COMPARISONS BETWEEN DIFFERENT INTERPOLATION TECHNIQUES

42 citations as recorded by crossref.

1. A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation S. Salekin et al. https://doi.org/10.3390/ijgi7080300
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4. Using High-Density LiDAR Data and 2D Streamflow Hydraulic Modeling to Improve Urban Flood Hazard Maps: A HEC-RAS Multi-Scenario Approach A. Mihu-Pintilie et al. https://doi.org/10.3390/w11091832
5. Geospatial assessment of habitat degradation and climate impacts on migratory crane habitat in Pakistan’s Wetland ecosystems M. Suliman et al. https://doi.org/10.1038/s41598-026-41758-y
6. Evaluation of interpolation methods for the prediction of urban methane concentrations C. Stadler et al. https://doi.org/10.1016/j.uclim.2023.101556
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8. Developing Gridded Climate Data Sets of Precipitation for Greece Based on Homogenized Time Series F. Gofa et al. https://doi.org/10.3390/cli7050068
9. Determination of the groundwater recharge in the Blagoevgrad valley Neogene sediments M. Ivanov et al. https://doi.org/10.52321/igh.34.1.19
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11. The effect of different sampling schemes on estimation precision of snow water equivalent (SWE) using geostatistics techniques in a semi-arid region of Iran H. Ganjkhanlo et al. https://doi.org/10.1080/10106049.2019.1581267
12. Statistical interpolation and spatial mapping of geotechnical soil parameters of District Sargodha, Pakistan W. Hassan et al. https://doi.org/10.1007/s10064-022-03059-2
13. Long-Term Volumetric Change Estimation of Red Ash Quarry Sites in the Afro-Alpine Ecosystem of Bale Mountains National Park in Ethiopia M. Muhammed et al. https://doi.org/10.3390/rs16071226
14. Indicators of wind-driven forest disturbances – pit–mound topography, its automatic detection and significance J. Godziek & Ł. Pawlik https://doi.org/10.1016/j.catena.2022.106757
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17. The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS J. Barman et al. https://doi.org/10.1016/j.mex.2024.103005
18. Investigating the association between groundwater contaminants and hypertension risk in India: a machine learning-based analysis S. Biswas et al. https://doi.org/10.1038/s41370-025-00776-0
19. Integrating vegetation indices and geo-environmental factors in GIS-based landslide-susceptibility mapping: using logistic regression H. Abeysiriwardana & P. Gomes https://doi.org/10.1007/s11629-021-6988-8
20. Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia S. Prasetyo et al. https://doi.org/10.7717/peerj-cs.415
21. Geospatial-based approach for susceptibility assessment of expansive soils using a new multicriteria classification model A. Djellali et al. https://doi.org/10.1007/s12517-022-11024-2
22. Spatial Interpolation to Predict Mangrove above-Ground Carbon in Loh Buaya, Komodo National Park, Indonesia S. Rijal et al. https://doi.org/10.4028/p-p1w5RV
23. Impact of landscape patterns on spatial and seasonal water quality: an insight from the Sub-Himalayan rivers in Siliguri Planning Area, India A. Saha et al. https://doi.org/10.1080/1573062X.2026.2648225
24. Assessment of nitrogen dioxide concentrations and main sources in a medium-sized city in Latin America using geographic information systems A. Berkovic et al. https://doi.org/10.1007/s11869-024-01621-3
25. Interpolation Theory and Artificial Intelligence: A Roadmap for Satellite Data Augmentation I. Mereu et al. https://doi.org/10.1109/JSTARS.2025.3577534
26. Impact of interpolation techniques on the accuracy of large-scale digital elevation model M. Habib et al. https://doi.org/10.1515/geo-2020-0012
27. Mining Ground Deformation Estimation Based on Pre-Processed InSAR Open Data—A Norwegian Case Study J. Blachowski & S. Ellefmo https://doi.org/10.3390/min13030328
28. Assessment of soil erosion and sediment yield in response to land use and land cover changes using geospatial techniques in Dumuga Watershed, Ethiopia Z. Reta Roba et al. https://doi.org/10.1080/27669645.2025.2460917
29. CPC-GAIN:a data imputation method via clustering, probabilistic modeling and classification with enhanced GANs for time series data Q. Yang & Y. Tong https://doi.org/10.1007/s13042-026-03174-6
30. A data processing and governance framework implemented as software to enable utility customer data de-identification and sharing E. Sjöstedt et al. https://doi.org/10.2166/hydro.2026.084
31. Do relationships between ambient temperature and serious adverse health outcomes vary among users of different antidiabetes drugs? A retrospective cohort study of US Medicaid beneficiaries with type 2 diabetes C. Leonard et al. https://doi.org/10.1136/bmjopen-2024-085139
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34. Generation and evaluation of synthetic cone penetration test (CPT) data using various spatial interpolation techniques M. Rahman et al. https://doi.org/10.1139/cgj-2019-0745
35. Spatiotemporal Variability of Rainfall‐Streamflow Dynamics in Mountainous Tropical Environments: Insights From Agricultural Catchments of Northwest Rwanda D. Nahayo et al. https://doi.org/10.1002/hyp.70360
36. SPATIAL REPRESENTATION OF SURFACE WATER MONITORING AND ITS ASSESSMENT USING GEOSTATISTICAL AND NON-GEOSTATISTICAL TECHNIQUES IN GIS E. Maliqi & P. Penev https://doi.org/10.3846/gac.2019.8590
37. GIS-Based Approach to Spatio-Temporal Interpolation of Atmospheric CO2 Concentrations in Limited Monitoring Dataset Y. Bezyk et al. https://doi.org/10.3390/atmos12030384
38. Cumulative Ordinary Kriging interpolation model to forecast radioactive fallout, and its application to Chernobyl and Fukushima assessment: a new method and mini review F. Külahcı & Z. Şen https://doi.org/10.1007/s11356-022-21921-4
39. Effect of statins on the association between high temperature and all-cause mortality in a socioeconomically disadvantaged population: a cohort study Y. Nam et al. https://doi.org/10.1038/s41598-019-41109-0
40. Estimation of methane emission from an urban wastewater treatment plant applying inverse Gaussian model C. Stadler et al. https://doi.org/10.1007/s10661-021-09660-4
41. Visualising post-disaster damage on maps: a user study T. Candela et al. https://doi.org/10.1080/13658816.2022.2063872
42. Attention Based Energy Demand Forecasting in Smart Grid Environments Y. Işıkdemir & F. Akal https://doi.org/10.62520/fujece.1423120