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基于衛星遙感監測極端氣象預報數據異常值檢測方法
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黑龍江省牡丹江市氣象局

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Detection method for outliers in extreme weather forecast data based on satellite remote sensing monitoring
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    摘要:

    在遙感數據采集過(guò)程中,由于傳感器故障、氣象條件等原因,可能會(huì )導致少量的異常點(diǎn)出現在采集的數據中,這些異常點(diǎn)可能會(huì )對極端天氣預報的準確性產(chǎn)生負面影響。為此,需要研究一種基于衛星遙感監測極端氣象預報數據異常值檢測方法。基于改進(jìn)K-均值聚類(lèi)算法對缺失的衛星遙感監測極端氣象預報數據進(jìn)行插補,還原數據完整性。劃分星遙感監測極端氣象預報數據區段,提取每個(gè)區段的四個(gè)特征參數,以此為輸入,利用蝙蝠算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò )識別異常區段。計算異常區段中每個(gè)衛星遙感監測極端氣象預報數據的局部離群因子,局部離群因子大于1.0數據為氣象預報數據異常值,以此完成氣象預報數據異常值檢測。結果表明:所提方法插補誤差小于±1.0,可以準確識別異常區段中的異常值,且在不同樣本中的協(xié)調指數高于0.8,檢測效果更好。

    Abstract:

    In the process of remote sensing data collection, due to sensor failures, meteorological conditions, and other reasons, a small number of abnormal points may appear in the collected data, which may have a negative impact on the accuracy of extreme weather forecasting. Therefore, it is necessary to study a method for detecting outliers in extreme weather forecast data based on satellite remote sensing monitoring. Based on the improved K-means clustering algorithm, the missing satellite remote sensing monitoring extreme weather forecast data is interpolated to restore data integrity. Divide extreme weather forecast data sections for satellite remote sensing monitoring, extract four feature parameters for each section, and use them as inputs to optimize BP neural network recognition of abnormal sections using bat algorithm. Calculate the local outlier factor of extreme weather forecast data monitored by each satellite remote sensing in the abnormal section. Data with a local outlier factor greater than 1.0 are considered abnormal values of weather forecast data, in order to complete the detection of abnormal values of weather forecast data. The results show that the interpolation error of the proposed method is less than ± 1.0, which can accurately identify outliers in the abnormal section. Moreover, the coordination index in different samples is higher than 0.8, and the detection effect is better.

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李春艷.基于衛星遙感監測極端氣象預報數據異常值檢測方法計算機測量與控制[J].,2024,32(11):41-47.

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歷史
  • 收稿日期:2023-10-18
  • 最后修改日期:2023-11-28
  • 錄用日期:2023-12-01
  • 在線(xiàn)發(fā)布日期: 2024-11-19
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