An enhanced dual IDW method for high-quality geospatial interpolation

Li, Zhanglin

doi:10.1038/s41598-021-89172-w

Cited by 16 publications

(8 citation statements)

References 36 publications

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“…Fourier curve fitting [14] was performed on the spatial mean of each block in the temporal domain to obtain the spatial mean of each block at each observation moment because it has good performance on data with certain temporal repeating patterns. Next, the demeaned deformation data were spatially modeled by combining the enhanced inverse-distance weighting method [15] and the least squares method [16] to consider the spatial location of the site and its deformation characteristics. Finally, support vector regression (SVR) [17] was used to estimate the residuals of spatial modeling in both the temporal and spatial domains and obtain deformation results with high precision and high spatiotemporal resolution.…”

Section: Methodsmentioning

confidence: 99%

“…Considering that the deformation value of the site is related to its geographic location and specific deformation characteristics, we divided the spatial basis into two parts for construction. One part is related to the site location and is calculated using the improved inverse-distance weighting method [15], and the other part is related to the deformation characteristic and is calculated using the least squares method [16]. The components are formulated as follows:…”

Section: Spatial Modelingmentioning

confidence: 99%

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Fusion of Spatially Heterogeneous GNSS and InSAR Deformation Data Using a Multiresolution Segmentation Algorithm and Its Application in the Inversion of Slip Distribution

et al. 2022

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The fusion of global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) deformation data can leverage the advantages of GNSS high temporal resolution and InSAR high spatial resolution, and obtain more abundant deformation data for constraints on geophysical structural and mechanical parameters. Existing studies seldom consider the spatial heterogeneity of largescale deformation data, which easily leads to obvious spatial aggregation of errors in the results of fusion. Here, we propose a novel multiresolution segmentation fusion (MRSF) method that uses a multiresolution segmentation algorithm to automatically classify the spatial heterogeneity of InSAR deformation data with similar deformation characteristics. We applied the MRSF method to the fusion of GNSS and InSAR deformation data covering the central valley aquifer system (CVAS) in southern California to verify its precision and robustness. Results show that the MRSF method can accurately reflect spatiotemporal evolution characteristics of displacement data and reliably estimate deformation for the times and locations of missing data. We then tested this method for geophysical parameter estimation by constructing three different sets of data, including dense GNSS sites, sparse GNSS sites, and sparse GNSS sites fused with InSAR data using MRSF, to invert the slip distribution of the Cascadia subduction zone. Results show that the inverted slip of the fused InSAR and GNSS data is comparable to that of the dense GNSS sites. Therefore, the MRSF method can obtain deformation results with high precision and high spatiotemporal resolution and effectively compensate for the lack of data caused by sparse GNSS sites during the geophysical inversion process.

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Section: Methodsmentioning

confidence: 99%

Section: Spatial Modelingmentioning

confidence: 99%

Fusion of Spatially Heterogeneous GNSS and InSAR Deformation Data Using a Multiresolution Segmentation Algorithm and Its Application in the Inversion of Slip Distribution

et al. 2022

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“…In this study, the unknown point is the centroid of the watershed where we assumed the average precipitation is located. The IDW method has been used in several studies to analyze the spatial rainfall distribution (Ruelland et al 2008, Wagner et al 2012, Li 2021.…”

Section: Model Inputs Preparationmentioning

confidence: 99%

Effectiveness of SAC-SMA model to simulate streamflow in the Colombian Andes: results for watersheds with different physical properties and high climate variability

Alvarez,

Barco

2023

Preprint

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The Andean region of Colombia, characterized by hydrometeorological extreme events and watershed complex topography, give rise to the main rivers of the Colombia fluvial network, essential for agriculture, ecosystems, consumption, and hydropower generation. Therefore, promote hydrology research and direct policy making towards sustainable and integrated water resources management for the Andean region is needed. This study evaluates the spatially lumped Sacramento Soil Moisture Accounting (SAC-SMA) model for 12 Colombian watersheds located in the Andean region with different climate regimes and geomorphological features. The model inputs include rainfall and streamflow data from gauges stations and potential evapotranspiration and land cover satellite-based data. The model calibration was performed using a Multistep Automatic Calibration Scheme coupled with the Shuffled Complex Evolution optimization algorithm. SAC-SMA Model performance was evaluated with Nash-Sutcliffe, Kling–Gupta efficiency, and Percent Bias. The model shows good performance, exhibiting NSE values > 0.5, KGE > 0.5 and Bias ±18% for the calibration period. Streamflow is underestimated for all watersheds in the calibration period and underestimated as well in the validation in 7/12 watersheds, however, the calibrated model was able to predict the observed outputs with reasonable accuracy. Correlation analysis show an impact of the watershed slope on the factors that control the different types of flows. The model results show the capabilities of SAC-SMA on representing the hydrograph recession and estimation of the baseflow in the study watersheds. This study provides information for land-use planning, agriculture, flood and drought risk management and water resources management in the Andean region.

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“…The spatial interpolation method used was inverse distance weighting (IDW). The IDW method is considered one of the standard methods for obtaining interpolated data [39]- [41]. This method assumes that the value of the point located closer to the measurement location will be affected more by the value of the measurement point.…”

Section: ) Rainfallmentioning

confidence: 99%

Flood Vulnerability Evaluation and Prediction Using Multi-temporal Data: A Case in Tangerang, Indonesia

Santosa¹,

Martono²,

Purwana³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Land-use change has an impact on growing physical flood vulnerability. Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) approaches are increasingly being used for flood vulnerability assessments. However, none has used timeseries land cover data for evaluation and rainfall over various return periods for prediction simultaneously, especially in Indonesia. Therefore, this study aims to evaluate and predict physical flood vulnerability using time-series land cover data and rainfall data over various return periods. Eight criteria were considered in the assessment: elevation, topographic wetness index, slope, distance to the river, distance downstream, soil type, rainfall, and land cover. The criteria weights were determined using the AHP method based on expert judgment. The multi-criteria model was built and validated using flood inundation data. Based on the validated model, the effect of land cover changes on flood vulnerability was evaluated. The flood vulnerability changes were also predicted based on rainfall over various return periods. The evaluation and prediction models have shown reliable findings. The criterion elevation and distance to the river significantly influenced the physical flood vulnerability by 41% and 20%. The evaluation model showed a strong correlation between the built-up area and the area with high flood vulnerability (r 2 = 0.96). Furthermore, the model predicted an inundation area expansion for rainfall over various return periods. Further research using spatial data with higher resolution and more advanced validation techniques is needed to improve the model accuracy.

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An enhanced dual IDW method for high-quality geospatial interpolation

Cited by 16 publications

References 36 publications

Fusion of Spatially Heterogeneous GNSS and InSAR Deformation Data Using a Multiresolution Segmentation Algorithm and Its Application in the Inversion of Slip Distribution

Fusion of Spatially Heterogeneous GNSS and InSAR Deformation Data Using a Multiresolution Segmentation Algorithm and Its Application in the Inversion of Slip Distribution

Effectiveness of SAC-SMA model to simulate streamflow in the Colombian Andes: results for watersheds with different physical properties and high climate variability

Flood Vulnerability Evaluation and Prediction Using Multi-temporal Data: A Case in Tangerang, Indonesia

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