, the month, the accumulated precipitation and the occurrence frequency, and carrying out correlation analysis;
43) acquiring a selected feature with highest correlation with the accumulated precipitation, and selecting space-time data corresponding to the selected feature and the accumulated precipitation from the space-time data corresponding to all rainstorm paths;
44) and acquiring the mean value of the space-time data corresponding to the selected characteristics and the accumulated precipitation, and taking the mean value as a prediction result.
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| CN109711725A (en) * | 2018-12-26 | 2019-05-03 | 上海师范大学 | Rainstorm Flood event information processing method and processing device based on image recognition technology |
| CN109783473A (en) * | 2018-12-26 | 2019-05-21 | 上海师范大学 | A kind of information processing method automatically creating Rainfall Disaster data-base recording |
| CN111242404B (en) * | 2019-11-12 | 2022-08-12 | 中国水利水电科学研究院 | Extreme evaluation method and system for heavy rainfall induced flood incident |
| CN113535671A (en) * | 2020-04-15 | 2021-10-22 | 宁波弘泰水利信息科技有限公司 | Rainfall data abnormal value space consistency checking method |
| CN111598344A (en) * | 2020-05-19 | 2020-08-28 | 浙江职信通信科技有限公司 | Flood prevention resource allocation method and system based on WEB GIS |
| CN113408211B (en) * | 2021-07-12 | 2022-02-11 | 中国水利水电科学研究院 | Urban inland inundation water accumulation point water accumulation process prediction method and system based on machine learning |
| CN116050676B (en) * | 2023-03-31 | 2023-06-16 | 四川互慧软件有限公司 | Emergency plan recommendation system based on emergency group events |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101634721A (en) * | 2009-04-15 | 2010-01-27 | 华东师范大学第二附属中学 | Historical data based intelligent early warning system for typhoon and flood |
| CN104408900A (en) * | 2014-11-10 | 2015-03-11 | 柳州师范高等专科学校 | Dynamic optimization based neural network flood warning device and method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006002687A2 (en) * | 2004-06-30 | 2006-01-12 | Swiss Reinsurance Company | Method and system for automated location-dependent recognition of flood risks |
| WO2013063699A1 (en) * | 2011-10-31 | 2013-05-10 | Insurance Bureau Of Canada | System and method for predicting and preventing flooding |
-
2016
- 2016-11-18 CN CN201611015273.9A patent/CN106651004B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101634721A (en) * | 2009-04-15 | 2010-01-27 | 华东师范大学第二附属中学 | Historical data based intelligent early warning system for typhoon and flood |
| CN104408900A (en) * | 2014-11-10 | 2015-03-11 | 柳州师范高等专科学校 | Dynamic optimization based neural network flood warning device and method |
Non-Patent Citations (1)
| Title |
|---|
| 基于WebGIS的台风信息服务系统研究及应用;郑晓阳等;《城市道桥与防洪》;20070415(第04期);全文 * |
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