scholarly journals Ordinary kriging and genetic programming for spatial estimation of rainfall in the Middle Yarra River catchment, Australia

2016 ◽  
Vol 47 (6) ◽  
pp. 1182-1197 ◽  
Author(s):  
Sajal Kumar Adhikary ◽  
Nitin Muttil ◽  
Abdullah Gokhan Yilmaz
Keyword(s):  
Genetic Programming ◽  
Ordinary Kriging ◽  
Prediction Error ◽  
Interpolation Method ◽  
River Catchment ◽  
Spatial Estimation ◽  
Variogram Model ◽  
Distance Weighting ◽  
Important Input ◽  
Yarra River

Rainfall maps with gridded data are frequently used as an important input for many hydrological models. In this study, two kriging-based interpolation methods (i.e., ordinary kriging (OK) and kriging with genetic programming (KGP)) and a deterministic interpolation method (inverse distance weighting (IDW)) are implemented to generate gridded rainfall maps from point rainfalls. The KGP is implemented as a new kriging method in which the genetic programming-based non-parametric variogram model is used with kriging. Rainfall records from existing 19 raingauges in the Middle Yarra River catchment, Australia are used for the analysis. The performance of each method is assessed through the cross-validation test. Results indicate that the kriging-based methods clearly outperform the IDW method. Among all the kriging-based methods, OK with the spherical variogram model yields the lowest prediction error and best estimates for all months. The KGP method gives an almost identical error to that given by the OK with the spherical variogram model for most of the months and a lower prediction error than that given by OK with the exponential or Gaussian variogram model. Thus, the KGP can be used in line with traditional kriging as a viable alternative technique for spatial estimation and mapping of rainfall.

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 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.

scholarly journals Parallel Algorithms for Spatial Rainfall Distribution

Jurnal INKOM ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Arnida Lailatul Latifah ◽  
Adi Nurhadiyatna
Keyword(s):  
Parallel Algorithms ◽  
Computation Time ◽  
Computational Time ◽  
Rainfall Distribution ◽  
Flood Modelling ◽  
Computation Efficiency ◽  
Distance Weighting ◽  
Speed Up ◽  
Important Input ◽  
Serial Algorithms

This paper proposes parallel algorithms for precipitation of flood modelling, especially applied in spatial rainfall distribution. As an important input in flood modelling, spatial distribution of rainfall is always needed as a pre-conditioned model. In this paper two interpolation methods, Inverse distance weighting (IDW) and Ordinary kriging (OK) are discussed. Both are developed in parallel algorithms in order to reduce the computational time. To measure the computation efficiency, the performance of the parallel algorithms are compared to the serial algorithms for both methods. Findings indicate that: (1) the computation time of OK algorithm is up to 23% longer than IDW; (2) the computation time of OK and IDW algorithms is linearly increasing with the number of cells/ points; (3) the computation time of the parallel algorithms for both methods is exponentially decaying with the number of processors. The parallel algorithm of IDW gives a decay factor of 0.52, while OK gives 0.53; (4) The parallel algorithms perform near ideal speed-up.

scholarly journals Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction

2016 ◽  
Author(s):  
Ling Huang ◽  
Hongping Zhang ◽  
Peiliang Xu ◽  
Jianghui Geng ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Ordinary Kriging ◽  
Variance Components ◽  
Harmonic Functions ◽  
Measurement Errors ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Single Frequency ◽  
Estimation Accuracy ◽  
Spherical Cap ◽  
Unknown Variance

Ionospheric delay has been a critical issue that limits the accuracy of GNSS precise positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where irregularity of ionosphere is often significant. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on TEC semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospherical delay. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The results have shown that the interpolation accuracy of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy.

scholarly journals Relationship between Spatial Variability Pattern of Wheat Yield and Soil Properties

2018 ◽  
pp. 1-14
Author(s):  
Alidad Karami ◽  
Sadegh Afzalinia
Keyword(s):  
Spatial Structure ◽  
Soil Properties ◽  
Soil Ph ◽  
Precision Agriculture ◽  
Interpolation Method ◽  
Irrigation System ◽  
Wheat Yield ◽  
Gaussian Model ◽  
Variogram Model ◽  
Clay Percentage

Aims: Determining effects of spatial variation of some soil properties on wheat quantity and quality variation in order that proper soil and inputs management can be applied for sustainable wheat production. Study Design: Analyzing data of a field with center pivot irrigation system and uniform management using the geostatistical method. Place and Duration of Study: Soil and Water Research Department, Fars Agricultural and Natural Resources Research and Education Center, Darab, Iran, from September 2013 to February 2014. Methodology: Wheat yield data harvested by class lexion 510 combine from 25 m2 plots (11340 locations) with the corresponding geographical location were used. Besides, soil properties and wheat yield were measured at 36 randomly selected points on the field. Interpolation of parameters was predicted with the best semi-variogram model using kriging, inverse distance weighted (IDW), and cokriging methods. Results: Results showed that wheat yield varied from 2 to 10.08 tons per hectare. Cokriging with cofactor of kernel weight interpolator had more accuracy compared to the combine default interpolator (kriging). A logical, linear correlation was found between different parameters. The best variogram model for pH, OC, and ρb was exponential, for EC, TNV, SP, soil silt and clay percentage was spherical, and for soil, percentage sand was Gaussian model. Data of soil sand, silt, and clay percentage, EC, TNV, and SP had strong spatial structure, and soil pH, OC, and ρb had moderate spatial structure. The best interpolation method for soil pH, EC, sand and silt percentage was kriging method; while, for TNV, SP, OC, ρb, and clay percentage was IDW. Conclusion: There was a close relationship between wheat yield variation and changes in the soil properties. Soil properties and wheat yield distribution maps provided valuable information which could be used for wheat yield improvement in precision agriculture.

scholarly journals Optimal design of rain gauge network in the Middle Yarra River catchment, Australia

2014 ◽  
Vol 29 (11) ◽  
pp. 2582-2599 ◽  
Author(s):  
Sajal Kumar Adhikary ◽  
Abdullah Gokhan Yilmaz ◽  
Nitin Muttil
Keyword(s):  
Optimal Design ◽  
Rain Gauge ◽  
River Catchment ◽  
Rain Gauge Network ◽  
Yarra River

scholarly journals Bathymetry and geographical regionalization of Brepollen (Hornsund, Spitsbergen) based on bathymetric profiles interpolations

2013 ◽  
Vol 34 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Mateusz Moskalik ◽  
Piotr Grabowiecki ◽  
Jarosław Tęgowski ◽  
Monika Żulichowska
Keyword(s):  
Ordinary Kriging ◽  
Interpolation Method ◽  
High Arctic ◽  
Kriging Method ◽  
Small Boat ◽  
Single Beam ◽  
Arctic Regions ◽  
Interpolation Techniques ◽  
Geographical Regionalization

Abstract Determination of High Arctic regions bathymetry is strictly dependent from weather and ice mass quantity. Due to safety, it is often necessary to use a small boat to study fjords area, especially close to glaciers with unknown bathymetry. This precludes the use of modern multi−beam echosounders, and so traditional single−beam echosounders have been used for bathymetry profiling. Adequate interpolation techniques were determined for the most probable morphological formations in−between bathymetric profiles. Choosing the most accurate interpolation method allows for the determination of geographical regionalisation of submarine elevations of the Brepollen area (inner part of Hornsund, Spitsbergen). It has also been found that bathymetric interpolations should be performed on averaged grid values, rather than individual records. The Ordinary Kriging Method was identified as the most adequate for interpolations and was compared with multi beam scan− ning, which was possible to make due to a previously modelled single beam interpolation map. In total, eight geographical units were separated in Brepollen, based on the bathy− metry, slope and aspect maps. Presented results provide a truly new image of the area, which allow for further understanding of past and present processes in the High Arctic.

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