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

scholarly journals APLIKASI METODE INTERPOLASI INVERSE DISTANCE WEIGHTING DALAM PENAKSIRAN SUMBERDAYA LATERIT NIKEL (Studi kasus di Blok R, Kabupaten Konawe-Sulawesi Tenggara)

2018 ◽  
Vol 10 (1) ◽  
pp. 49
Author(s):  
Hendro Purnomo
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Inverse Distance Weighting ◽  
Mean Square ◽  
Distance Weighting ◽  
Inverse Distance

Abstrak. Inverse Distance Weighting (IDW) adalah salah satu metode interpolasi untuk menaksir suatu nilai pada lokasi yang tidak tersampel berdasarkan data disekitarnya. Metode ini sering digunakan dalam kegiatan eksplorasi karena dalam proses perhitungannya lebih sederhana dan mudah difahami. Penelitian ini bertujuan untuk memetakan sebaran bijih limonit secara lateral dan potensi sumberdaya nikel, dengan menggunakan metode IDW untuk menaksir kadar Ni dan ketebalan zona mineralisasi. Penelitian ini dilakukan berdasarkan data hasil pengebeboran prospeksi sebanyak 60 titik bor. Parameter power yang digunakan dalam proses interpolasi adalah 1, 2, 3, 4 dan 5. Untuk memilih nilai power yang terbaik digunakan parameter Root Mean Square Error (RMSE) yang diperoleh dari prosedur cross validation. Berdasarkan nilai RMSE terkecil diperoleh bahwa metode IDW dengan power 1 adalah yang terbaik. Dengan asumsi nilai cut-off grade adalah 1,2% Ni dan densitas limonit 1,6 ton/m³, hasil estimasi sumberdaya pada zona limonit adalah 151,7 juta ton bijih dengan kadar rata-rata 1,45% Ni atau setara dengan ± 2,2 juta ton logam nikel. Peta sebaran bijih menunjukkan bahwa potensi tambahan sumberdaya masih terbuka kearah selatan dan barat laut dari daerah penelitian.

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 Studi Komparasi Kriging dan IDW untuk Estimasi Spasial Bahan Organik Tanah

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Nursida Arif
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Inverse Distance Weighting ◽  
Mean Square ◽  
Distance Weighting ◽  
Inverse Distance

Interpolasi merupakan metode yang dapat diunggulkan untuk melakukan prediksi nilai-nilai sebaran pada suatu area berdasarkan data sampel. Karakteristik data sampel sangat mempengaruhi hasil dari metode interpolasi yang digunakan. Tujuan penelitian ini adalah membandingkan metode interpolasi Inverse Distance Weighting (IDW) dan kriging dengan berbagai variogram untuk memprediksi sebaran spasial bahan organik tanah di DAS Serang Kulonprogo. Metode yang digunakan untuk mengukur metode yang paling akurat yaitu: 1) menghitung root mean square error (RMSE), dan 2) melihat nilai minimum dan maksimum yang memenuhi data sampel. Hasil penelitian menunjukan metode IDW lebih akurat karena nilai yang dihasilkan mendekati nilai data sampel dan memenuhi nilai minimum dan maksimum data. Sedangkan RMSE terendah yaitu kriging dengan variogram gaussian.

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

Quantitative Analysis of Forsythiae fructus by Near Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 1967-1971
Author(s):  
Yan Bai ◽  
Xiao Yan Duan ◽  
Hai Yan Gong ◽  
Cai Xia Xie ◽  
Zhi Hong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Ethanol Extract ◽  
Mean Square ◽  
Validation Set

In this paper, the content of forsythoside A and ethanol-extract were rapidly determinated by near-infrared reflectance spectroscopy (NIRS). 85 samples of Forsythiae Fructus harvested in Luoyang from July to September in 2012 were divided into a calibration set (75 samples) and a validation set (10 samples). In combination with the partical least square (PLS), the quantitative calibration models of forsythoside A and ethanol-extract were established. The correlation coefficient of cross-validation (R2) was 0.98247 and 0.97214 for forsythoside A and ethanol-extract, the root-mean-square error of calibration (RMSEC) was 0.184 and 0.570, the root-mean-square error of cross-validation (RMSECV) was 0.81736 and 0.36656. The validation set were used to evaluate the performance of the models, the root-mean-square error of prediction (RMSEP) was 0.221 and 0.518. The results indicated that it was feasible to determine the content of forsythoside A and ethanol-extract in Forsythiae Fructus by near-infrared spectroscopy.

Multivariate analysis of three chemometric algorithms on rapid prediction of some important quality parameters of crude shea butter using Fourier transform-near infrared spectroscopy

2019 ◽  
Vol 27 (3) ◽  
pp. 220-231
Author(s):  
Emmanuel Amomba Seweh ◽  
Zou Xiaobo ◽  
Feng Tao ◽  
Shi Jiachen ◽  
Haroon Elrasheid Tahir ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square ◽  
Saponification Value ◽  
Peroxide Values

A comparative study of three chemometric algorithms combined with NIR spectroscopy with the aim of determining the best performing algorithm for quantitative prediction of iodine value, saponification value, free fatty acids content, and peroxide values of unrefined shea butter. Multivariate calibrations were developed for each parameter using supervised partial least squares, interval partial least squares, and genetic-algorithm partial least square regression methods to establish a linear relationship between standard reference and the Fourier transformed-near infrared predicted. Results showed that genetic-algorithm partial least square models were superior in predicting iodine value and saponification value while partial least squares was excellent in predicting free fatty acids content and peroxide values. The nine-factor genetic-algorithm partial least square iodine value calibration model for predicting iodine value yielded excellent ( R2 cal = 0.97), ( R2 val = 0.97), low (root mean square error of cross-validation = 0.26), low (root mean square error of Prediction = 0.23), and (ratio of performance to deviation = 6.41); for saponification value, the nine-factor genetic-algorithm partial least square saponification value calibration model had excellent R2 cal (0.97), R2 val (0.99); low root mean square error of cross-validation (0.73), low root mean square error of Prediction (0.53), and (ratio of performance to deviation = 8.27); while for free fatty acids, the 11-factor partial least square free fatty acids produced very high R2 cal (0.97) and R2 val (0.97) with very low root mean square error of cross-validation (0.03), low root mean square error of Prediction (0.04) and (ratio of performance to deviation = 5.30) and finally for peroxide values, the 11-factor partial least square peroxide values calibration model obtained excellent R2 cal (0.96) and R2val (0.98) with low root mean square error of cross-validation (0.05), low root mean square error of Prediction (0.04), and (ratio of performance to deviation = 5.86). The built models were accurate and robust and can be reliably applied in developing a handheld quality detection device for screening, quality control checks, and prediction of shea butter quality on-site.

Rapid Determination of Docosahexaenoic Acid in Powdered Oil by Near-Infrared Spectroscopy

2010 ◽  
Vol 16 (2) ◽  
pp. 187-193 ◽  
Author(s):  
Yang Meiyan ◽  
Li Jing ◽  
Nie Shaoping ◽  
Hu Jielun ◽  
Yu Qiang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Docosahexaenoic Acid ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Mean Square ◽  
Validation Set

Near-infrared spectroscopy (NIRS) was used as a rapid and nondestructive method to determine the content of docosahexaenoic acid (DHA) in powdered oil samples. A total of 82 samples were scanned in the diffuse reflectance mode by Nicolet 5700 FTIR spectrometer and the reference values for DHA was measured by gas chromatography. Calibration equations were developed using partial least-squares regression (PLS) with internal cross-validation. Samples were split in two sets, one set used as calibration (n = 66) whereas the remaining samples (n=16) were used as validation set. Two mathematical treatments (first and second derivative), none (log(1/R)) and standard normal variate as scatter corrections and Savitzky—Golay smoothing were explored. To decide upon the number of PLS factors included in the PLS model, the model with the lowest root mean square error of cross-validation (RMSECV=0.44) for the validation set is chosen. The correlation coefficient (r) between the predicted and the reference results which used as an evaluation parameter for the models is 0.968. The root mean square error of prediction of the final model is 0.59. The results reported in this article demonstrate that FT-NIR measurements can serve as a rapid method to determine DHA in powdered oil.

scholarly journals CHEMOMETRIC ASSISTED QUANTITATIVE DETERMINATION OF VITEXIN IN ETHANOLIC EXTRACT OF BINAHONG (ANREDERA CORDIFOLIA (TEN.) STEENIS) LEAVES

2020 ◽  
pp. 69-75
Author(s):  
OCTAVIANUS BUDI SANTOSA ◽  
MICHAEL RAHARJA GANI ◽  
SRI HARTATI YULIANI
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Multivariate Calibration ◽  
Data Sets ◽  
Calibration Data ◽  
Mean Square ◽  
Calibration Models ◽  
Pcr Method

Objective: The objective of this study was to develop a UV spectroscopy method in combination with multivariate analysis for determining vitexin in binahong (Anredera cordifolia (Ten.) Steenis) leaves extract. Methods: The partial least square (PLS) regression and the principal component regression (PCR) was performed in this study to evaluate several statistical performances such as coefficient of determination (R2), root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and relative error of prediction (REP). Cross-validation in this study was performed using leave one out technique. Results: The R2 values of calibration data sets resulted from PLS ​​and PCR method were 0.9675 and 0.9648, respectively. The low values of RMSEC and RMSECV both for PLS ​​and PCR method indicated the minimum error of the calibration models. The R2 values of validation data sets resulted from PLS ​​and PCR method were 0.9778 and 0.9820, respectively. The low values of RMSEP both for PLS ​​and PCR method indicated the minimum error of prediction generated from the calibration data sets. Multivariate calibration techniques were applied to determine the content of vitexin in binahong leaves extract. Predicted values from the multivariate calibration models were compared to the actual values determined from a validated HPLC method. It was found that PLS models resulted in the lowest REP values compared to the PCR models. Conclusion: The chemometrics technique can be applied as an alternative method for determining vitexin levels in the ethanol solution of binahong leaves extract.

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