Spatial interpolation of climatic variables using land surface temperature and modified inverse distance weighting

2015 ◽  
Vol 36 (4) ◽  
pp. 1000-1025 ◽  
Author(s):  
Emre Ozelkan ◽  
Serdar Bagis ◽  
Ertunga Cem Ozelkan ◽  
Burak Berk Ustundag ◽  
Meric Yucel ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Interpolation ◽  
Inverse Distance Weighting ◽  
Climatic Variables ◽  
Distance Weighting ◽  
Inverse Distance

scholarly journals Distance in spatial interpolation of daily rain gauge data

2006 ◽  
Vol 10 (2) ◽  
pp. 197-208 ◽  
Author(s):  
B. Ahrens
Keyword(s):  
Spatial Interpolation ◽  
Rain Gauge ◽  
Inverse Distance Weighting ◽  
Mountainous Terrain ◽  
Geographical Distance ◽  
Rain Gauge Data ◽  
Distance Weighting ◽  
Gauge Data ◽  
Statistical Distance ◽  
Inverse Distance

Abstract. Spatial interpolation of rain gauge data is important in forcing of hydrological simulations or evaluation of weather predictions, for example. This paper investigates the application of statistical distance, like one minus common variance of observation time series, between data sites instead of geographical distance in interpolation. Here, as a typical representative of interpolation methods the inverse distance weighting interpolation is applied and the test data is daily precipitation observed in Austria. Choosing statistical distance instead of geographical distance in interpolation of available coarse network observations to sites of a denser network, which is not reporting for the interpolation date, yields more robust interpolation results. The most distinct performance enhancement is in or close to mountainous terrain. Therefore, application of statistical distance in the inverse distance weighting interpolation or in similar methods can parsimoniously densify the currently available observation network. Additionally, the success further motivates search for conceptual rain-orography interaction models as components of spatial rain interpolation algorithms in mountainous terrain.

scholarly journals GIS based Inverse Distance Weighting Spatial Interpolation Technique for Fluoride Occurrence in Ground Water

OALib ◽  
2014 ◽  
Vol 01 (04) ◽  
pp. 1-6 ◽  
Author(s):  
M. Arif ◽  
J. Hussain ◽  
I. Hussain ◽  
S. Kumar ◽  
G. Bhati
Keyword(s):  
Ground Water ◽  
Spatial Interpolation ◽  
Inverse Distance Weighting ◽  
Distance Weighting ◽  
Interpolation Technique ◽  
Inverse Distance

scholarly journals Perbandingan Akurasi Metode Inverse Distance Weighting dan Kriging dalam Pemetaan Kedalaman Muka Airtanah

2020 ◽  
Vol 33 (2) ◽  
Author(s):  
Sadewa Purba Sejati
Keyword(s):  
Random Sampling ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Inverse Distance Weighting ◽  
Mean Square ◽  
Interpolation Model ◽  
Systematic Random Sampling ◽  
Depth To Water Table ◽  
Distance Weighting ◽  
Inverse Distance

Setiap metode interpolasi spasial yang disediakan oleh sitem informasi geografis (SIG) memiliki akurasi yang berbeda. Oleh karena itu pengetahuan terhadap akurasi metode tersebut sangat diperlukan oleh pengguna SIG. Penelitian ini dilakukan untuk mengetahui perbandingan akurasi metode interpolasi spasial inverse distance weighting (IDW) dan Kriging untuk menghasilkan informasi kedalaman muka airtanah. Penelitian ini menggunakan 65 data primer kedalaman muka airtanah yang diperoleh dengan metode systematic random sampling. Hasil interpolasi setiap metode kemudian dibandingkan tingkat akurasinya, yaitu dengan melihat nilai root mean square error (RMSE) dan persentase kesesuaian sampel validator terhadap model yang dihasilkan. Pengolahan data menunjukkan bahwa model interpolasi terbaik pada metode Kriging diperoleh melalui varian Ordinary Kriging. Metode tersebut menghasilkan model dengan nilai RMSE sebesar 2,98 dan kesesuaian sampel validator sebesar 50%. Sedangkan model interpolasi terbaik pada metode IDW diperoleh melalui parameter power (p) dengan nilai 3. Metode tersebut mengasilkan model interpolasi dengan nilai RMSE sebesar 3,233 dengan kesesuaian sampel validator sebesar 40%. Berdasarkan perbandingan diperoleh kesimpulan bahwa metode Kriging lebih akurat jika dibandingkan dengan metode IDW karena menghasilkan nilai RMSE yang lebih kecil dan persentase kesesuaian sampel validator terdahap model interpolasi lebih besar.  Every spatial interpolation method provided by geographic information system (GIS) has different accuracy. Therefore, it’s very necessary for GIS users to know the accuracy of every method. This study was performed to determine the comparison of accuracy of inverse distance weighting (IDW) and Kriging spatial interpolation methods to produce information on depth to water table. This study used 65 primary data of depth to water table obtained using systematic random sampling method. The interpolation result of the accuracy of every method was compared by assessing root mean square error (RMSE) and percentage of consistency of validator sample with the resulting model. Data processing showed that the best interpolation method of Kriging was Ordinary Kriging variance. The method produced a model with RMSE value of 2.98 and validator sample consistency of 50%. The best interpolation method of IDW method used power (p) parameter with a value of 3. The method produced an interpolation model with RMSE value of 3.233 and validator sample consistency of 40%. Based on the comparison, it was concluded that Kriging method was more accurate than IDW method because it had smaller RMSE value and bigger percentage of validator sample consistency to interpolation model.

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