Experiment, evaluate methods of interpolation of terain surface for different types of terrain

2020 ◽  
Vol 61 (2) ◽  
pp. 116-125
Author(s):  
Yen Quoc Phan ◽  
Nga Thu Thi Nguyen ◽  
Keyword(s):  
Nearest Neighbor ◽  
Polynomial Interpolation ◽  
Spline Functions ◽  
Mountainous Terrain ◽  
Mean Square ◽  
Data Set ◽  
Natural Neighbor ◽  
Distance Weighting ◽  
Different Types ◽  
Inverse Distance

Surface modeling is done by many classic and modern algorithms such as Polynomial Interpolation, Delaunay Triangulation, Nearest Neighbor, Natural Neighbor, Kriging, Inverse Distance Weighting (IDW), Spline Functions, etc. The important issue is to experiment, evaluate and select algorithms suitable to the reality of the data and the study area. The paper used three algorithms IDW, Kriging and Natural Neighbor to model the terrain on two map sheets representing different types of terrain. From there, compare the results and evaluate the accuracy of the methods based on random test data from the data set which is extracted from the original map. In addition, checking the contour determined from the algorithm compared to the original contour were also carried out on the entire map sheet. Results show that: Natural Neighbor algorithm gives better results on both experimental areas, then IDW and Kriging algorithms, the root mean Square Error of 15.2922, 16.4754 and 17.9949 m respectively for average high terrain and 13.9728, 15.2466, 15.7613 meters with high mountainous terrain

Adaptive K-Means Algorithm with Dynamically Changing Cluster Centers and K-Value

2012 ◽  
Vol 532-533 ◽  
pp. 1373-1377 ◽  
Author(s):  
Ai Ping Deng ◽  
Ben Xiao ◽  
Hui Yong Yuan
Keyword(s):  
Nearest Neighbor ◽  
Experimental Results ◽  
Data Set ◽  
Number Of Clusters ◽  
K Value ◽  
Testing Data ◽  
Different Types ◽  
Data Points ◽  
Shared Nearest Neighbor

In allusion to the disadvantage of having to obtain the number of clusters in advance and the sensitivity to selecting initial clustering centers in the K-means algorithm, an improved K-means algorithm is proposed, that the cluster centers and the number of clusters are dynamically changing. The new algorithm determines the cluster centers by calculating the density of data points and shared nearest neighbor similarity, and controls the clustering categories by using the average shared nearest neighbor self-similarity.The experimental results of IRIS testing data set show that the algorithm can select the cluster cennters and can distinguish between different types of cluster efficiently.

scholarly journals Evaluation of Satellite and Reanalysis Precipitation Products Using GIS for All Basins in Turkey

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ahmet Irvem ◽  
Mustafa Ozbuldu
Keyword(s):  
Rain Gauge ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
The West ◽  
Data Set ◽  
Precipitation Product ◽  
Distance Weighting ◽  
Areal Precipitation ◽  
Average Annual Precipitation ◽  
Inverse Distance

Use of the satellite and reanalysis precipitation products, as supplementary data sources, are steadily rising for hydrometeorological applications, especially in data-sparse areas. However, the accuracy of these data sets is often lacking, especially in Turkey. This study evaluates the accuracy of satellite precipitation product (TRMM 3B42V7) and reanalysis precipitation product (NCEP-CFSR) against rain gauge observations for the 1998–2010 periods. Average annual precipitation for the 25 basins in Turkey was calculated using rain gauge precipitation data from 225 stations. The inverse distance weighting (IDW) method was used to calculate areal precipitation for each basin using GIS. According to the results of statistical analysis, the coefficient of determination for the TRMM product gave satisfactory results (R2 > 0.88). However, R2 for the CFSR data set ranges from 0.35 for the Eastern Black Sea basin to 0.93 for the West Mediterranean basin. RMSE was calculated to be 95.679 mm and 128.097 mm for the TRMM and CFSR data, respectively. The NSE results of TRMM data showed very good performance for 6 basins, while the PBias value showed very good performance for 7 basins. The NSE results of CFSR data showed very good performance for 3 basins, while the PBias value showed very good performance for 6 basins.

scholarly journals Investigation of relationship between particulate matter (PM2.5) and meteorological parameters in Isfahan, Iran

2020 ◽  
Author(s):  
Majid Kermani ◽  
Ahmad Jafari ◽  
Mitra Gholami ◽  
Hossein Arfaeinia ◽  
Abbas Shahsavani ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Meteorological Parameters ◽  
Mean Square ◽  
Kriging Method ◽  
Pollution Levels ◽  
Distance Weighting ◽  
Air Quality Control ◽  
Health Related ◽  
And Control ◽  
Inverse Distance

Introduction: Estimating air pollution levels in areas with no measurements is a major concern in health-related studies. Therefore, the aim of this study was to investigate the amount of exposure to particulate matter below 2.5 µ (PM2.5) in the metropolis of Tehran. Materials and methods: The hourly concentrations of PM2.5 during 2017- 2018 period were acquired from the Department of Environment (DOE) and Air Quality Control Company of Tehran (AQCC). The hourly concentrations were validated and 24-h concentrations were calculated. Inverse distance weighting (IDW), Universal Kriging, and Ordinary Kriging were used to spatially model the PM2.5 over Tehran metropolis area. Root Mean Square Error (RMSE) and Mean Error (ME) were used to measure and control for the accuracy of the methods. Results: The results of this study showed that RMSE and MENA values in Kriging method was less than the IDW, which indicates that the Kriging was the best method to estimate PM2.5 concentrations. According to the final map, the highest annual concentrations of PM2.5 were observed in the southern and southwestern areas of Tehran (districts 10, 15, 16, 17, and 18). The lowest exposure to PM2.5 was found to be in districts 1, 2, 3, 6, and 8. Conclusion: It can be concluded that Kriging method can predict spatial variations of PM2.5 more accurately than IDW method.

scholarly journals Pengukuran Dasar Telaga menggunakan Alat Perum Gema Untuk Menghasilkan Peta Batimetri di Telaga Winong Yogyakarta

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Erik Febriarta ◽  
Septian Vienastra ◽  
Agus Suyanto ◽  
Ajeng Larasati
Keyword(s):  
Bottom Topography ◽  
Kriging Interpolation ◽  
Time Interval ◽  
Mean Squared Errors ◽  
Depth Data ◽  
Natural Neighbor ◽  
Distance Weighting ◽  
The Village ◽  
Inverse Distance ◽  
Special Region

Lake Winong is one lake that is not dried on dry season. This lake is located in the Village Kepek, District Saptosari, Gunung Kidul Regency, Special Region Province Yogyakarta. This study was designed to determine the depth or bottom topography of Lake Winong to produce bathymetry maps. Firstly, a survey was conducted to measure the depth with an echosounder instrument. Principally, an echosounder records the time interval required by the emitted sound wave to propagate to the bottom and return, from which the distance or depth (m) can be computed. Secondly, the depth data were interpolated by initially cross-validating the smallest Root Mean Squared Errors (RMSE) of the Inverse Distance Weighting (IDW), kriging, and natural neighbor methods. Each was run with the power values (weight factors) of 0.5, 1, 2, and 3. The results showed that kriging interpolation with a power value of 3 yielded the smallest RMSE, namely 0.005, and the lake observed was -0.2 to -1.8 m deep, with the deepest location found in the middle of the lake.

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 Spatial Prediction Techniques Accuracy for Elevation Determination in Akure South Local Government, Ondo State

2020 ◽  
Vol 5 (5) ◽  
pp. 550-553
Author(s):  
Victor Ayodele Ijaware ◽  
Adebayo T. Adeboye
Keyword(s):  
Polynomial Interpolation ◽  
Inverse Distance Weighting ◽  
Universal Kriging ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Local Polynomial ◽  
Distance Weighting ◽  
Elevation Data ◽  
Inverse Distance

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI), with the collaboration of scientific and industry organizations in both countries. The ASTER instrument provides a more robust remote sensing imaging capability when compared to the older Landsat Thematic Mapper. This paper deals with the accuracy assessment of elevation data obtained using ASTER from each of the eleven (11) selected extrapolation/interpolation algorithms: Inverse Distance Weighting, Natural Neighbor, Spline Regular, Spline Tension, Universal Kriging, Empirical Bayesian Kriging, Topo to Raster, global (trend surface), local polynomial, kernel interpolation with barriers and radial basis functions in Digital Elevation Model (DEM) surface creation. The data were compared with reference to ground control points of differential GPS measurements in the study area. The error statistics were generated between DGPS measurements and Extracted elevation data from each selected interpolation method. It was observed that Spline Regular Interpolation shown the best overall accuracy of ±11.520m when elevation data extracted from Inverse distance weighting, Natural Neighbour, Spline T, Topo to Raster, Universal Kriging, Empirical Bayesian kriging, Global polynomial interpolation (GPI), local polynomial interpolation (LPI), Radial basis function and Kernel interpolation of ±15.170, ±14.340, ±12.336, ±13.551, ±14.707, ±13.711, ±15.363, ±13.964, ±13.590 and ±15.376 respectively when compared with elevation values from GPS method. The study recommends capacity building in the form of workshop, training, and flexible integration of point elevation data to DEM.

scholarly journals Improving GNSS Zenith Wet Delay Interpolation by Utilizing Tropospheric Gradients: Experiments with a Dense Station Network in Central Europe in the Warm Season

2019 ◽  
Vol 11 (6) ◽  
pp. 674 ◽  
Author(s):  
Florian Zus ◽  
Jan Douša ◽  
Michal Kačmařík ◽  
Pavel Václavovic ◽  
Kyriakos Balidakis ◽  
...  
Keyword(s):  
Warm Season ◽  
Satellite System ◽  
Data Set ◽  
Target Station ◽  
Benchmark Data ◽  
Weather Model ◽  
Distance Weighting ◽  
Global Navigation Satellite ◽  
Development And Validation ◽  
Inverse Distance

The Benchmark data set collected within the European COST Action ES1206 has aimed to support the development and validation of advanced Global Navigation Satellite System (GNSS) tropospheric products, in particular high-resolution zenith delays and tropospheric gradients. In this work we utilize this unique data set to show that the interpolation of GNSS Zenith Wet Delays (ZWDs) can be improved by utilizing tropospheric gradients. To do this we first prove the concept with simulated observations, that is, zenith delays and tropospheric gradients derived from a Numerical Weather Model. We show how tropospheric gradients can be converted to ZWD gradients. Then the ZWD gradients together with the ZWDs at selected reference stations are used in an inverse distance weighting interpolation scheme to estimate the ZWD at some target station. For a station configuration with an average station distance of 50 km in Germany and a period of two months (May and June 2013), we find an improvement of 20% in interpolated ZWDs when tropospheric gradients are taken into account. Next, we replace the simulated by real observations, that is, zenith delays and tropospheric gradients from a Precise Point Positioning (PPP) solution provided with the G-Nut/Tefnut analysis software. Here we find an improvement of 10% in interpolated ZWDs when tropospheric gradients are taken into account.

scholarly journals TOPOLOGICAL 3D ELEVATION DATA INTERPOLATION OF ASTER GDEM BASED ON CONTINUOUS DEFORMATION

2018 ◽  
Vol XLII-4/W10 ◽  
pp. 71-77
Author(s):  
A. Jamali ◽  
F. A. Castro
Keyword(s):  
Polynomial Interpolation ◽  
Inverse Distance Weighting ◽  
Homotopy Continuation ◽  
Data Interpolation ◽  
3D Data ◽  
Distance Weighting ◽  
Non Linear ◽  
Elevation Data ◽  
Inverse Distance ◽  
Polynomial Methods

<p><strong>Abstract.</strong> In Geographic Information Science, polynomial methods such as linear estimation and non-polynomial methods including Inverse Distance Weighting and Kriging have been used for elevation data interpolation. In this paper, 3D data interpolation using linear and non-linear homotopy continuation as well as advanced polynomial interpolation methods are researched. Continuous deformations that reconstruct straight lines or algebraic curves between any pair of 3D data are presented. The implemented topological mathematical algorithm for 3D elevation data interpolation is compared to Inverse Distance Weighting and Triangulated Irregular Network (TIN) methods. The presented linear and non-linear mathematical algorithms show better results compared to Inverse Distance Weighting and TIN in terms of Root Mean Square Error and L-infinity.</p>

scholarly journals استخدام طريقة مقلوب المسافة الموزونة IDW لإنتاج خرائط لبعض خواص التربة في محطة الابحاث الزراعية جامعة طرابلس

2019 ◽  
Vol 34 (4) ◽  
pp. 315-327
Author(s):  
ماجدة بشير البشتي ◽  
أبوعبدالله سعد الشريف ◽  
إيهاب محمد الصقر
Keyword(s):  
Root Mean Square Error ◽  
Bulk Density ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Inverse Distance Weighting ◽  
Mean Square ◽  
Distance Weighting ◽  
Inverse Distance

هدفت الدراسة لتحديد الاختلافات المكانية لبعض الخواص الفيزيائية والكيميائية لعينات تربة محطات أبحاث كلية الزراعة/ جامعة طرابلس في شهر أبريل 2013، واستخدمت النتائج لإنتاج خرائط مكانية لمعرفة التوزيع المكاني لكل من الخواص الآتية: الكثافة الظاهرية Bulk density (BD)، محتوى الرطوبة الوزني (GWC)، ملوحة التربة (EC)، ودرجة حموضة التربة (pH) باستخدام طريقة مقلوب المسافة الموزونة Inverse Distance Weighting IDW)). أجريت هذه الدراسة على أرض مساحتها 01320 م2 تقريبا قُسمت إلى شبكة بأبعاد   12م X 11م لتنتج 100 وحدة مساحية وأختيرت 36 وحدة حددت إحداثياتها بواسطة جهاز GPS المحمول لتجميع العينات منها. أجريت تحاليل لكل من BD وGWC من الأعماق 0-10 سم، 10-20 سم و20-30 سم، بينما تحاليل EC وpHأجريت للعينات بعمق 30 سم. أنتجت خرائط مكانية ذات أسطح مستمرة وذات جودة اختلفت من خاصية لأخرى حسب قيم الجذر التربيعي لمتوسط الخطأ Root Mean Square Error (RMSE). من النتائج تراوحت قيم (RMSE) (0.71، 0.82، 0.86) لخاصية GWC و (0.06، 0.13، 0.08) لخاصية BD للأعماق الثلاثة على التوالي. بينما كانت قيم RMSE (0.15، 0.84) لخاصيتي pH وEC على التوالي. أظهرت قيم RMSE المنخفضة لخرائط BD عند العمق الأول والثالث وكذلك خريطة pH مؤشر جودة أعلى للخرائط. بينما بينت قيم (RMSE) المرتفعة نوعا ما بأن الخرائط المنتجة لكل من خاصيتي EC وGWC كانت أقل جودة. خلصت هذه الدراسة إلى إمكانية إنتاج خرائط مكانية متفاوتة الجودة لبعض خواص التربة داخل الحقل باستخدام IDW. وبالتالي يمكن استخدام هذه الخرائط للتنبؤ بخاصيتي BD وpH في الحقل، بينما يصعب استخدامها للتنبؤ بخاصيتي EC وGWC، ولهذا ينصح باستمرار البحث في إمكانية إنتاج خرائط ذات جودة عالية بطرق أخرى لهاتين الخاصيتين مع مراعاة زيادة عدد العينات.

scholarly journals COMPARISON THE PERFORMANCE OF ORDINARY KRIGING AND INVERSE DISTANCE WEIGHTING METHODS FOR MAPPING NICKEL LATERITE PROPERTIES

KURVATEK ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 57-67
Author(s):  
Hendro Purnomo
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Ordinary Kriging ◽  
Cross Validation ◽  
Inverse Distance Weighting ◽  
Mean Square ◽  
Nickel Laterite ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Weighting Methods

Pemilihan metode interpolasi yang sesuai untuk memprediksi kadar bijih pada lokasi yang tidak tersampel merupakan hal yang penting untuk pemetaan sebaran anomaly kadar dan estimasi sumberdaya. Tujuan penelitian ini dilakukan untuk mengevaluasi hasil estimasi metode ordinary kriging (OK) dan inverse distance weighting (IDW) dalam pemetaan distribusi dan potensi sumberdaya nikel (Ni) dan cobalt (Co) pada zona limonit dan saprolit. Dalam penelitian ini digunakan aplikasi perangkat lunak ArcGis 10.2 dengan Geostatistical Analyst Extention untuk menganalisis data. Untuk pemilihan model variogram dan interpolasi yang terbaik digunakan nilai parameter root mean square error (RMSE) yang diperoleh dari prosedur cross validation. Fitting variogram eksperimental dilakukan dengan model spherical, exponential dan gaussian, sedangkan pemilihan model variogram terbaik dilakukan berdasarkan nilai RMSE terkecil. Pada zona limonit, metode IDW dengan power 2 mempunyai performa terbaik untuk kadar Ni dan Co, sedangkan prosedur OK menghasilkan performa terbaik untuk  ketebalan. Pada zona saprolit metode IDW dengan power 5 mempunyai performa terbaik untuk kadar Ni dan IDW power 1 menunjukkan performa terbaik pada kadar co dan ketebalan. Hasil interpolasi menunjukkan bahwa distribusi nikel dan potensi tambahan sumberdaya pada zona limonit dan saprolit masih terbuka ke arah timur laut dan barat daya daerah penelitian.Kata Kunci: ArcGIS, cross validation, IDW, OK, RMSE

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