scholarly journals Spatial interpolation methods for estimating monthly rainfall distribution in Thailand

2021 ◽  
Author(s):  
Nawinda Chutsagulprom ◽  
Kuntalee Chaisee ◽  
Ben Wongsaijai ◽  
Papangkorn Inkeaw ◽  
Chalump Oonariya
Keyword(s):  
Spatial Interpolation ◽  
Cross Validation ◽  
Monthly Rainfall ◽  
Rainfall Distribution ◽  
Mathematical Concepts ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Fold Cross Validation ◽  
Spatial Interpolation Methods

Abstract Spatial interpolation methods usually differ in their underlying mathematical concepts, each with inherent advantages and drawbacks depending on the properties of data. This paper, therefore, aims to compare and evaluate the performances of well-established interpolation techniques for estimating monthly rainfall data in Thailand. The selected methods include the inverse distance-based method, multiple linear regression (MLR), artificial neural networks (ANN), and ordinary kriging (OK). The technique of searching nearest stations is additionally imposed for some aforementioned schemes. The k -fold cross-validation method is exploited to assess the efficiency of each method, then the metric scores, RMSE, and MAE are used for comparisons. The results suggest the ANN might be the least favorite as it underperforms in many folds. While the OK method provides the most accurate prediction, the inverse distance weighting (IDW), particularly inverse exponential weighting (IEW), and MLR are considerably comparative. Overall, IEW is plausible for monthly rainfall estimation of Thailand because it is less computationally expensive than the OK and its flexible computation.

scholarly journals Application of the Modified Shepard’s Method (MSM): A Case Study with the Interpolation of Neogene Reservoir Variables in Northern Croatia

Stats ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 68-83 ◽  
Author(s):  
Tomislav Malvić ◽  
Josip Ivšinović ◽  
Josipa Velić ◽  
Jasenka Sremac ◽  
Uroš Barudžija
Keyword(s):  
Cross Validation ◽  
Statistical Properties ◽  
Inverse Distance Weighting ◽  
Geological Mapping ◽  
Interpolation Methods ◽  
Geometrical Features ◽  
Distance Weighting ◽  
Northern Croatia ◽  
Inverse Distance

Interpolation is a procedure that depends on the spatial and/or statistical properties of the analysed variable(s). It is a particularly challenging task for small datasets, such as in those with less than 20 points of data. This problem is common in subsurface geological mapping, i.e., in cases where the data is taken solely from wells. Successful solutions of such mapping problems depend on interpolation methods designed primarily for small datasets and the datasets themselves. Here, we compare two methods, Inverse Distance Weighting and the Modified Shepard’s Method, and apply them to three variables (porosity, permeability, and thickness) measured in the Neogene sandstone hydrocarbon reservoirs (northern Croatia). The results show that cross-validation itself will not provide appropriate map selection, but, in combination with geometrical features, it can help experts eliminate the solutions with low-probable structures/shapes. The Golden Software licensed program Surfer 15 was used for the interpolations in this study.

scholarly journals A Spatial Interpolation of Meteorological Parameters considering Geographic Semantics

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Wenjun Wu ◽  
Ruijie Gan ◽  
Junli Li ◽  
Xiu Cao ◽  
Xinxin Ye ◽  
...  
Keyword(s):  
Land Surface ◽  
Spatial Interpolation ◽  
Meteorological Parameters ◽  
Weight Coefficient ◽  
Estimation Accuracy ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Spatial Attributes ◽  
Sample Points ◽  
Spatial Interpolation Methods

Spatial interpolation of meteorological parameters, closely related to the earth surface, plays important roles in climatological study. However, most of traditional spatial interpolation methods ignore the geographic semantics of interpolation sample points in practical application. This paper attempts to propose an improved inverse-distance weighting interpolation algorithm considering geographic semantics (S-IDW), which adds geographic semantic similarity to the traditional IDW formula and adjusts weight coefficient. In the interpolation process, the geographic semantic differences between sample points and estimation points are considered comprehensively. In this study, 3 groups of land surface temperature data from 2 different areas were selected for experiments, and several commonly used spatial interpolation methods were compared. Experimental results indicated that S-IDW outperformed IDW and several existing spatial interpolation methods, but there were also some abnormal value and interpolation outliers. This method provides a new insight toward the estimation accuracy, data missing, and error correction of spatial attributes related to meteorological parameters.

scholarly journals Comparison of methods for spatial interpolation of fire weather in Alberta, Canada

2017 ◽  
Vol 47 (12) ◽  
pp. 1646-1658 ◽  
Author(s):  
P. Jain ◽  
M.D. Flannigan
Keyword(s):  
Spatial Interpolation ◽  
Weather Data ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Interpolation Methods ◽  
Geostatistical Methods ◽  
Geostatistical Interpolation ◽  
Distance Weighting ◽  
Inverse Distance

Spatial interpolation of fire weather variables from station data allow fire danger indices to be mapped continuously across the landscape. This information is crucial to fire management agencies, particularly in areas where weather data are sparse. We compare the performance of several standard interpolation methods (inverse distance weighting, spline, and geostatistical interpolation methods) for estimating output from the Canadian Fire Weather Index (FWI) system at unmonitored locations. We find that geostatistical methods (kriging) generally outperform the other methods, particularly when elevation is used as a covariate. We also find that interpolation of the input meteorological variables and the previous day’s moisture codes to unmonitored locations followed by calculation of the FWI output variables is preferable to first calculating the FWI output variables and then interpolating, in contrast to previous studies. Alternatively, when the previous day’s moisture codes are estimated from interpolated weather, rather than directly interpolated, errors can accumulate and become large. This effect is particularly evident for the duff moisture code and drought moisture code due to their significant autocorrelation.

scholarly journals Application of Modified Shepard's Method (MSM) case study with interpolation of Neogene reservoirs variables in the Northern Croatia

2019 ◽  
Author(s):  
Tomislav Malvić ◽  
Josip Ivšinović ◽  
Josipa Velić ◽  
Jasenka Sremac ◽  
Uroš Barudžija
Keyword(s):  
Cross Validation ◽  
Statistical Properties ◽  
Inverse Distance Weighting ◽  
Geological Mapping ◽  
Interpolation Methods ◽  
Geometrical Features ◽  
Distance Weighting ◽  
Northern Croatia ◽  
Inverse Distance

Interpolation is procedure that depends on spatial and/or statistical properties of analysed variable(s). It is special challenging task for data that included low number of samples, like dataset with less than 20 data. This problem is especially emphasized in the subsurface geological mapping, i.e. in the cases where data are taken solely from wells. Successful solutions of such mapping problems ask for knowledge about interpolation methods designed primarily for small datasets and dataset itself. Here are compared two methods, namely Inverse Distance Weighting and Modified Shepard’s Method, applied for three variables (porosity, permeability, thickness) measured in the Neogene sandstone hydrocarbon reservoirs (Northern Croatia). The results showed that pure cross-validation is not enough condition for appropriate map selection, but also geometrical features need to be considered, for datasets with less than 20 points.

scholarly journals Ordinary kriging vs inverse distance weighting: spatial interpolation of the sessile community of Madagascar reef, Gulf of Mexico

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4078 ◽  
Author(s):  
Salvador Zarco-Perello ◽  
Nuno Simões
Keyword(s):  
Remote Sensing ◽  
Gulf Of Mexico ◽  
Spatial Variability ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Prediction Errors ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Inverse Distance

Information about the distribution and abundance of the habitat-forming sessile organisms in marine ecosystems is of great importance for conservation and natural resource managers. Spatial interpolation methodologies can be useful to generate this information from in situ sampling points, especially in circumstances where remote sensing methodologies cannot be applied due to small-scale spatial variability of the natural communities and low light penetration in the water column. Interpolation methods are widely used in environmental sciences; however, published studies using these methodologies in coral reef science are scarce. We compared the accuracy of the two most commonly used interpolation methods in all disciplines, inverse distance weighting (IDW) and ordinary kriging (OK), to predict the distribution and abundance of hard corals, octocorals, macroalgae, sponges and zoantharians and identify hotspots of these habitat-forming organisms using data sampled at three different spatial scales (5, 10 and 20 m) in Madagascar reef, Gulf of Mexico. The deeper sandy environments of the leeward and windward regions of Madagascar reef were dominated by macroalgae and seconded by octocorals. However, the shallow rocky environments of the reef crest had the highest richness of habitat-forming groups of organisms; here, we registered high abundances of octocorals and macroalgae, with sponges, Millepora alcicornis and zoantharians dominating in some patches, creating high levels of habitat heterogeneity. IDW and OK generated similar maps of distribution for all the taxa; however, cross-validation tests showed that IDW outperformed OK in the prediction of their abundances. When the sampling distance was at 20 m, both interpolation techniques performed poorly, but as the sampling was done at shorter distances prediction accuracies increased, especially for IDW. OK had higher mean prediction errors and failed to correctly interpolate the highest abundance values measured in situ, except for macroalgae, whereas IDW had lower mean prediction errors and high correlations between predicted and measured values in all cases when sampling was every 5 m. The accurate spatial interpolations created using IDW allowed us to see the spatial variability of each taxa at a biological and spatial resolution that remote sensing would not have been able to produce. Our study sets the basis for further research projects and conservation management in Madagascar reef and encourages similar studies in the region and other parts of the world where remote sensing technologies are not suitable for use.

scholarly journals Analysis of rainfall distribution in Kelantan river basin, Malaysia

2018 ◽  
Vol 34 ◽  
pp. 02020
Author(s):  
Faizah Che Ros ◽  
Hiroyuki Tosaka
Keyword(s):  
Spatial Interpolation ◽  
Rainfall Runoff ◽  
Rainfall Distribution ◽  
Flood Prediction ◽  
Distance Weighting ◽  
Average Rainfall ◽  
Kelantan River Basin ◽  
Distribution Sensitivity ◽  
Inverse Distance ◽  
Daily Time

Using rainfall gauge on its own as input carries great uncertainties regarding runoff estimation, especially when the area is large and the rainfall is measured and recorded at irregular spaced gauging stations. Hence spatial interpolation is the key to obtain continuous and orderly rainfall distribution at unknown points to be the input to the rainfall runoff processes for distributed and semi-distributed numerical modelling. It is crucial to study and predict the behaviour of rainfall and river runoff to reduce flood damages of the affected area along the Kelantan river. Thus, a good knowledge on rainfall distribution is essential in early flood prediction studies. Forty six rainfall stations and their daily time-series were used to interpolate gridded rainfall surfaces using inverse-distance weighting (IDW), inverse-distance and elevation weighting (IDEW) methods and average rainfall distribution. Sensitivity analysis for distance and elevation parameters were conducted to see the variation produced. The accuracy of these interpolated datasets was examined using cross-validation assessment.

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

Comparison of Quantitative Precipitation Estimation Using Spatial Interpolation methods in Bukhan River Basin

2020 ◽  
Author(s):  
Sanghoo Yoon ◽  
Junseok Kim ◽  
Taeyong Kwon
Keyword(s):  
Spatial Interpolation ◽  
Mean Squared Error ◽  
Additive Model ◽  
Absolute Error ◽  
Interpolation Methods ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Weight Method ◽  
Inverse Distance ◽  
Spatial Interpolation Methods

<p>Quantitative precipitation estimation is needed to reduce damages from weather disasters such as torrential rain. This study is dealt with estimates of the quantitative precipitation using multiple spatial interpolation methods and compares the results. Inverse distance weight method and k-nearest neighborhood algorithm were considered as a deterministic approach and the general additive model and kriging methods were used as a stochastic approach. To evaluate the prediction performance, leave-one-out cross-validation was performed with the root mean squared error (RMSE), mean absolute error (MAE), bias, and correlation coefficient. The research data were rain gauged and radar data in the Bukhan river, which were collected from May 2018 to August 2019. The results showed that the inverse distance weight method reflected the spatial rainfall characteristics well. However, caution is needed because the best models vary depending on the pattern of rainfall in the sense of RMSE.</p><p>*This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD(No. 2018-Tech-20)</p>

Application of the "genRE" approach to spatial interpolation of precipitation gauge data for the Suir River Basin

2021 ◽  
Author(s):  
Georgios Boumis ◽  
Bart van Osnabrugge ◽  
Jan Verkade
Keyword(s):  
Real Time ◽  
River Basin ◽  
Spatial Interpolation ◽  
Cross Validation ◽  
Inverse Distance Weighting ◽  
Data Sets ◽  
Precipitation Data ◽  
Data Set ◽  
Distance Weighting ◽  
Inverse Distance

<p>Operational near real-time flood forecasting relies heavily on adequate spatial interpolation of precipitation forcing which bears a huge impact on the accuracy of hydrologic forecasts. In this study, the generalized REGNIE (genRE) interpolation technique is examined. The genRE approach was shown to enhance the traditional Inverse Distance Weighting (IDW) method with information from existing observed climatological precipitation data sets (Van Osnabrugge, 2017). The successful application of the genRE method with a re-analysis precipitation data set, expands the applicability of the method as detailed re-analysis data sets become more prevalent while high density observation networks remain scarce.</p><p>Here, the approach is extended to use climatological precipitation data from the Met Éireann’s Re-Analysis (MÉRA). Investigations are carried out using hourly precipitation accumulations for two major flood events induced by Atlantic storms in the Suir River Basin, Ireland. Alongside genRE, the following techniques are comparatively explored: Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and Regression Kriging (RK). Cross-validation is applied in order to compare the different interpolation methods, while spatial maps and correlation coefficients are utilized for assessing the skill of the interpolators to emulate the climatology of MÉRA. In the process, a preliminary intercomparison between the observed precipitation and MÉRA precipitation for the two events is also made.</p><p>In a statistical sense, cross-validation results verify that genRE performs slightly better than all three interpolation techniques for both events studied. Overall, OK is found to be the most inadequate approach, specifically in terms of preserving the original variance in observed precipitation. MÉRA manages to reproduce the temporal variations of observations in a good manner for both events, whereas it displays less skill when considering spatial variations especially where topography has a major influence. Finally, genRE outperforms all other interpolators in mimicking the climatological conditions of MÉRA for both events.</p><p> </p><p>Van Osnabrugge, B., Weerts, A.H. and Uijlenhoet, R., 2017. genRE: A method to extend gridded precipitation climatology data sets in near real-time for hydrological forecasting purposes. Water Resources Research, 53(11), pp.9284-9303.</p>

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