A novel dynamic interpolation method based on both temporal and spatial correlations

Gao, Shujing; He, Dongjie; Zhang, Zhouzhuo; Tang, Xiaoqian; Zhao, Zhili

doi:10.1007/s10489-022-03815-7

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…These methods can be considered as an expansion of the traditional spatial interpolation method into the temporal dimension. Nevertheless, accurately depicting the intricate ST patterns of air quality poses a great challenge for a single mathematical model (Gao et al., 2023; Tong et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal stacking method with daily‐cycle restrictions for reconstructing missing hourly PM2.5 records

Chen,

2024

Transactions in GIS

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The reliability of hourly PM2.5 data obtained from air quality monitoring stations is compromised as a result of the missing values, thereby impeding the thorough examination of crucial information. In this paper, we present a spatiotemporal (ST) stacking machine learning (ML) method with daily‐cycle restrictions for reconstructing missing hourly PM2.5 records. First, the ST neighbors for the target station with missing values are selected at a daily scale. Subsequently, the non‐null data within the ST neighbors undergo an iterative P‐BSHADE interpolation process for re‐interpolation. Next, a stacking ML model is constructed using the re‐interpolation values and several environmental factors associated with PM2.5 as the predictors, while the observed PM2.5 is taken as the independent variable. Finally, the missing values are reconstructed by inputting the predictors into the trained stacking model. The study utilized hourly PM2.5 data in the Beijing‐Tianjin‐Hebei region as a case study to assess the effectiveness of the proposed method, using daily missing ratios of 10%, 30%, and 50%, respectively. The accuracy of the proposed method was then compared to four contemporary ST interpolation methods. The results indicate that the proposed method exhibits superior performance compared to the classical methods. Specifically, it achieves a reduction in the average root mean square error and mean absolute error by at least 40.6% and 40.1%, respectively. Additionally, the proposed method demonstrates the successful recovery of extreme values in the hourly PM2.5 records, in contrast to the classical methods which often exhibit a tendency to overestimate low values and underestimate high values. Overall, the proposed method presents a viable and efficient approach to recover missing values in the hourly PM2.5 records that demonstrate evident daily periodic patterns.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal stacking method with daily‐cycle restrictions for reconstructing missing hourly PM2.5 records

Chen,

2024

Transactions in GIS

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“…These products aim to provide a continuous and gap-free dataset, addressing the issues of missing data in traditional AOD products [18,19]. The interpolation of missing values is an important part of data preprocessing, and significant efforts have been devoted to data interpolation in recent years [20][21][22][23]. Based on a major concept, "everything is related to everything else, but near things are more related than distant things" [24], various interpolation methods have been developed to solve the problem of missing data.…”

Section: Introductionmentioning

confidence: 99%

Empirical Correlation Weighting (ECW) Spatial Interpolation Method for Satellite Aerosol Optical Depth Products by MODIS AOD over Northern China in 2016

Wang,

Zhang,

Zhou

et al. 2023

Remote Sensing

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Satellite aerosol products are pivotal in studies of regional air quality and global climate change. Compared with accurate in situ observations, satellite measurements provide valuable large-scale atmospheric information. However, limitations such as clouds and retrieval assumptions result in a significant number of missing values in satellite aerosol optical depth (AOD) products, which severely hampers the representativeness. To address this issue, spatial interpolation of the AOD data is necessary to improve data coverage. In this study, one year of AOD observation data from the MODIS C6.1 version was applied to analyze the spatiotemporal correlated characteristics. The statistical parameters were used as dynamic interpolation weights to develop a novel interpolation method called empirical correlation weighting (ECW) based on MODIS AOD over Northern China in 2016. The ECW interpolation results were obtained at a 0.05° resolution (~5 km). The results showed that the spatial coverage of the Deep Blue (DB) and Dark Target (DT) products increased from 43.88% to 70.65% and from 15.04% to 32.62%, respectively. The reconstruction of the ECW method illustrated good agreement with original values in three cases and in two experimental areas. The mean absolute error (MAE) and root mean square error (RMSE) in the two experiments were 0.1171 and 0.0809, and 0.1212 and 0.0838, respectively, indicating that the ECW exhibited the better accuracy than ordinary Kriging (OK) and Thin Plate Spline (TPS). The AERONET validation results indicated that the values of RMSE and MAE were slightly higher after interpolation compared with those before interpolation, maintaining relatively low values, 0.241 and 0.257, 0.140 and 0.150, respectively.

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A spatial interpolation based on neighbor cluster adaptive model with spatial color block clustering algorithm

Zhu,

Chen,

Song

2024

Appl Intell

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A novel dynamic interpolation method based on both temporal and spatial correlations

Cited by 3 publications

References 38 publications

Spatiotemporal stacking method with daily‐cycle restrictions for reconstructing missing hourly PM2.5 records

Spatiotemporal stacking method with daily‐cycle restrictions for reconstructing missing hourly PM2.5 records

Empirical Correlation Weighting (ECW) Spatial Interpolation Method for Satellite Aerosol Optical Depth Products by MODIS AOD over Northern China in 2016

A spatial interpolation based on neighbor cluster adaptive model with spatial color block clustering algorithm

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