Spatial estimation of wind speed:a new integrative model using inverse distance weighting and power law

Spatial interpolation(SI)is currently one of the most common ways to estimate wind speed(Ws).However,classic SI models either ignore the complex geography[e.g.inverse distance weighting(IDW)],or demand high computational resources(e.g.cokriging).This study aimed to develop a simple yet effective SI model for estimating Ws in Eastern Thrace of Turkey.This new method,named MIDW(Ws),is a modified IDW through the integration of IDW with wind profile model,power law(PL),representing the influence of land cover and topography on Ws.Terrain features and elevation data of PL were obtained using normalized difference vegetation index(NDVI)and digital elevation model(DEM),respectively.Results showed superior and comparable performance of MIDW(Ws)to standard IDW and ordinary kriging(OK)across all months of year.Compared to ordinary cokriging(OCK)using DEM as covariate,MIDW(Ws)generated better results in the arid–semiarid seasons(around summer).Local complex atmospheric conditions during rainy seasons(around winter)may have affected the performance of incorporating PL with MIDW(Ws).Generally,the proposed MIDW(Ws)is simpler and easier to implement compared to OCK.For landscape-scale projects,its high computational efficiency and relatively robust performance show potential to deal with large volumes of datasets.

Mapping Distribution of Precipitation, Temperature and Evaporation in Seydisuyu Basin with the Help of Distance Related Estimation Methods

In this research, distributions of precipitation, temperature and evaporation in Seydisuyu basin were analyzed with the help of inverse distance weighted (IDW) method. Because real meteorological data of the basin (precipitation, temperature and evaporation) do not have normal distribution, precipitation, temperature and evaporation distribution maps are drawn after normalization process. The number of meteorological stations, in other words the number of samples, is low, so only IDW method is used in this research. In addition to the research, reliability of the results obtained with the help of inverse distance weighting method was examined with accuracy analysis. The purpose of this study, the spatial distribution of meteorological data on a basin or areas is to demonstrate the applicability of the statistical basis.

Using WGM2012 to Compute Gravity Anomaly Corrections of Leveling Observations in China

Gravity Anomaly Correction(GAC)is a very important term in leveling data processing.In most cases,it is troublesome for field surveyors to measure gravity when leveling.In this paper,based on the complete Bouguer Gravity Anomaly(BGA)map of WGM2012,the feasibility of replacing in-situ gravity surveying in China is investigated.For leveling application,that is to evaluate the accuracy of WGM2012 in China.Because WGM2012 is organized with a standard rectangle grid,two interpolation methods,bilinear interpolating and Inverse Distance Weighted(IDW)interpolating,are proposed.Four sample areas in China,i.e.,Hanzhong,Chengdu,Linzhi and Shantou,are selected to evaluate the systems bias and precision of WGM2012.Numerical results show the average system bias of WGM2012 BGA in west China is about-100.1 mGal(1 mGal=10^(-5) m/s^(2))and the standard deviation is about 30.7 mGal.Tests in Shantou indicate the system bias in plain areas is about-130.4 mGal and standard deviation is about 6.8 mGal.All these experiments means the accuracy of WGM2012 is limited in high mountain areas of western China,but in plain areas,such as Shantou,WGM2012 BGA map is quite good for most leveling applications after calibrating the system bias.

Water Quality Evaluation of Chapurson Valley in Hunza Nagar, Gilgit Baltistan, Pakistan, Based on Statistical Analysis and Water Quality Index

Water borne ailments are of serious public health concern in Gilgit Baltistan’s (GB) region of Pakistan. The pollution load on the glacio-fluvial streams and surface water resources of the Chapurson Valley in the Hunza Nagar area of the GB is increasing as a result of anthropogenic activities and tourism. The present study focuses on the public health quality of drinking water of Chapurson valley. The study addressed the fundamental drinking water quality criteria in order to understand the state of the public health in the valley. To ascertain the current status of physico-chemical, metals, and bacteriological parameters, 25 water samples were collected through deterministic sampling strategy and examined accordingly. The physico-chemical parameters of the water samples collected from the valley were found to meet the World Health Organization (WHO) guidelines of drinking water. The water samples showed a pattern of mean metal concentrations in order of Arsenic (As) > Lead (Pb) > Iron (Fe) > Zinc (Zn) > Copper (Cu) > Magnesium (Mg) > Calcium (Ca). As, Cu, Zn, Ca and Mg concentration were under the WHO guidelines range. However, results showed that Pb and Fe are present at much higher concentrations than recommended WHO guidelines. Similarly, the results of the bacteriological analysis indicate that the water samples are heavily contaminated with the organisms of public health importance (including total coliforms (TCC), total faecal coliforms (TFC) and total fecal streptococci (TFS) are more than 3 MPN/100mL). Three principal components, accounting for 48.44% of the total variance, were revealed using principal component analysis (PCA). Bacteriological parameters were shown to be the main determinants of the water quality as depicted by the PCA analysis. The dendrogram of Cluster analysis using the Ward’s method validated the same traits of the sampling locations that were found to be contaminated during geospatial analysis using the Inverse Distance Weight (IDW) method. Based on these findings, it is most likely that those anthropogenic activities and essentially the tourism results in pollution load from upstream channels. Metals may be released into surface and groundwater from a few underlying sources as a result of weathering and erosion. This study suggests that the valley water resources are more susceptible to bacteriological contamination and as such no water treatment facilities or protective measure have been taken to encounter the pollution load. People are drinking the contaminated water without questioning about the quality. It is recommended that the water resources of the valley should be monitored using standard protocol so as to protect not only the public health but to safe guard sustainable tourism in the valley.

GIS Mapping, Biomonitoring and Distribution of Heavy Metal Concentrations Using Neem (Azadirachta indica) Bark in Makurdi Town, Benue State, Nigeria

The present work determined the concentration and spatial distribution of heavy metals (As, Hg, Cr, Cd and Pb) in soil samples from Makurdi and its environs using geographic information system (GIS). The specific objective is to produce the spatial distribution maps showing the spatial distribution of toxic heavy metals in the study area. Inverse Distance Weighting (IDW), a GIS technique was used to produce dotted maps showing the spatial distribution of heavy metals in neem bark for better visualization of contamination zones and non-contamination zones. The map reveals few hotspot areas showing areas of high concentrations of the heavy metals investigated which were identified in red colours, the following concentration ranges were obtained;As (4.71 - 6.43 mg/kg), Cd (13.9 - 16.84 mg/kg), Cr (46.3 - 60.84 mg/kg), Hg (3.70 - 5.05 mg/kg) and Pb (24.02 - 31.34 mg/kg). These hotspot areas were found close to business outlets, fuel filling and service stations, farm sites where the application of fertilizers and pesticides were persistent coupled with heavy traffic of vehicles and other machinery which was associated with As, Hg, Cd, Pb and Cr been released into the environment thus, suggesting anthropogenic activities controlling the concentration of these heavy metals in the study areas. The cumulative effect of these heavy metals into the barks of neem could pose as danger, because this plant is used as herbs in folk medicine.

Identification and spatial patterns of coastal water pollution sources based on GIS and chemometric approach

Comprehensive and joint applications of GIS and chemometric approach were applied in identification and spatial patterns of coastal water pollution sources with a large data set （5 years （2000-2004）, 17 parameters） obtained through coastal water monitoring of Southern Water Control Zone in Hong Kong. According to cluster analysis the pollution degree was significantly different between September-next May （the 1st period） and June-August （the 2nd period）. Based on these results, four potential pollution sources, such as organic/eutrophication pollution, natural pollution, mineral/anthropic pollution and fecal pollution were identified by factor analysis/principal component analysis. Then the factor scores of each monitoring site were analyzed using inverse distance weighting method, and the results indicated degree of the influence by various potential pollution sources differed among the monitoring sites. This study indicated that hybrid approach was useful and effective for identification of coastal water pollution source and spatial patterns.

Large-scale deformation monitoring in mining area by D-InSAR and 3D laser scanning technology integration

Large-scale deformation can not be detected by traditional D-InSAR technique because of the limit of its detectable deformation gradient,we propose a method that combines SAR data with point cloud data obtained by 3D laser scanning to improve the gradient of deformation detection.The proposed method takes advantage of high-density of 3D laser scanning point cloud data and its high precision of point positioning after 3D modeling.The specifc process can be described as follows:frst,large-scale deformation points in the interferogram are masked out based on interferometric coherence;second,the interferogram with holes is unwrapped to obtain a deformation map with holes,and last,the holes in the deformation map are flled with point cloud data using inverse distance weighting algorithm,which will achieve seamless connection of monitoring region.We took the embankment dam above working face of a certain mining area in Shandong province as an example to study large-scale deformation in mining area using the proposed method.The results show that the maximum absolute error is 64 mm,relative error of maximum subsidence value is 4.95%,and they are consistent with leveling data of ground observation stations,which confrms the feasibility of this method.The method we presented provides new ways and means for achieving large-scale deformation monitoring by D-InSAR in mining area.

Analysis of Intra-annual Heterogeneous Characteristics of Precipitation in Shaoguan City

Based on the daily precipitation data between 1965 and 2009 from 8 rainfall stations in Shaoguan City,the indexes of precipitation concentration degree（ PCD） and precipitation concentration period（ PCP） were calculated. And then inverse distance weighted（ IDW） interpolation method was used to analyze the heterogeneous distribution characteristics of inter-annual precipitation by introducing the spatial distribution of annual mean values,variable coefficients,correlation coefficients with annual precipitation,change trends and composite analysis. The results showed that PCD was mainly decreasing from southeast to northwest in spatial distribution,long-term average annual values of PCP were distributed in the first ten days of June at most region. Annual precipitation increased as PCD increased in southern region,but the change trend was the opposite in northern region. Annual precipitation increased as PCP lagged in most region. PCD and PCP mainly appeared upward trend. Composite analysis of PCD in more-precipitation years was similar to less-precipitation years in spatial distribution,but the PCD in less-precipitation years was higher.Seen from the mean in the whole region,PCP in more-precipitation years lagged about 20 days behind those in less-precipitation years. The research can provide basis for the production of agriculture and industry as well as disaster prevention and reduction.

Spatial variability of soil chemical properties of Moso bamboo forests of China

This study investigates the spatial variability of soil organic matter(SOM),soil organic carbon(SOC)and pH in the upper 20-cm layer and 20-40 cm layer in Moso bamboo(Phyllostachys pubescens Pradelle)forests using a geostatistics model.Interpolation maps of SOM,SOC,and pH were developed using ordinary kriging(OK)and inverse distance weighted(IDW)methods.The pH,SOC,and SOM of the two soil layers ranged from 4.6 to 4.7,from 1.5 to 2.7 g kg^(-1)and from 20.3 to 22.4 g kg^(-1),respectively.The coefficient of variation for SOM and SOC was 29.9-43.3%while a weak variability was found for pH.Gaussian and exponential models performed well in describing the spatial variability of SOC contents with R^(2)varying from 0.95 to 0.90.The nugget/sill values of pH are less than 25%,which indicates a strong spatial correlation,while the nugget/sill values of SOC and SOM fall under moderate spatial correlation.Interpolation using ordinary kriging and inverse distance weighted methods revealed that the spatial distribution of SOM,SOC,and pH was inconsistent due to external and internal factors across the plots.Regarding the cross-validation results,the ordinary kriging method performed better than inverse distance weighted method for selected soil properties.This study suggests that the spatial variability of soil chemical properties revealed by geostatistics modeling will help decision-makers improve the management of soil properties.

Trends and Geostatistical Interpolation of Spatio-Temporal Variability of Precipitation in Northern Cameroon

This paper examines the spatial and temporal variability of the mean annual precipitation in the Northern Cameroon on the context of climate change during the time period 1950-2013. The study used homogeneous monthly and annual precipitations database of twenty-five stations located in the Northern Cameroon and Southern Chad Republic. Geostatisticals interpolation methods (Kriging and Inverse Distance Weighting method) associated with Digital Elevation Model were used to establish the spatial distribution of annuals precipitations. The non-parametric Mann- Kendall test and Sen’s slope method were performed to determine respectively trend and magnitude. The result indicates a spatial distribution of precipitation mainly determined by the topography and the geography of the study area. The trend analysis shows a decrease of annual average precipitation across the Northern Cameroon at a rate of ?0.568 mm/year over the time period 1950-2013. The magnitude of decreasing trends ranged between 0.11 and 3.92 mm/year. Statistically significant decreasing trends at the 95% level of confidence were noted at 10% while 5% of stations showed statistically significant increasing. However, 60% of stations indicate a decreasing trend. Monthly analysis of rainfall shows a decreasing trend during June and September while July and August present an increasing trend.

Spatio-Temporal Air Pollutant Characterization for Urban Areas

Urban pollution has now become increasingly recognized as an important determinant of air pollution in developed countries. The effect of urban air pollution in developing countries, on the other hand, has not been adequately addressed in the data Spatio-temporal time series. Thus, this study was intended to characterize the effect of urbanization on air pollution for an urbanized Klang Valley, Malaysia using Spatio-temporal data from 2008 to 2017. The Air Pollution Index (API) data and local pollutant concentration were employed to establish the links between urban air pollution. The analysis will be supported by determining the source of pollutants during the study period using Principal Component Analysis (PCA). The study identified that Carbon monoxide (CO), Nitrogen Dioxide (NO2), and Ozone (O3) are the major air pollution that has contributed to degrading air quality in the Klang Valley due to the vehicles, combustion process, and industries.

Mapping of Precipitation, Temperature and Evaporation Distributions in the Porsuk Basin Using Distant Forecasting Methods

Geographical data are of great importance in meteorology and climate science. These data can create the areal distribution models analyzed by spatial interpolation methods. The values of the areas without measurement data are estimated with these distribution models. In this study, distribution of meteorological parameters such as precipitation, temperature and evaporation in Porsuk basin, which is determined as research area, was investigated by Inverse Distance Weighting (IDW) and Ordinary Kriging methods. Actual meteorological data analyzed of the basin do not show a normal distribution statistically. Therefore, the data were firstly subjected to normalization and then analyzed according to the IDW and Ordinary Kriging methods to create distribution maps of precipitation, temperature and evaporation data. Quadratic mean error values were compared to investigate the reliability of analyzes. In this study, the analysis results of precipitation, temperature and evaporation data have been calculated by two different methods. Ordinary Kriging method has been determined as the method making the most accurate estimation.

Soil property mapping by combining spatial distance information into the Soil Land Inference Model(SoLIM)

The Soil Land Inference Model(SoLIM) was primarily proposed by Zhu et al.(Zhu A X, Band L, Vertessy R, Dutton B. 1997. Derivation of soil properties using a soil land inference model(SoLIM). Soil Sci Soc Am J. 61: 523–533.) and was based on the Third Law of Geography. Based on the assumption that the soil property value at a location of interest will be more similar to that of a given soil sample when the environmental condition at the location of interest is more similar to that at the location from which the sample was taken, SoLIM estimates the soil property value of the location of interest using the soil property values of known samples weighted by the similarity between those samples and the location of interest in terms of an attribute domain of environmental conditions. However, the current SoLIM method ignores information about the spatial distances between the location of interest and those of the sample. In this study, we proposed a new method of soil property mapping, So LIM-IDW, which incorporates spatial distance information into the SoLIM method by means of inverse distance weighting(IDW). The proposed method is based on the assumption that the soil property value at a location of interest will be more similar to that of a known sample both when the environmental conditions are more similar and when the distance between the location of interest and the sample location is shorter. Our evaluation experiments on A-horizon soil organic matter mapping in two study areas with independent evaluation samples showed that the proposed SoLIM-IDW method can obtain lower prediction errors than the original SoLIM method, multiple linear regression, geographically weighted regression, and regression-kriging with the same modeling points. Future work mainly includes the determination of optimal power parameter values and the appropriate setting of the parameter under different application contexts.

Soil-Landscape Estimation and Evaluation Program(SLEEP)to predict spatial distribution of soil attributes for environmental modeling

The spatial distribution of surface and subsurface soil attributes is an important input to environmental modeling.Soil attributes represent an important input to the Soil and Water Assessment Tool(SWAT),which influence the accuracy of the modeling outputs.An ArcGIS-based tool was developed to predict soil attributes and provide inputs to SWAT.The essential inputs are digital elevation model and field observations.Legacy soil data/maps can be used to derive observations when recent field surveys are not available.Additional layers,such as satellite images and auxiliary data,improve the prediction accuracy.The model contains a series of steps(menus)to facilitate iterative analysis.The steps are summarized in deriving many terrain attributes to characterize each pixel based on local attributes as well as the characteristics of the contributing area.The model then subdivides the entire watershed into smaller facets(subdivisions of subwatersheds)and classifies these into groups.A linear regression model to predict soil attributes from terrain attributes and auxiliary data are established for each class and implemented to predict soil attributes for each pixel within the class and then merged for the entire watershed or study area.SLEEP(Soil-Landscape Estimation and Evaluation Program)utilizes Pedo-transfer functions to provide the spatial distribution of the necessary unmapped soil data needed for SWAT prediction.An application of the tool demonstrated acceptable accuracy and better spatial distribution of soil attributes compared with two spatial interpolation techniques.The analysis indicated low sensitivity of SWAT prediction to the number of field observations when SLEEP is used to provide the soil layer.This demonstrates the potential of SLEEP to support SWAT modeling where soil data is scarce.

A new grid deformation technology with high quality and robustness based on quaternion

Quality and robustness of grid deformation is of the most importance in the field of aircraft design, and grid in high quality is essential for improving the precision of numerical simulation. In order to maintain the orthogonality of deformed grid, the displacement of grid points is divided into rotational and translational parts in this paper, and inverse distance weighted interpolation is used to transfer the changing location from boundary grid to the spatial grid. Moreover, the deformation of rotational part is implemented in combination with the exponential space mapping that improves the certainty and stability of quaternion interpolation. Furthermore, the new grid deformation technique named ‘‘layering blend deformation＇＇ is built based on the basic quaternion technique, which combines the layering arithmetic with transfinite interpolation（TFI） technique. Then the proposed technique is applied in the movement of airfoil, parametric modeling, and the deformation of complex configuration, in which the robustness of grid quality is tested. The results show that the new method has the capacity to deal with the problems with large deformation, and the ‘‘layering blend deformation＇＇ improves the efficiency and quality of the basic quaternion deformation method significantly.

Comparison and correction of IDW based wind speed interpolation methods in urbanized Shenzhen

Based on the 2-min average wind speed observations at 100 automatic weather stations in Shenzhen from January 2008 to December 2018,this study tries to explore the ways to improve wind interpolation quality over the Shenzhen region.Three IDW based methods,i.e.,traditional inverse distance weight(IDW),modified inverse distance weight(MIDW),and gradient inverse distance weight(GIDW)are used to interpolate the near surface wind field in Shenzhen.In addition,the gradient boosted regression trees(GBRT)model is used to correct the wind interpolation results based on the three IDW based methods.The results show that among the three methods,GIDW has better interpolation effects than the other two in the case of stratified sampling.The MSE and R2 for the GIDW’s in different months are in the range of 1.096-1.605 m/s and 0.340-0.419,respectively.However,in the case of leave-one-group-out crossvalidation,GIDW has no advantage over the other two methods.For the stratified sampling,GBRT effectively corrects the interpolated results by the three IDW based methods.MSE decreases to the range of 0.778-0.923 m/s,and R2 increases to the range of 0.530-0.671.In the nonstation area,the correction effect of GBRT is still robust,even though the elevation frequency distribution of the non-station area is different from that of the stations’area.The correction performance of GBRT mainly comes from its consideration of the nonlinear relationship between wind speed and the elevation,and the combination of historical and current observation data.

Monitoring and early warning system of porcine pasteurellosis based on WebGIS

The incidence of porcine pasteurellosis in China is so widespread that it is difficult to clearly understand the prevalence and maintain continuous monitoring.In order to reduce immense negative economic impact on the livestock industry;monitoring,early warning,and visual management systems are highly desirable.In this study,a monitoring and early warning systemfor porcine pasteurellosis was established based onWeb Geographical Information System(WebGIS)technology.By establishing a path analysis function,buffer analysis function,and hot spot analysis function,it can provide a method of support and control of infectious diseases.For early warning of disease,four common interpolation methods were tested,all of which showed that the affected area of porcine pasteurellosis in China was mainly concentrated in the south of the mainland.A cross-validation method was used to compare the four interpolation methods.The cross-validation showed that the inverse distance weighting(IDW)method was suitable for forecasting the occurrence of porcine pasteurellosis in China.Finally,using C sharp(C#)as the development language and WebGIS technology,a monitoring and early warning system based on Browser/Server structure was developed.This is the first monitoring and early warning system of porcine pasteurellosis based onWebGIS.The performance of theWebGIS technology indicated a great potential for animal infectious disease applications and provided a foundation for future work.