scholarly journals Investigating consequences of choosing IDW interpolation parameters in East Java using raster analyses

2021 ◽  
Vol 893 (1) ◽  
pp. 012059
Author(s):  
A Kurniawan ◽  
M Ryan ◽  
A M Rafi ◽  
B E A Haq ◽  
Sudirman ◽  
...  
Keyword(s):  
Interpolation Method ◽  
Observation Point ◽  
Control Parameters ◽  
R Language ◽  
Observation Network ◽  
Language Analysis ◽  
Distance Weighting ◽  
Quality Control Parameters ◽  
Point Data ◽  
Inverse Distance

Abstract An observation network will never be enough for creating good information about monthly rainfall. An interpolation method is always needed. For operational purposes, inverse distance weighting (IDW) method is used. In East Java, 197 observation points are involved, then IDW's parameters used are neighbor=12 and power=2. The consequences of this framework are investigated in this study. By reversing IDW's formula, two kinds of raster analyses are developed, distance to neighbor used (DNU) and coefficient from point (CFP). DNU shows how far points are used for doing interpolation in some area by kilometer (km) meanwhile CFP describes an area impacted by a point and value sent to another cell from an observation point. Data used in this study are longitude and latitude of 197 observation points. The scripts are written in R Language. Analysis based on local governmental region shows that Sumenep has very far DNU. In average, the first point used located more than 25 km, and its twelfth is 112 km (average other regions DNU1=7 km and DNU12=35 km). It means there should be a unique interpolation mechanism for Sumenep. CFP confirms that some points give impact in unnatural ways (impacted area=5741 km2). We propose DNU and CFP as alternative quality control parameters for investigating consequences in interpolating rainfall.

scholarly journals A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 592
Author(s):  
Mehdi Aalijahan ◽  
Azra Khosravichenar
Keyword(s):  
Regional Planning ◽  
Interpolation Method ◽  
Absolute Error ◽  
Rain Gauge ◽  
Annual Precipitation ◽  
Coefficient Of Determination ◽  
Distance Weighting ◽  
Average Annual Precipitation ◽  
Inverse Distance

The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future.

scholarly journals Atmospheric Deposition Effects on Agricultural Soil Acidification State — Key Study: Krupanj Municipality

2014 ◽  
Vol 40 (2) ◽  
pp. 137-148 ◽  
Author(s):  
Dragan Čakmak ◽  
Jelena Beloica ◽  
Veljko Perović ◽  
Ratko Kadović ◽  
Vesna Mrvić ◽  
...  
Keyword(s):  
Soil Acidification ◽  
Interpolation Method ◽  
Natural Processes ◽  
Target Loading ◽  
Distance Weighting ◽  
The Status ◽  
Inverse Distance ◽  
Sustainable Productivity

Abstract Acidification, as a form of soil degradation is a process that leads to permanent reduction in the quality of soil as the most important natural resource. The process of soil acidification, which in the first place implies a reduction in soil pH, can be caused by natural processes, but also considerably accelerated by the anthropogenic influence of excessive S and N emissions, uncontrolled deforestation, and intensive agricultural processes. Critical loads, i.e. the upper limit of harmful depositions (primarily of S and N) which will not cause damages to the ecosystem, were determined in Europe under the auspices of the Executive Committee of the CLRTAP in 1980. These values represent the basic indicators of ecosystem stability to the process of acidification. This paper defines the status of acidification for the period up to 2100 in relation to the long term critical and target loading of soil with S and N on the territory of Krupanj municipality by applying the VSD model. The Inverse Distance Weighting (IDW) geostatistic module was used as the interpolation method. Land management, particularly in areas susceptible to acidification, needs to be focused on well-balanced agriculture and use of crops/seedlings to achieve the optimum land use and sustainable productivity for the projected 100-year period.

Spatial Interpolation

2010 ◽  
pp. 129-136 ◽  
Author(s):  
Xiaojun Yang
Keyword(s):  
Spatial Interpolation ◽  
Future Research ◽  
Research Needs ◽  
Local Accuracy ◽  
Distance Weighting ◽  
History Of ◽  
Thiessen Polygons ◽  
Point Data ◽  
Global Accuracy ◽  
Inverse Distance

Spatial interpolation is a core component of data processing and analysis in geoinformatics. The purpose of this chapter is to discuss the concept and techniques of spatial interpolation. It begins with an overview of the concept and brief history of spatial interpolation. Then, the chapter reviews some commonly used interpolations that are specifically designed for working with point data, including inverse distance weighting, kriging, triangulation, Thiessen polygons, radial basis functions, minimum curvature, and trend surface. This is followed by a discussion on some criteria that are proposed to help select an appropriate interpolator; these criteria include global accuracy, local accuracy, visual pleasantness and faithfulness, sensitivity, and computational intensity. Finally, future research needs and new, emerging applications are presented.

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 An enhanced dual IDW method for high-quality geospatial interpolation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhanglin Li
Keyword(s):  
Ordinary Kriging ◽  
Interpolation Method ◽  
The Public ◽  
Data Process ◽  
Distance Weighting ◽  
The Face ◽  
Spatial Interpolation Method ◽  
Stability And Accuracy ◽  
Inverse Distance

AbstractMany geoscience problems involve predicting attributes of interest at un-sampled locations. Inverse distance weighting (IDW) is a standard solution to such problems. However, IDW is generally not able to produce favorable results in the presence of clustered data, which is commonly used in the geospatial data process. To address this concern, this paper presents a novel interpolation approach (DIDW) that integrates data-to-data correlation with the conventional IDW and reformulates it within the geostatistical framework considering locally varying exponents. Traditional IDW, DIDW, and ordinary kriging are employed to evaluate the interpolation performance of the proposed method. This evaluation is based on a case study using the public Walker Lake dataset, and the associated interpolations are performed in various contexts, such as different sample data sizes and variogram parameters. The results demonstrate that DIDW with locally varying exponents stably produces more accurate and reliable estimates than the conventional IDW and DIDW. Besides, it yields more robust estimates than ordinary kriging in the face of varying variogram parameters. Thus, the proposed method can be applied as a preferred spatial interpolation method for most applications regarding its stability and accuracy.

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

scholarly journals Building Forecast Maps Of Water Quality For Main Rivers And Canals In Tien Giang Province, Vietnam

2015 ◽  
Vol 61 (3) ◽  
pp. 45-52
Author(s):  
Pham Anh Duc ◽  
Truong Thi Thuy Duong ◽  
Dang Quoc Dung
Keyword(s):  
Water Quality ◽  
Interpolation Method ◽  
Oxygen Demand ◽  
Mekong Delta ◽  
Total Suspended Solid ◽  
Water Quality Assessment ◽  
Suspended Solid ◽  
Quality Parameters ◽  
Distance Weighting ◽  
Inverse Distance

Abstract This study aims to enhance the mapping of forecast for water quality assessment in Mekong Delta provinces. The data from 32 sites from main rivers and canals in an area of around 2,482 km2 in Tien Giang Province, Vietnam, were used for calculation and mapping. The ArcGIS 9.3 software, Inverse Distance Weighting (IDW) interpolation method, hydrologic data, and water quality parameters in March (2010-2014) were applied to build the maps showing 2020 water quality predictions for main rivers and canals in Tien Giang Province. The estimation was based on the Water Quality Index (WQI) with 6 parameters such as pH, total suspended solid (TSS), dissolved oxygen (DO), biochemical oxygen demand (BOD), total nitrogen (T_N), and coliform. The results showed that water quality in the studied area in dry season will not be improved by the year 2020. The finding could be a scientific reference for the selection of effective approaches to improve water quality in main rivers and canals in Tien Giang Province.

scholarly journals Monitoring the Variations of Soil Salinity in a Palm Grove in Southern Algeria

2020 ◽  
Vol 12 (15) ◽  
pp. 6117 ◽  
Author(s):  
Abderraouf Benslama ◽  
Kamel Khanchoul ◽  
Fouzi Benbrahim ◽  
Sana Boubehziz ◽  
Faredj Chikhi ◽  
...  
Keyword(s):  
Soil Salinity ◽  
Arid Regions ◽  
Environmental Problem ◽  
Interpolation Method ◽  
Soil Analysis ◽  
Arid Environments ◽  
Geostatistical Methods ◽  
Distance Weighting ◽  
The Mean ◽  
Inverse Distance

Soil salinity is considered the most serious socio-economic and environmental problem in arid and semi-arid regions. This study was done to estimate the soil salinity and monitor the changes in an irrigated palm grove (42 ha) that produces dates of a high quality. Topsoil samples (45 points), were taken during two different periods (May and November), the electrical conductivity (EC) and Sodium Adsorption Ratio (SAR) were determined to assess the salinity of the soil. The results of the soil analysis were interpolated using two geostatistical methods: inverse distance weighting (IDW) and ordinary Kriging (OK). The efficiency and best model of these two methods was evaluated by calculating the mean error (ME) and root mean square error (RMSE), showing that the ME of both interpolation methods was satisfactory for EC (−0.003, 0.145) and for SAR (−0.03, −0.18), but the RMSE value was lower using the IDW with both data and periods. This can explain the accuracy of the IDW interpolation method. This model showed a dominance of soil salinity distribution in the South and South-East of the study area during the first season, and for the second season, the salts were concentrated in the middle of the area. Several factors could interact in this variation such as the topographic direction of the water flow and the aridity of the climate (evaporation). From this study emerges the need to maintain a better management of agricultural water and soils, avoiding salt accumulation, to ensure a good yield and the sustainability of agriculture in arid environments.

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