CN111428915A - Method for predicting silt returning amount of deep-water channel at river estuary based on big data - Google Patents
Method for predicting silt returning amount of deep-water channel at river estuary based on big data Download PDFInfo
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Abstract
The invention provides a prediction method/model for building the silt returning amount of a deep-water channel at a river entrance and a sea mouth based on big data (actually measured data), which can be used for predicting the silt returning amount of the channel (monthly degree) for dredging the deep-water channel at the river entrance and the sea mouth quickly and accurately and provides an important reference index for making a dredging construction capacity arrangement plan. The big data for predicting the silt returning amount of the deepwater channel at the river entrance comprises flow, water temperature, tide level, tidal range, wave energy and channel unit water depth. The method/model for predicting the back silt amount of the deepwater channel can continuously carry out deep learning along with the accumulation of big data, and has the characteristic of self-perfection so as to further improve the prediction precision. The method has important guiding significance for reducing the waste of construction capacity, ensuring the navigation guarantee rate of the channel and the like.
Description
Technical Field
The invention relates to a dredging and maintaining method for a deep water channel at a river estuary, in particular to a method for determining the along-the-way silt return amount of the deep water channel for dredging and maintaining the deep water channel at the river estuary, and more particularly to a method for determining the dredging and maintaining of the deep water channel at a Yangtze river estuary along-the-way monthly silt return amount of the deep water channel.
Technical Field
Generally, a deep water channel at a river entrance is a throat of a water channel and a traffic hub at sea. For example, the Yangtze river is a main channel and a main framework in a water transportation system of China, and a channel at the Yangtze river mouth is a throat of a golden water channel of the Yangtze river and is the only traffic hub for communicating an economic zone of the Yangtze river with a coastal economic zone and a road of silk at sea of China, so that the smoothness of the channel at the Yangtze river mouth, particularly the smoothness of a deep water channel, is ensured, and is in line with the global development of national economy and the promotion of strong traffic strategies.
In addition, in rivers that are at home and abroad, deep water channels at sea mouths, in particular deep water channels with strong silt back at sea mouths, such as mississippi river channels, time river sea channels, amazon channels, france giron river mouth channels, dutch red harbor channels and the like, the problem of channel dredging management is mainly focused on the problem of dredging efficiency optimization, and the research on channel dredging is generally researched from a plurality of points of ship equipment improvement, dredging operation automation, construction management measure improvement, environmental protection dredging, dredging soil diffusion and the like.
Therefore, foreign dredging technology is mainly embodied in that ships are equipped more advanced, all monitoring can be realized by using a computer, all dredging construction, particularly after dredging, automatic display of a dredging effect graph by software can be realized, different water depths and shallow sweeping stages are displayed by different colors, electronic version data of mapping are input into the computer every time, different water depth areas, which shallow dredging is displayed by different color blocks, pertinence is strong, and dredging efficiency is improved.
However, although the method can achieve automation and efficiency optimization of dredging operation of the ship in the dredging process through improvement of the ship equipment, the method does not relate to a construction management method based on dynamic change of dynamic prediction of the on-way back-flow quantity of the deepwater channel at the sea entrance, environmental protection dredging, dredging soil diffusion, construction organization management arrangement, ship allocation, construction plan making and the like.
On the other hand, in some deepwater channels, especially, for example, the deepwater channel at the estuary, during the maintenance and dredging process of the deepwater channel at the estuary, the configuration of the dredging ship per month changes with the actual water depth and the sediment deposition amount of the channel dredging unit, only 2 dredging ships are generally needed when the water depth is good and the sediment deposition is small, and more than 10 dredging ships are needed when the local water depth is shallow and the sediment deposition is large; thus, excessive and insufficient vessel deployment can result in substantial waste of construction capacity or difficulty in meeting channel maintenance water depth standards. Therefore, the waste of construction capacity and the guarantee rate of channel navigation are difficult to avoid.
For example, the planning pattern of the channel at the estuary is "one main channel and two auxiliary channels", that is, the deepwater channel at the estuary comprises:
a main channel of the deep-water channel of the estuary with the water depth of 12.5 m;
the target water depth distribution is 8.0m and 6.0m, and the 'auxiliary channel' of the first-stage engineering north and south channel is implemented at the end of 2018 years;
the branch channel is a north branch channel.
The arrangement of channel maintenance and dredging construction is one of the important factors for determining the safe and stable operation of the channel. Aiming at the desilting sections and the distribution of the on-way desilting amount in specific time periods of specific deepwater channels, the method combines specific dredging construction capacity, and ensures that the channels meet the water depth maintenance standard by using the most reasonable dredging ship configuration so as to reduce the waste of the construction capacity and improve the navigation guarantee rate of the channels.
The channel silting amount of the main channel section of the deepwater channel at the estuary is large, the silting sections are concentrated, maintenance dredging needs to be carried out all the year round, and the north channel deepwater channel generates about 5000-7000 million of dredged soil every year, so that the main channel of the deepwater channel at the estuary and the north channel need to be maintained and dredged all the year round.
Therefore, the prior patent application with the application number of 'CN 201710915252.0' and the invention name 'a method for determining the minimum along-the-way monthly dredging amount of an entry deepwater channel' of the applicant provides a method for determining the minimum along-the-way monthly dredging amount of the entry deepwater channel, and the method mainly aims at the construction organization management arrangement, the ship allocation and the construction plan formulation of all dredging ships in the whole channel so as to achieve the aim of optimizing the dredging management. The method provides a reference basis for the total capacity arrangement of channel lunar dredging construction.
However, this method mainly considers: and predicting the minimum monthly dredging amount by using the reference water depth value of the minimum dredging amount, the water depth of the bed surface of the initial channel, the sedimentation strength of the channel dredging unit, the bed surface of the lunar bottom prediction channel and the like. The flow, water temperature, tide level, tidal range, wave energy, water depth of the channel unit and internal relation of data are not adopted. It is difficult to continuously refine the re-establishment and self-refinement of the method/model to further improve the prediction accuracy as large data accumulates.
In addition, before the method is provided, the monthly silt amount of the deep water channel at the estuary is predicted mainly by a large-scale mathematical model, the modeling and calculation time is long, more than 2 days are generally needed, and moreover, the prediction of the channel silt amount under the condition of wind waves is an academic problem, the precision of the prediction is limited by the coupling calculation precision of a plurality of models such as a weather model, a wave model, a water and sand dynamic transportation model and the like, so that the calculation precision of the minimum dredging amount is difficult to further improve when the minimum dredging amount is influenced by the wind waves.
Disclosure of Invention
In order to overcome the problems and continuously improve the dredging and maintaining method of the deep water channel of the river estuary, the invention aims at:
the invention provides a prediction method for establishing a dredging amount of a deep-water channel at a river entrance based on measured data (big data), which is used for dredging and maintaining the deep-water channel, and more particularly relates to a method for predicting the distribution and the total amount of a monthly dredging amount of the deep-water channel at a Yangtze river mouth along a channel dredging unit by using a dredging and maintaining method for the deep-water channel at a Yangtze river mouth and determining the monthly dredging amount of the deep-water channel at the Yangtze river mouth along the way, thereby providing scientific basis for the channel maintenance dredging construction arrangement and improving the dredging and maintaining efficiency of the deep-water channel.
The invention relates to a prediction method/model for building the silt returning amount of a deep-water channel at a river entrance to the sea based on measured data (big data), which can be used for rapidly and accurately predicting the lunar silt returning amount of the channel and is used for dredging the deep-water channel at the river entrance to the sea, thereby providing important reference indexes for making a lunar dredging construction capacity arrangement plan.
The method provided by the invention adopts the flow, water temperature, tide level, tidal range, wave energy, water depth of the channel unit, internal relation of data and other big data, the calculation efficiency and the calculation precision are both high, and the desired prediction result can be obtained within 5 minutes. In addition, the method can directly give the channel back silting under the condition of wind waves through big data statistics of wave energy, continuously improve the reestablishment and self-improvement of the method/model along with the accumulation of big data so as to further improve the prediction precision, and has important guiding significance for reducing the waste of construction capacity, ensuring the channel navigation guarantee rate and the like.
The technical scheme of the invention is as follows:
a method for predicting the back siltation amount of a deep-water channel at a river entrance to the sea based on big data is used for back siltation dredging of the deep-water channel at the river entrance to the sea to make a construction capacity arrangement plan for monthly back siltation dredging, and is characterized by comprising the following steps:
step 1), channel units used for predicting the monthly silt returning amount of a main channel of a deepwater channel at an entrance are determined, the unit number is I, I is 1,2, … I, and I is the total number of channel dredging units:
wherein delta l is the length of the channel dredging unit, L is the total length of the channel, the along-the-way back silt amount of the deep water channel is determined by taking the moon as a unit, and the channel dredging unit is taken as a statistical range;
step 2), determining the m group of measured values of main influence factors for predicting monthly silt return amount of the representative stations of the deep-water channel main channel at the river entrance to the sea:
flow (Q) of representative station at upstream of main channel at estuary of riverm，m3)；
The longitudinal middle position of the main channel at the estuary of the river represents the water temperature (T) at the stationm，℃)；
The position of the longitudinal middle part of the main channel at the entrance of the river represents the tide level (H) at the stationm，m)；
The position of the longitudinal middle part of the main channel at the estuary of the river represents the tidal range (delta H) at the stationm，m)；
The unit water depth (D) at the longitudinal middle position of the main channel at the estuary of the riveri,mM), and
wave energy at the station represented by the lower section of the main channel at the entrance of the river (E)mKilowatt-hour/meter);
here, i is a channel unit number;
m is 1,2, … M, and M is the total number of statistical data;
step 3), based on M groups of statistical data, the back silt amount y of each group of channel dredging units ii,mThe following were used:
yi,m＝α0,i+Qmα1,i+Tmα2,i+Hmα3,i+ΔHmα4,i+Emα5,i+Di,mα6,i+i,m(1)
here α0,i～α6,iFor the regression coefficients obtained based on the M sets of data,i,mto calculate the prediction error.
According to the invention, it is preferred that i is from 30 to 100.
The channel dredging unit is defined herein as a channel dredging unit which segments a long channel in the longitudinal direction for the purpose of facilitating dredging management, each segment being a channel dredging unit.
The size of the channel dredging units does not influence the use of the method, and the channel dredging units can be divided according to the actual dredging condition of the channel. When the division of the channel unit is smaller, the channel unit describes the distribution of the channel back silt more accurately along the way, which is helpful for improving the accuracy of the result calculated by the method, but obviously increases the difficulty and workload of measurement and data statistics. The size of the channel dredging units is divided, and the using steps and the using process of the method are not influenced.
According to the invention, the "representative station" is determined according to the following (taking the main channel of the deep water channel at the estuary as an example):
the 'representative station' of the flow of the main channel at the river estuary is a hydrological station (such as a Datong station) at the upstream of the main channel at the river estuary, and the data of the station has great influence on the back silting of the main channel and is basically not influenced by the ocean tides;
the representative station of the water temperature, the tide level and the tide difference of the main channel of the river entrance to the sea selects a hydrological station (such as a station in a north trough) at the longitudinal middle position (namely along the central line of the main channel) of the main channel, and the data of the station basically represents the general law of the main channel along the way;
the lower section of the main channel represents wave energy at a station, the transmission characteristics of waves are that the waves are transmitted from the outside of a sea entrance to the near shore, so that the lower section of the main channel (a hydrological station-a cowskin reef station at the gate section of the north notch) is selected, and the data of the lower section of the main channel represents the characteristics of the waves transmitted by the open sea and directly influencing the back silting of the main channel.
The data of 'representative stations' of water temperature, tide level and tide difference of main channel of river estuary have selectivity, and under the condition that the data difference of each station is less than 10-30%, every Q value can be obtainedm、Tm、 Hm、ΔHmAnd EmThe value of the whole channel is uniform.
Referring to fig. 2 and 3, in practice, the acquisition of field data requires a large investment in capital and the construction of multiple sites, which is difficult, so that the data of existing sites can be selected and utilized as much as possible according to the above principles.
Here α0,i～α6，iThe regression coefficient is obtained by least square estimation based on known data and a multiple linear equation/model, and has no value range.
For example, the total length of the deep water channel of the "main channel" section of the estuary is about 92km, the length is divided according to the requirements of construction and management, the division schematic diagram is shown in fig. 4, the position distribution and the main unit number are shown in fig. 5 and fig. 6, and the unit length is about 2 km.
The method for predicting the sludge return amount of the deep-water channel at the river entrance to the sea based on the big data is characterized in that the sludge return amount of channel dredging units i of M groups of channels is calculated;
yi＝(yi，1，yi，2，…，yi，m)′
(yi,1，yi，2，…，yi，m) ' expression matrix (y)i，1，yi，2，…，yi，m) The transposition calculation of (1);
writing the calculation formula (1) of the M groups of data into a vector form, wherein the backset amount can be described by the following multivariate linear equation/model to be an independent variable, namely a plurality of influence factors and a dependent variable, namely a multivariate linear relation exists in the backset amount of the channel:
yi＝Xiαi+i(2)
here, the dependent variable yi＝(yi,1，yi,2,…，yi，m) ', is the back-silting amount statistic value of M groups of data of unit i; m is 1,2, … M, M is the total number of statistical data, and M can be more than or equal to 12 for data analysis of statistical years of data,
in the formula (2), XiIs represented as follows:
it is a matrix of M × 7, the first column corresponding to the constant terms of the equation/model, thus all elements are 1, the remaining X' sm(1-6)iThe column is independent variable and corresponds to 6 back-silting influence factors of channel unit i, namely Qm、Tm、Hm、ΔHmAnd Em、Di,m，
In formula (2), αiRepresentation αi＝(α0,i,α1,i,…,α6,i) ', it meansThe regression coefficient of the i < th > dredging unit back-flow multi-linear calculation model (α)0,i,α1,i,…,α6,i) ' representation matrix (α)0,i，α1，i，…，α6，i) The transposition calculation of (1);
in the formula (2), the reaction mixture is,ito representi＝(1，i，2，i，…，m,i) ', which represents the predicted random error of the amount of backset for each of the M sets of data M, where: (1，i,2,i,…,m,i) ' representation matrix1,i,2,i,…,m,i) The transpose calculation of (2).
The method for predicting the silt amount of the deep-water channel at the river entrance and the sea mouth based on the big data is characterized in that the silt amount of the deep-water channel along the way is determined by taking a channel unit as a statistical unit.
For example, in the total length of the deep water channel of the 'main channel' section of the estuary, which is about 92km, the total number of the channel units is 46 according to the requirements of construction and management.
The size of the channel unit is about 2km according to the requirement of dredging management, the one-time dredging operation distance of a common dredging ship is considered, and the convenience of channel dredging and desilting data statistics is also considered.
The method for predicting the silt returning amount of the deep-water channel at the river entrance and the sea mouth based on the big data is characterized in that α in the formula0,i～α6,iThe value law of the coefficients obtained by regression analysis is as follows:
TABLE 1 regression coefficient values
The method for predicting the silt returning amount of the deep-water channel at the river entrance and the sea mouth based on the big data is characterized in that when the measured data of M groups and 6 main influence factors and the silt returning amount of the measured data are known, a least square method is adopted to obtain a regression coefficient α in the formula (2)iThe calculation formula of (2) is as follows:
αi≈(Xi′Xi)-1Xi′yi(3)
here, (X)i′Xi)-1Representation matrix Xi′XiThe inverse matrix of (2).
α is obtained by least squares calculation of the formula (3)iSo that the error in the formula (1)i,mThe impact is minimal, whereby the multivariate linear model/equation for the calculation and prediction of the amount of backset of the final available channel unit i is described as follows:
yi,m＝α0,i+Qmα1,i+Tmα2,i+Hmα3,i+ΔHmα4,i+Emα5,i+Di,mα6,i(4)。
the method for predicting the silt returning amount of the deep-water channel at the river entrance and the sea mouth based on the big data is characterized in that according to the formula (4),
yi,m＝α0,i+Qmα1,i+Tmα2,i+Hmα3,i+ΔHmα4,i+Emα5,i+Di,mα6,i(4)
forecasting the back silt amount of the channel;
here Qm、Tm、Hm、ΔHm、Em、Di,mTo predict the value of the corresponding independent variable of the month,
where J is 1,2 to J, J is the statistical number (unit: year) of the independent variable data corresponding to the predicted month,
Di,mand the value is taken as the average water depth of each channel unit in the beginning of the month, and the channel unit water depth is measured and obtained on site.
The method for predicting the silt content of the deepwater channel at the river entrance and the sea mouth based on the big data is characterized in that the J value is gradually increased along with the gradual increase of the M value, the accumulated data content of the silt content of the channel is gradually increased, and the regression coefficient estimated value α can be performed once when the J value is increased for 1 yeariAnd (4) recalculating, thereby continuously improving the prediction accuracy of the prediction model of the back silt quantity.
The J value is gradually increased along with the gradual increase of the M value, the accumulated data volume of the channel back silt quantity is gradually increased, and the regression coefficient estimation value can be carried out once every 1 year of the increase of the J
And (4) recalculating, thereby continuously improving the prediction accuracy of the prediction model of the back silt quantity.
The invention discloses a method for predicting the silt returning amount of a deep-water channel at a river entrance and a sea mouth based on big data, which is characterized in that,
the establishment of the method introduces more universal commercial software in the world, namely software SPSSV19 developed by IBM company, and utilizes the analysis-regression-linearity function to carry out multiple linear regression analysis of 6 main influence factors and the channel back-silting amount to obtain a model calculation coefficient α0,i～α6,i。
The method for predicting the silt returning amount of the deep-water channel at the river entrance and the sea mouth based on the big data is characterized in that the wave energy E is calculated according to the following formula:
in the formula:
h is the wave height (m),
k is the number of waves (n),
h is the depth of water (m);
T is the wave period(s),
g is the acceleration of gravity (m)2/s)。
The method for predicting the silt return amount of the deep-water channel at the river estuary is characterized in that the deep-water channel at the estuary is a section of water area at the estuary, the length of the deep-water channel is about 150-250 km, the plane of the estuary is in a horn shape, and the width of the river surface at the narrow opening end is 10-20 times of the width of the river surface at the wide opening.
The method for predicting the silt return amount of the deep-water channel at the river estuary based on the big data is characterized in that the estuary is a three-stage branched and sandy high-turbidity delta estuary.
The method for determining the minimum along-the-way monthly dredging amount of the deepwater channel at the sea entrance is characterized in that the channel unit size is 1.5-2.5 km in length according to the dredging management requirement.
The method for predicting the silt returning amount of the deep-water channel at the river estuary is characterized by comprising the steps of setting an auxiliary channel of the deep-water channel at the river estuary and a method for predicting the silt returning amount of a branch channel besides a main channel, wherein the method is similar to the main channel. The invention discloses a method for predicting the silt returning amount of a deep-water channel at a river entrance and a sea mouth based on big data, which is characterized in that,
the deep water channel at the sea entrance refers to a section of water area at the sea entrance, the length of the deep water channel is about 150-250 kilometers, the sea entrance plane is in a horn shape, and the width of the river surface at the narrow opening is 10-20 times of that of the river surface at the wide opening.
Advantages of the invention
The method adopts the big data of flow, water temperature, tide level, tidal range, wave energy, water depth of a channel unit, internal relation of the data and the like. The method/model can be continuously improved to be reestablished and self-improved along with the accumulation of big data so as to further improve the prediction precision. The calculation speed is high, and all data needing prediction can be obtained within 1 hour
In addition, before the method is provided, the monthly silt returning amount of the deep water channel at the estuary is mainly predicted by a large-scale mathematical model, the modeling and calculation time is long, generally more than 2 days are needed, and the method provided by the invention can obtain the desired prediction result in only 5 minutes. In addition, the prediction of the channel silt return under the stormy wave condition is an academic problem, the precision of the prediction is limited by the coupling calculation precision of a plurality of models such as a weather model, a wave model, a water and sand dynamic transport model and the like, so that the calculation precision of the minimum dredging amount is difficult to further improve when the calculation precision is influenced by the stormy wave.
Drawings
Fig. 1 shows the distribution of the amount of returned sludge of each dredging unit in the deepwater channel at the estuary (2012).
Fig. 2 is a schematic diagram of a hydrological station of a deepwater channel at a estuary.
Fig. 3 is an enlarged schematic block representation of fig. 2.
Fig. 4 is a schematic diagram of a deepwater channel dredging unit at a Yangtze river mouth.
Fig. 5 is a schematic diagram of the positions of the dredging units of the deep water channel at the estuary and the numbering arrangement of the main units.
FIG. 6 is a schematic diagram of an operation menu of the linear regression of the SPSS V19 multivariate data.
FIG. 7 is a schematic diagram of an operation menu of the linear regression of the SPSS V19 multivariate data.
Fig. 8 is a schematic diagram of comparison between predicted backset amount and measured value of a deepwater channel at a Yangtze river estuary in 2019 in 7 months.
Detailed Description
Example 1 main channel of deepwater channel of estuary.
(1) Selection of main influence factors (big data) used for monthly backset prediction
According to a general mechanism formed by the monthly silt returning amount of a channel, the channel is mainly influenced by external conditions such as hydrology, terrain and the like of different months; on the basis of collecting and sorting the existing research results, the invention selects the following 6 main influence factors to establish a big data analysis base.
The method comprises the following steps: the flow of the upstream representative station site, the water temperature of the Yangtze river mouth representative station site, the tide level of the Yangtze river mouth representative station site, the tide difference of the Yangtze river mouth representative station site, the on-way water depth of the deepwater channel of the Yangtze river mouth and the wave energy of the Yangtze river mouth representative station site.
(2) Method for evaluating flow of upstream representative station
Flow (Q) of representative station at upstream of main channel at estuary of riverm，m3)；
Selecting a Yangtze river hydrological station, namely a large-flow hydrological station, from the upstream representative station, and averaging monthly values according to actual measurement flow data of the station (the actual measurement data of the station is published openly) to serve as input data for predicting the back-up amount; for 2016-2018, the monthly average flow data are as follows:
table 2 flow data, units: m is3
(3) Method for taking value of water temperature at representative site of Yangtze estuary
The longitudinal middle position of the main channel at the estuary of the river represents the water temperature (T) at the stationm，℃)；
Taking a hydrological observation station at the Yangtze river estuary, namely a hydrological station in the north trough (relevant data can be obtained from relevant management departments), and according to the actually measured hydrological data of the station, monthly averaging is taken as input data for predicting the back-silting amount. For 2016-2018, the monthly water temperature data are as follows:
TABLE 3 Water temperature data, units, degrees
(4) Method for dereferencing tide level at Yangtze estuary representative station
The position of the longitudinal middle part of the main channel at the entrance of the river represents the tide level (H) at the stationm，m)；
Taking the hydrological observation station at the Yangtze river estuary, namely a north trough (relevant data can be obtained from relevant management departments), and according to the actually measured hydrological data of the station, averaging monthly to be used as input data for predicting the back-up amount. For 2016-2018, the monthly tide level data are shown in the following table:
table 4 tidal level data, units: m is
(5) Tidal range dereferencing method at Yangtze estuary representative station
The position of the longitudinal middle part of the main channel at the estuary of the river represents the tidal range (delta H) at the stationm，m)；
Taking the hydrological observation station at the Yangtze river estuary, namely a north trough (relevant data can be obtained from relevant management departments), and according to the actually measured hydrological data of the station, averaging monthly to be used as input data for predicting the back-up amount. For 2016-2018, the tidal range data of the month are shown in the following table:
table 5 tidal range almanac data, units: m is
(6) Method for taking value of on-way water depth of deepwater channel at Yangtze river mouth
The unit water depth (D) at the longitudinal middle position of the main channel at the estuary of the riveri,m，m)
And (3) according to actual measured terrain of a channel at the beginning of the forecast month and month (relevant data can be obtained from relevant management departments) as input data for forecasting the back silt amount. For 2016, the water depth values for each channel dredging unit at the beginning of the month are shown in the following table:
table 6 water depth data on water course, unit: m is
(7) Method for evaluating wave energy at Yangtze estuary representative station
Wave energy at the station represented by the lower section of the main channel at the entrance of the river (E)mKilowatt-hour/meter);
a cow leather reef hydrological station (related data can be obtained from related management departments) which is a hydrological observation station at the Yangtze river estuary is taken, and the wave height and the period are mainly determined according to the actually measured hydrological data of the station.
Considering that the general waves can not obviously influence the siltation of the channel at the estuary, the average effective wave height (about 0.7m based on the statistics of measured data for many years) of the cowhide reef hydrological station is used as a critical judgment index of the influence of the waves, namely the waves with the effective wave height larger than 0.7m are selected to calculate and obtain the wave energy, and the total monthly degree is obtained through statistics and is used as input data for predicting the siltation amount.
The wave energy E is a general statistic whose general formula is as follows:
in the formula: h is wave height, k is wave number, and H is water depth; and sigma-2 pi/T is the wave circle frequency, T is the wave period, and g is the gravity acceleration.
For 2016-2018, the monthly wave energy data are as follows:
TABLE 7 wave energy calendar year data, kilowatt-hour/meter
(8) Establishment of prediction model of lunar silt return of channel
The invention adopts a general method of data analysis, namely a multivariate linear regression analysis method, to build a silt return prediction model. The establishment of the method introduces more general commercial software in the world, namely software SPSS V19 developed by IBM company, and utilizes the analysis-regression-linearity function (figure 5) to carry out multivariate linear regression analysis of 6 main influence factors and the channel silt return quantity to obtain model calculation coefficients.
The invention provides a prediction target which takes the monthly sludge return of each channel dredging unit as data fitting; taking the channel dredging unit K unit as an example, namely the back silt amount is a dependent variable, and the other 6 are independent variables.
The statistics of the main influence factors and the actually measured backset amount in different months are as follows:
TABLE 8 channel dredging Unit-6 main influencing factors and measured sludge amount statistics for different months for K Unit
Taking the above K unit as an example, α based on formula (3)i≈(Xi′Xi)-1Xi′yi(ii) a The coefficient matrix of the backset amount prediction model of all the units can be obtained as follows:
TABLE 9 coefficient matrix for channel dredging unit sludge return prediction
From the above table, the calculation coefficient of equation (4) of the monthly sludge return calculation model of the channel unit is obtained, α0,i～α6,i。
(9) Channel monthly silt return amount prediction step and process
According to the established prediction model, namely the formula (4) and the table 9, the channel back silt amount can be predicted.
Taking the channel back silting amount of 7 months in 2019 as an example: assuming that the value is unknown, 6 main influence factors from about three years (2016-2018) are taken as variables in the formula (4), namely, the average value of the flow, the water temperature, the tide level, the tide difference and the wave energy and the measured early monthly water depth of the channel unit are taken as follows:
TABLE 10 prediction input data of the sludge return amount of channel dredging unit-7 months in 2019
The calculation formula is as follows:
here, J is 1,2,3, J is 3(2016 to 2018), m is 7 (7 months in 2019), and Di,mThe average water depth of each channel unit in early 7 months in 2019 is shown, and the channel units I are III-A-III-I.
According to the established prediction model, formula (4):
yi,m＝α0,i+Qmα1,i+Tmα2,i+Hmα3,i+ΔHmα4,i+Emα5,i+Di,mα6,i(4)
inputting the data in the tables 9 and 10, calculating to obtain a predicted value, and comparing the calculated predicted value with the measured value of the channel back silt amount in 7 months in 2019 with reference to a figure 7; from the calculation result, the coincidence of the two is high along the navigation channel, and the total error of the whole navigation channel is about 9%.
According to the specification and according to the precision requirement of JTS/T231-2-2010 of coast and estuary tide sediment simulation technical regulation, the allowable deviation between model prediction calculation and the actually measured average scouring thickness is 30%, and the rationality and the accuracy of the prediction method described by the invention can be known through comparison.
According to the invention, a new method for predicting the lunar silt content of the channel based on big data (flow, water temperature, tide level, tidal range, wave energy and channel unit water depth) is provided.
The method can continuously reestablish the model along with the accumulation of big data, has the characteristic of self-perfection, and can further improve the prediction precision.
Since the implementation of the method in 1 month in 2019, the maintenance amount of the deepwater channel at the estuary is in a reduction trend, the deepwater channel is reduced from about 5800 ten-thousand square in 2016-2018 to 5400 ten-thousand square in 2019, the prediction error of the total desilting amount of the channel in 2019 is not more than 5%, the standard navigation guarantee rate of the deepwater channel basically meets the requirement of 100%, the method plays a positive role in reducing the waste of dredging construction capacity, the standard of channel maintenance water depth and the like, and obvious economic benefit and management benefit are brought.
At present, the management method based on the invention is completely integrated into the dredging maintenance management of the deep water channel of the Yangtze river mouth, and continuously plays a remarkable role.
Claims (12)
1. A method for predicting the back siltation amount of a deep-water channel at a river entrance to the sea based on big data is used for back siltation dredging of the deep-water channel at the river entrance to the sea to make a construction capacity arrangement plan for monthly back siltation dredging, and is characterized by comprising the following steps:
step 1), channel units used for predicting the monthly silt returning amount of a main channel of a deepwater channel at an entrance are determined, the unit number is I, I is 1,2, … I, and I is the total number of channel dredging units:
wherein delta l is the length of the channel dredging unit, L is the total length of the channel, the along-the-way back silt amount of the deep water channel is determined by taking the moon as a unit, and the channel dredging unit is taken as a statistical range;
step 2), determining the m group of measured values of main influence factors for predicting monthly silt return amount of the representative stations of the deep-water channel main channel at the river entrance to the sea:
flow (Q) of representative station at upstream of main channel at estuary of riverm，m3)；
The longitudinal middle position of the main channel at the estuary of the river represents the water temperature (T) at the stationm，℃)；
The position of the longitudinal middle part of the main channel at the entrance of the river represents the tide level (H) at the stationm，m)；
The position of the longitudinal middle part of the main channel at the estuary of the river represents the tidal range (delta H) at the stationm，m)；
The unit water depth (D) at the longitudinal middle position of the main channel at the estuary of the riveri，mM), and
wave energy at the station represented by the lower section of the main channel at the entrance of the river (E)mKilowatt-hour/meter);
here, i is a channel unit number;
m is 1,2, … M, and M is the total number of statistical data;
step 3), based on M groups of statistical data, the back silt amount y of each group of channel dredging units ii，mThe following were used:
yi，m＝α0，i+Qmα1，i+Tmα2，i+Hmα3，i+ΔHmα4，i+Emα5，i+Di，mα6，i+i，m(1)
here α0，i～α6，iFor the regression coefficients obtained based on the M sets of data,i，mto calculate the prediction error.
2. The method for predicting the amount of silt in the deepwater channel at the river entrance and the sea mouth based on the big data as claimed in claim 1, wherein α in formula (1)0，i～α6，iThe coefficients obtained by regression analysis have the following values:
α0，i：-12.2～37.5，
α1，i：0.0000278～0.0000891，
α2，i：-0.275～0.0337，
α3，i：-3.44～5.42，
α4，i：-5.82～3.42，
α5，i：-0.00862～0.00195，
α6，i：-3.3～0.827。
3. the method for predicting the silt returning amount of the deepwater channel at the river entrance and the sea mouth based on the big data as claimed in claim 1, wherein the silt returning amounts of the channel dredging units i of the total M groups of each M groups;
yi＝(yi，1，yi，2，…，yi，m)′
(yi，1，yi，2，…，yi，m) ' expression matrix (y)i，1，yi，2，…，yi，m) The transposition calculation of (1);
writing the calculation formula (1) of the M groups of data into a vector form, wherein the backset amount can be described by the following multivariate linear equation/model to be an independent variable, namely a plurality of influence factors and a dependent variable, namely a multivariate linear relation exists in the backset amount of the channel:
yi＝Xiαi+i(2)
here, the dependent variable yi＝(yi，1，yi，2，…，yi，m) ', is the back-silting amount statistic value of M groups of data of unit i; m is 1,2, … M, M is the total number of statistical data, and M can be more than or equal to 12 for data analysis of statistical years of data,
in the formula (2), XiIs represented as follows:
it is a matrix of M × 7, the first column corresponding to the constant terms of the equation/model, thus all elements are 1, the remaining X' sm(1-6)iThe column is independent variable and corresponds to 6 back-silting influence factors of channel unit i, namely Qm、Tm、Hm、ΔHmAnd Em、Di，m，
In formula (2), αiRepresentation αi＝(α0，i，α1，i，…，α6，i) ', it representsIs the regression coefficient of the i-th dredging unit back-flow multi-linear calculation model (α)0，i，α1，i，…，α6，i) ' representation matrix (α)0，i，α1，i，…，α6，i) The transposition calculation of (1);
in the formula (2), the reaction mixture is,ito representi＝(1，i，2，i，…，m，i) ', which represents the predicted random error of the amount of backset for each of the M sets of data M, where: (1，i，2，i，…，m，i) ' representation matrixi，i，2，i，…，m，i) The transpose calculation of (2).
4. The method for predicting the amount of silt in the deepwater channel at the river entrance and at the sea mouth based on the big data as claimed in claim 3, wherein when the measured data of the total M groups and 6 main influence factors and the amount of silt thereof are known, the least square method is adopted to obtain the regression coefficient α in the formula (2)iThe calculation formula of (2) is as follows:
αi≈(Xi′ Xi)-1Xi′yi(3)
here, (X)i′ Xi)-1Representation matrix Xi′ XiThe inverse matrix of (2).
α is obtained by least squares calculation of the formula (3)iSo that the error in the formula (1)i，mThe impact is minimal, whereby the multivariate linear model/equation for the calculation and prediction of the amount of backset of the final available channel unit i is described as follows:
yi，m＝α0，i+Qmα1，i+Tmα2，i+Hmα3，i+ΔHmα4，i+Emα5，i+Di，mα6，i(4)。
5. the method for predicting the amount of silt in the deepwater channel at the river entrance and the sea mouth based on the big data as claimed in claim 3, wherein according to the formula (4),
yi，m＝α0，i+Qmα1，i+Tmα2，i+Hmα3，i+ΔHmα4，i+Emα5，i+Di，mα6，i(4)
forecasting the back silt amount of the channel;
here Qm、Tm、Hm、ΔHm、Em、Di，mTo predict the value of the corresponding independent variable of the month,
where J is 1,2 to J, J is the statistical number (unit: year) of the independent variable data corresponding to the predicted month,
Di,mand the value is taken as the average water depth of each channel unit in the beginning of the month, and the channel unit water depth is measured and obtained on site.
6. The method for predicting the amount of silt in the deepwater channel at the river entrance and at the sea mouth based on the big data as claimed in claim 5, wherein the J value is gradually increased along with the gradual increase of the M value, the accumulated data amount of the silt in the channel is gradually increased, and the regression coefficient estimated value α can be performed once every 1 year of the increase of JiIs recalculated toAnd calculating so as to continuously improve the prediction precision of the prediction model of the back silt quantity.
7. The method for predicting the amount of sludge in deep water channel at river entrance and at sea level based on big data as claimed in claim 1, wherein the establishment of the method introduces more general commercial software worldwide-software SPSSV19 developed by IBM corporation, and performs multiple linear regression analysis of 6 main influence factors and channel sludge amount by using the analysis-regression-linear function thereof, and obtains model calculation coefficients α0,i～α6,i。
8. The method for predicting the silt returning capacity of the deep-water channel at the river entrance and the sea mouth based on the big data as claimed in claim 1 or 3, wherein the wave energy E is calculated by the following formula:
in the formula:
h is the wave height (m),
k is the number of waves (n),
h is the depth of water (m);
sigma 2 pi/T is the wave circle frequency(s)-1)，
T is the wave period(s),
g is the acceleration of gravity (m)2/s)。
9. The method as claimed in claim 1, wherein the deep water channel at the estuary of the river has a length of about 150 and 250 km, and is flared on the plane of the estuary, and the width of the river surface at the narrow end is 10-20 times of the width of the river surface at the wide end.
10. The method for predicting the silt return amount of the deep-water channel at the river estuary based on the big data as claimed in claim 1, wherein the estuary is a three-stage branched, sandy and high turbidity delta estuary.
11. The method for determining the minimum along-the-way monthly dredging amount of the deepwater channel at the sea entrance according to claim 1, wherein the channel unit size is 1.5-2.5 km in length according to the dredging management requirement.
12. The method for predicting the silt amount of the deep-water channel at the river estuary according to claim 1, which is similar to the main channel, and is characterized in that an auxiliary channel and a branch channel silt amount prediction method of the deep-water channel at the river estuary are arranged in addition to the main channel.
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