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.

scholarly journals An Adaptive Inverse-Distance Weighting Interpolation Method Considering Spatial Differentiation in 3D Geological Modeling

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 51
Author(s):  
Zhen Liu ◽  
Zhilong Zhang ◽  
Cuiying Zhou ◽  
Weihua Ming ◽  
Zichun Du
Keyword(s):  
Large Scale ◽  
Interpolation Method ◽  
Eastern China ◽  
Spatial Differentiation ◽  
Inverse Distance Weighting ◽  
Selection Strategy ◽  
Geological Modeling ◽  
3D Geological Modeling ◽  
Distance Weighting ◽  
Inverse Distance

The inverse-distance weighting interpolation is widely used in 3D geological modeling and directly affects the accuracy of models. With the development of “smart” or “intelligent” geology, classical inverse-distance weighting interpolation cannot meet the accuracy, reliability, and efficiency requirements of large-scale 3D geological models in these fields. Although the improved inverse-distance weighting interpolation can basically meet the requirements of accuracy and reliability, it cannot meet the requirements of efficiency at the same time. In response to these limitations, the adaptive inverse-distance weighting interpolation method based on geological attribute spatial differentiation and geological attribute feature adaptation was proposed. This method takes into account the spatial differentiation of geological attributes to improve the accuracy and considers the first-order neighborhood selection strategy to adaptively improve efficiency to meet above requirements of large-scale geological modeling. The proposed method was applied to an area in eastern China, and the results of the proposed method, compared to the results of classical inverse-distance weighting interpolation and improved inverse-distance weighting interpolation, suggest that the problems encountered above in large-scale geological modeling can be solved with the proposed method. The method can provide effective support for large-scale 3D geological modeling in smart geology.

scholarly journals Prediction of electricity sales using neural based inverse distance weighting method

2018 ◽  
Vol 7 (2.2) ◽  
pp. 65 ◽  
Author(s):  
Bustani . ◽  
Sunu Pradana ◽  
Mulyanto . ◽  
Nurjanana .
Keyword(s):  
Interpolation Method ◽  
Back Propagation ◽  
Inverse Distance Weighting ◽  
Data Availability ◽  
Sampled Data ◽  
Weighting Method ◽  
Distance Weighting ◽  
Som Algorithm ◽  
Z Value ◽  
Inverse Distance

Prediction of electricity sales becomes important for State Electricity Company of Indonesia (PLN) in order to estimate the Statement of Profit and Loss in next year. To obtain good predictive results may require many variables and data availability. There are many available methods that do not require so many variables to get predicted results with a good approximation. The aim of this study was to predict electricity sales by using an interpolation method called IDW (Inverse Distance Weighting). Several data samples are mapped into Cartesian coordinates. The data samples used are power connected to the household (X-axis), to industry (Y-axis), and electricity sales (Z value). Firstly, the sampled data clustered by using SOM algorithm. The Z value in each cluster is predicted by using the IDW method. The prediction results of IDW method are then optimized using ANN-BP (Artificial Neural Network Back Propagation). The trained net structure is then used to predict the electricity sale in next year.  

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 Assessment of Ordinary Kriging and Inverse Distance Weighting Methods for Modeling Chromium and Cadmium Soil Pollution in E-Waste Sites in Douala, Cameroon

2020 ◽  
Vol 10 (26) ◽  
pp. 200605
Author(s):  
Romaric Emmanuel Ouabo ◽  
Abimbola Y. Sangodoyin ◽  
Mary B. Ogundiran
Keyword(s):  
Urban Areas ◽  
Ordinary Kriging ◽  
Cross Validation ◽  
Interpolation Method ◽  
Waste Recycling ◽  
Inverse Distance Weighting ◽  
P Value ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Inverse Distance

Background. Several studies have demonstrated that chromium (Cr) and cadmium (Cd) have adverse impacts on the environment and human health. These elements are present in electronic waste (e-waste) recycling sites. Several interpolation methods have been used to evaluate geographical impacts on humans and the environment. Objectives. The aim of the present paper is to compare the accuracy of inverse distance weighting (IDW) and ordinary kriging (OK) in topsoil analysis of e-waste recycling sites in Douala, Cameroon. Methods. Selecting the proper spatial interpolation method is crucial for carrying out surface analysis. Ordinary kriging and IDW are interpolation methods used for spatial analysis and surface mapping. Two sets of samples were used and compared. The performances of interpolation methods were evaluated and compared using cross-validation. Results. The results showed that the OK method performed better than IDW prediction for the spatial distribution of Cr, but the two interpolation methods had the same result for Cd (in the first set of samples). Results from Kolmogorov-Smirnov and Shapiro-Wilk tests showed that the data were normally distributed in the study area. The p value (0.302 and 0.773) was greater than 0.05 for Cr and for Cd (0.267 and 0.712). In the second set of samples, the OK method results (for Cd and Cr) were greatly diminished and the concentrations dropped, looking more like an average on the maps. However, the IDW interpolation gave a better representation of the concentration of Cd and Cr on the maps of the study area. For the second set of samples, OK and IDW for Cd and Cr had more similar results, especially in terms of root mean square error (RMSE). Conclusions. Many parameters were better identified from the RMSE statistic obtained from cross-validation after exhaustive testing. Inverse distance weighting appeared more adequate in limited urban areas. Competing Interests. The authors declare no competing financial interests

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