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spatial interpolation of Daily Precipitation in China:1951-2005

Advances in Atmospheric Sciences 2010年第6期27卷 1221-1232页

作者： Deliang CHEN Tinghai OU Lebing GONG ChongYu XU 李维京 Chang—Hoi HO 钱维宏 Department of Earth Sciences University of Gothenburg Gothenburg Sweden School of Geography Beijing Normal University Beijing 100875 Department of Earth Sciences Uppsala University Sweden Department of Geosciences University of Oslo Norway Laboratory for Climate Studies/National Climate Center China Meteorological Administration Beijing 100029 School of Earth and Environmental Sciences Seoul National University Korea School of Physics Peking University Beijing 100871

Climate research relies heavily on good quality instrumental data; for modeling efforts gridded data are needed. So far, relatively little effort has been made to create gridded climate data for China. This is especially true for high-resolution daily data. This work, focuses on identifying an accurate method to produce gridded daily precipitation in China based on the observed data at 753 stations for the period 1951-2005. Five interpolation methods, including ordinary nearest neighbor, local polynomial, radial basis function, inverse distance weighting, and ordinary kriging, have been used and compared. Cross-validation shows that the ordinary kriging based on seasonal semi-variograms gives the best performance, closely followed by the inverse distance weighting with a power of 2. Finally the ordinary kriging is chosen to interpolate the station data to a 18 km× 18 km grid system covering the whole country. Precipitation for each 0.5°×0.5° latitude-longitude block is then obtained by averaging the values at the grid nodes within the block. Owing to the higher station density in the eastern part of the country, the interpolation errors are much smaller than those in the west （west of 100°E）. Excluding 145 stations in the western region, the daily, monthly, and annual relative mean absolute errors of the interpolation for the remaining 608 stations are 74%, 29%, and 16%, respectively. The interpolated daily precipitation has been made available on the internet for the scientific community.

关键词： daily precipitation spatial interpolation ordinary kriging gridded data China

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spatial interpolation of soil nutrients using algebra hyper-curve neural network

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International Journal of Agricultural and Biological Engineering 2008年第1期1卷 51-56页

作者： Chen Liping Zhao Chunjiang Huang Wenqian Chen Tian’en Wang Jihua Liu Zhenyan Hu Jing National Engineering Research Center for Information Technology in Agriculture Beijing 100097China Department of Software Engineering College of SoftwareBeijing Institute of TechnologyBeijing 100081China

spatial distribution of and interpolation methods for soil nutrients are the basis of soil nutrient management in precision agriculture.For study of application potential and characteristics of algebra hyper-curve neural network(AHCNN)in delineating spatial variability and interpolation of soil properties,956 soil samples were taken from a 50 hectare field with 20 m interval for alkaline hydrolytic nitrogen measurement.The test data set consisted of 100 random samples extracted from the 956 samples,and the training data set extracted from the remaining samples using 20,40,60,80,100 and 120 m grid intervals.Using the AHCNN model,three training plans were designed,including plan AHC1,using spatial coordinates as the only network input,plan AHC2,adding information of four neighboring points as network input,and plan AHC3,adding information of six neighboring points as network input.The interpolation precision of AHCNN method was compared with that of Kriging method.When the number of training samples was big,interpolation precisions of Kriging and AHCNN were similar.When the number of training samples was small,the precisions of both methods deteriorated.Since AHCNN method has no request on data distribution and it is non-linearization of neutron input variables,it is suitable for delineation of spatial distribution of nonlinear soil properties.In addition,AHCNN has an advantage of adaptive self-adjustment of model parameters,which makes it proper for soil nutrient spatial interpolation.After comparison of mean absolute error d,root mean squared error RMSE,and mean relative error%d,and the spatial distribution maps generated from different methods,it can be concluded that using spatial coordinates as the only network input cannot simulate the characteristics of soil nutrient spatial variability well,and the simulation results can be improved greatly after adding neighboring sample points’information and the distance effect as network input.When the number of samples was small,interpolation precision can be improved

关键词： algebra hyper-curve neural network(AHCNN) spatial interpolation soil nutrients spatial variability Kriging interpolation

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Comparison of spatial interpolation Methods for Gridded Bias Removal in Surface Temperature Forecasts

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Journal of Meteorological Research 2017年第4期31卷 791-799页

作者： Seyedeh Atefeh MOHAMMADI Majid AZADI Morteza RAHMANI Technology Development Institute Tehran 13445-686 Iran Atmospheric Science and Meteorological Research Center Tehran 16385-14977 Iran Faculty of Basic and Advanced Technologies in Biology University of Science and Culture Tehran 14619-68151 Iran

All numerical weather prediction（NWP） models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate（IDSW-CLR）, Kriging with constant lapse rate（Kriging-CLR）, gradient inverse distance squared with linear lapse rate（GIDS-LR）, and gradient inverse distance squared with lapse rate determined by classification and regression tree（GIDS-CART）, are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly,but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error（RMSE）and mean absolute error（MAE） were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.

关键词： spatial interpolation bias correction lapse rate Kriging classification and regression tree

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Reconstructing pre-erosion topography using spatial interpolation techniques： A validation-based approach

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Journal of Geographical Sciences 2015年第2期25卷 196-210页

作者： Rafaello BERGONSE Eusebio REIS Center for Geographical Studies University of Lisbon Portugal

Understanding the topographic context preceding the development of erosive landforms is of major relevance in geomorphic research, as topography is an important factor on both water and mass movement-related erosion, and knowledge of the original surface is a condition for quantifying the volume of eroded material. Although any reconstruction implies assuming that the resulting surface reflects the original topography, past works have been dominated by linear interpolation methods, incapable of generating curved surfaces in areas with no data or values out- side the range of variation of inputs. In spite of these limitations, impossibility of validation has led to the assumption of surface representativity never being challenged. In this paper, a validation-based method is applied in order to define the optimal interpolation technique for reconstructing pre-erosion topography in a given study area. In spite of the absence of the original surface, different techniques can be nonetheless evaluated by quantifying their ca- pacity to reproduce known topography in unincised locations within the same geomorphic contexts of existing erosive landforms. A linear method （Triangulated Irregular Network, TIN） and 23 parameterizations of three distinct Spline interpolation techniques were compared using 50 test areas in a context of research on large gully dynamics in the South of Portugal. Results show that almost all Spline methods produced smaller errors than the TIN, and that the latter produced a mean absolute error 61.4% higher than the best Spline method, clearly establishing both the better adjustment of Splines to the geomorphic context considered and the limitations of linear approaches. The proposed method can easily be applied to different interpolation techniques and topographic contexts, enabling better calculations of eroded volumes and denudation rates as well as the investigation of controls by antecedent topographic form over erosive processes.

关键词： pre-erosion topography surface reconstruction spatial interpolation spline interpolation triangulated irregular networks erosive landforms gully erosion

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Comparative Study on spatial interpolation Methods for 3D Modeling of Coal and Rock Layers Based on Borehole Data

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Meteorological and Environmental Research 2022年第6期13卷 68-72,77页

作者： Xiang GE Liangliang YANG Huainan City Construction and Development Planning and Design Institute Co. Ltd.Huainan 232001China

The patial interpolation of borehole data is an important means of stratigraphic structure to construct a three-dimensional model of coal strata,and the reasonable selection of an effective spatial interpolation method will directly affect the accuracy of three-dimensional modeling of the strata.To select an effective spatial interpolation method and improve the accuracy of 3D modeling of formations,four interpolation methods(the inverse distance weight interpolation algorithm,the local polynomial interpolation algorithm,the radial basis neural network interpolation algorithm and the kriging interpolation algorithm)were compared and analyzed.In particular,the methods of interpolation algorithm,interpolation surface,sample test error,and cross-validation error were used.The experiment of 13-1 seam coal in the Huainan mining area showed the spatial surface interpolation effect of the radial basis neural network interpolation algorithm(RBF)compared with the inverse distance weight interpolation algorithm(IDW),local polynomial interpolation algorithm(LPI)and kriging algorithm.The three interpolation methods have higher accuracy and are more suitable for surface interpolation of coal seams,which is of great significance for improving the accuracy of subsequent 3D modeling of coal seams.

关键词： Drilling spatial interpolation Coal-rock surface Cross-validation

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Edge-Oriented spatial interpolation for Error Concealment of Consecutive Blocks

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Journal of Computer Science & Technology 2007年第3期22卷 494-496,F0003页

作者：付炜邢广忠 Department of Electronic Engineering Yanshan University Qinhuangdao 066004 China Department of Material Science Yanshan University Qinhuangdao 066004 China

This paper proposes a low-complexity spatial-domain error concealment （EC） algorithm for recovering consecutive blocks error in still images or intra-coded （I） frames of video sequences. The proposed algorithm works with the following steps. Firstly the Sobel operator is performed on the top and bottom adjacent pixels to detect the most probable edge direction of current block area. After that one-dimensional （1-D） matching is used on the available block boundaries. Displacement between edge direction candidate and most probable edge direction is taken into consideration as an important factor to improve stability of 1-D boundary matching. Then the corrupted pixels are recovered by linear weighting interpolation along the estimated edge direction. Finally the interpolated values are merged to get last recovered picture. Simulation results demonstrate that the proposed algorithms obtain good subjective quality and higher PSNR than the methods in literatures for most images.

关键词： error concealment （EC） spatial interpolation boundary matching Sobel operator

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spatial interpolation Applied a Crustal Thickness in Brazil

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Journal of Geographic Information System 2012年第2期4卷 142-152页

作者： Cesar Garcia Pavao George Sand Franca Giuliano S.Marotta Paulo Henrique B.J.Menezes Gervasio Barbosa S.Neto Henrique Llacer Roig Seismological Observatory University of BrasiliaBrasiliaBrazil Laboratory of Remote Sensing and spatial Analysis University of BrasiliaBrasiliaBrazil

The use of spatial interpolation methods of data is becoming increasingly common in geophysical analysis, for that reason, currently, several software already contain many of these methods, allowing more detailed studies. In the present work four interpolation methods are evaluated, for the crustal thickness data of Brazil tectonic provinces, with the intention of making Moho’s map of the regions. The methods used were IDW, Natural Neighbor, Spline and Kriging. We compiled 257 data that constituted a geographic database implemented in the template Postgree PostGIS and were processed using the tools of interpolation located in the Spatyal Analyst Tools program ArcGIS?9 ESRI. Traditional methods, IDW, Natural Neighbor and Spline, generate artifacts in their results, the effects of aim, not consistent with the behavior of crust. Such anomalies are generated because of mathematical formulation methods added to data compiled gravimetry. The analysis results of geostatistical Kriging are more refined and consistent, showing no specific anormalities, i.e., the crustal thickness variation (thinning and thickening) is introduced gradually. Initial our estimates were separated in four specific blocks. With the approval of new networks (BRASIS, RSISNE and RSIS), the crustal thickness database for Brazil may be amended or supplemented so that new models may be generated more consistently, complementing studies of regional tectonics evolution and seismicity.

关键词： spatial interpolation Crustal Thickness Tectonic Provinces of Brazil

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spatial interpolation Method of the Meteorological Factor in Northeast China Based on GIS

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Meteorological and Environmental Research 2012年第3期3卷 23-25,33页

作者： LI Li-shuang ZHANG Ying School of Computer Science and Technology Dalian University of TechnologyDalian 116024China

[ Objective ] The research aimed to study the best spatial interpolation method of the meteorological factor in Northeast China. [ Method ] Based on geostatistical analysis tool of the Arclnfo GIS software, several spatial interpolation methods were used to estimate the meteorological fac- tore （annual rainfall and monthly average temperature） in Northeast China, such as inverse distance weighted （IDW）, radial basis function （RBF） and Kriging. Then, the best interpolation method of one meteorological factor was selected. [ Result] For monthly average temperature, Kriging method was better than others. For annual rainfall, precision of the evaluated value with RBF method was higher than that of the IDW and Kriging methods. [Conclusion] There was obvious regional difference of the meteorological factor in Northeast China. Monthly average temperature in south was higher than that in north, and annual rainfall in southeast was more than that in northwest in Northeast China.

关键词： GIS Northeast China Meteorological factor spatial interpolation China

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A Spatiotemporal Interactive Processing Bias Correction Method for Operational Ocean Wave Forecasts

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Journal of Ocean University of China 2022年第2期21卷 277-290页

作者： AI Bo YU Mengchao GUO Jingtian ZHANG Wei JIANG Tao LIU Aichao WEN Lianjie LI Wenbo College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao 266590China North China Sea Marine Forecasting Center of State Oceanic Administration Qingdao 266100China 391033 Troops People’s Liberation Army of ChinaQingdao 266000China

Numerical models and correct predictions are important for marine forecasting,but the forecasting results are often unable to satisfy the requirements of operational wave forecasting.Because bias between the predictions of numerical models and the actual sea state has been observed,predictions can only be released after correction by forecasters.This paper proposes a spati-otemporal interactive processing bias correction method to correct numerical prediction fields applied to the production and release of operational ocean wave forecasting products.The proposed method combines the advantages of numerical models and Forecast Discussion;specifically,it integrates subjective and objective information to achieve interactive spatiotemporal correc-tions for numerical prediction.The method corrects the single-time numerical prediction field in space by spatial interpolation and sub-zone numerical analyses using numerical model grid data in combination with real-time observations and the artificial judg-ment of forecasters to achieve numerical prediction accuracy.The difference between the original numerical prediction field and the spatial correction field is interpolated to an adjacent time series by successive correction analysis,thereby achieving highly efficient correction for multi-time forecasting fields.In this paper,the significant wave height forecasts from the European Centre for Medium-Range Weather Forecasts are used as background field for forecasting correction and analysis.Results indicate that the proposed method has good application potential for the bias correction of numerical predictions under different sea states.The method takes into account spatial correlations for the numerical prediction field and the time series development of the numerical model to correct numerical predictions efficiently.

关键词： numerical models ocean wave forecasts spatial interpolation time series interpolation successive correction

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A generic framework for geotechnical subsurface modeling with machine learning

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第5期14卷 1366-1379页

作者： Jiawei Xie Jinsong Huang Cheng Zeng Shan Huang Glen J.Burton Discipline of Civil Surveying and Environmental EngineeringPriority Research Centre for Geotechnical Science and EngineeringThe University of NewcastleCallaghanNSW2308Australia ATC Williams Pty.Ltd. SingletonNSW2330Australia

This study introduces a generic framework for geotechnical subsurface modeling, which accounts for spatial autocorrelation with local mapping machine learning(ML) methods. Instead of using XY coordinate fields directly as model input, a series of autocorrelated geotechnical distance fields(GDFs) is designed to enable the ML models to infer the spatial relationship between the sampled locations and unknown locations. The whole framework using GDF with ML methods is named GDF-ML. This framework is purely data-driven which avoids the tedious work in the scale of fluctuations(SOFs)estimating and data detrending in the conventional spatial interpolation methods. Six local mapping ML methods(extra trees(ETs), gradient boosting(GB), extreme gradient boosting(XGBoost), random forest(RF), general regression neural network(GRNN) and k-nearest neighbors(KNN)) are compared in the GDF-ML framework. The results show that the GDFs are better than the conventional XY coordinate fields based ML methods in both accuracy and spatial continuity. GDF-ML is flexible which can be applied to high-dimensional, multi-variable and incomplete datasets. Among these six methods, GDF with ET method(GDF-ET) clearly shows the best accuracy and best spatial continuity. The proposed GDF-ET method can provide a fast and accurate interpretation of the soil property profile. Sensitivity analysis shows that this method is applicable to very small training dataset size. The associated statistical uncertainty can also be quantified so that the reliability of the subsurface modeling results can be estimated objectively and explicitly. The uncertainty results clearly show that the prediction becomes more accurate when more sampled data are available.

关键词： Site investigation Machine learning(ML) spatial interpolation Geotechnical distance fields(GDFs) Tree-based models

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