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Multivariate statistical prediction method for polar region fixed ice thickness

A forecasting method and multivariate statistical technology, applied in the field of oceanography, can solve problems such as extreme value errors, short forecast timeliness, and errors

Active Publication Date: 2019-05-31
OCEAN UNIV OF CHINA
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Problems solved by technology

But at the same time, considering the limitations of artificial empirical formula forecasting, such as excessive reliance on observation data and short forecast timeliness, there may still be certain errors, especially the extreme value error, which is directly related to the occurrence of extreme sea ice disasters. Whether it can provide effective early warning and take corresponding measures in time to minimize the damage caused by ice conditions

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  • Multivariate statistical prediction method for polar region fixed ice thickness
  • Multivariate statistical prediction method for polar region fixed ice thickness
  • Multivariate statistical prediction method for polar region fixed ice thickness

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Embodiment Construction

[0061] The present invention will be described in detail below in combination with specific embodiments.

[0062] The inventive method such as figure 1 shown, follow the steps below:

[0063] (1) Establish a multiple regression analysis model

[0064] ①Determination of multivariate variables, that is, the ice condition environmental elements of the location of fixed ice

[0065] Suppose a random variable h and multiple variables x 1 ,x 2 ,...,x m There is the following linear relationship between

[0066] h=β 0 +β 1 x 1 +β 2 x 2 +...β m x m +ε (1)

[0067] Among them, h represents the ice thickness sequence of fixed ice; the sequence {x i , i=1,2,...m} represent the local thermodynamic and dynamic elements that affect sea ice production and disappearance, such as sea surface temperature, sea air temperature, seawater salinity, wind speed, flow speed, sensible heat, latent heat, cloud cover, Relative humidity, rainfall, snowfall (snow particle radius), runoff and...

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Abstract

The invention discloses a multivariate statistical prediction method for polar region fixed ice thickness. The multivariate statistical prediction method comprises the following steps: establishing anempirical formula of the fixed ice thickness by adopting a multivariate regression analysis model; And forecasting the sea ice thickness under the action of multiple factors by adopting an artificialneural network model. The method has the beneficial effects that the long-term ice thickness sequence with the ice thickness as the main information is adopted; An artificial experience formula for predicting the ice thickness is established through the multivariate regression analysis model, introduced environmental forced factors are input into the artificial neural network model, the sea ice thickness is forecasted, the forecasting precision can meet the service guarantee requirement, and technical support is provided for north-pole ocean engineering such as exploration of polar mineral resources and north-pole shipping.

Description

technical field [0001] The invention belongs to the technical field of oceanography, and relates to a multivariate statistical prediction method for polar fixed ice thickness. Background technique [0002] With the development of the "Ice Silk Road", Arctic mineral resources and Arctic routes have received increasing attention. Sudden sea ice disasters are likely to cause serious losses and hazards to marine engineering activities, such as damage to offshore platforms, channel blockage, frozen ports, and trapped ships. At present, the detection of polar sea ice fixed ice thickness mainly relies on satellite remote sensing, drilling exploration, ship navigation, submarine sonar detection and numerical simulation and other means. Among them, satellite remote sensing inversion is hindered by atmospheric conditions, and there are large errors in ice thickness inversion results; drilling exploration requires a large labor cost; ships are mainly concentrated in low- and medium-de...

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Application Information

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IPC IPC(8): G06Q10/04G06F17/18G06N3/08
Inventor 王智峰段成林董胜陶山山张日
Owner OCEAN UNIV OF CHINA
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