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Spatial interpolation is a required method to generate a continuous surface such as Digital Elevation Model (DEM) because field investigation for most of the surface’s part is time-consuming with a high demand in both human resources and monetory cost. One of the most used deterministic interpolation models is Inverse Distance Weighting (IDW) model. The model takes several neighbors’ information, and the weights are constructed based on the distance between the interpolated point and the neighbors’ points. From the machine learning model, Artificial Neural Networks (ANNs) model has also been used for spatial interpolation. The input of ANNs model is also one of the parameters that need to be defined when building the model. This paper evaluated the effect of the number of inputs (neighbors) on the elevation interpolation accuracy. We applied IDW and ANNs to interpolate the elevation of Balikpapan City, Indonesia. The results show that the accuracy increases significantly when the number of inputs is between one and three. However, after three inputs, additional input would not change the accuracy significantly. ANNs performed better than IDW. For three or more inputs, the MAE of ANNs and IDW interpolations are below 1.1 and around 2 meters, respectively.

This paper introduces a pose error compensation method performed in joint space to improve both position and orientation accuracy of industrial robots. Joint space division is proposed to achieve dimension reduction to reduce the workload of pose error sampling. The six-dimensional joint space is divided into two three-dimensional subspaces by the wrist center. Each subspace is discretized by a sequence of grid elements of which grid vertices are considered as sample points. Based on point set registration, two spatial databases of pose errors are built in the two subspaces respectively using a laser tracker. For a given robot posture, the pose errors generated in the two subspaces can be separately estimated using spatial interpolation based on inverse distance weighting algorithm. Formed by the estimated values, two transformation error matrices are introduced to compensate for the pose errors of the two subspaces. A Comau NJ370–3.0 manipulator was employed as an experimental platform to evaluate the effectiveness of the proposed method. Experimental results demonstrated the maximum position error was reduced by 96.06% to below 0.334 mm, and the maximum orientation errors have decreased to below 0.027° after compensation was performed.

© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.Meteorological drought analyses were conducted for Eastern Mediterranean, Seyhan, Ceyhan, and Asi Basins located in the south of Turkey. The gauged data of total precipitation, average temperature, relative humidity, number of sunny days, and number of overcast days, all in monthly steps, obtained from 35 meteorological stations were used as drought indicators. Aggregate drought index (ADI) was used as drought index calculator. Meteorological data series were arranged in accordance with the hydrological years starting on October 1st and ending on September 30th. The ADI results were organized in order to see the variation of the ADI values of the stations from year to year. ADI thresholds were determined individually for all stations. The ADI results obtained by using the principal component analysis (PCA) were presented in form of drought maps by using the inverse distance weighting (IDW) method. The regional drought frequency analysis by the L-moments method was performed to determine extreme ADI values corresponding to generally referred to specific average return periods. Using both the growth curves yielded by the most suitable probability distributions and the regression equations for the means, the extreme ADI values against these average return periods were computed and presented in tabular forms. According to the outcome of the ADI results, it can be concluded that a severe drought does not prevail over the study area as a whole, but some stations experience extreme droughts locally from time to time because of excessive irrigation water demand.

Virtual rotating array (VRA) beamforming is a robust technique in the identification of rotating sound sources in frequency-domain. Under normal circumstances, the configuration of microphone array is established in ring geometry centred around the rotating axis. Two interpolation methods for arbitrary microphone configurations are proposed by Jekosch and Sarradj (Acoustics, 2020). One is to construct a mesh between all stationary microphones using Delaunay-triangulation, another one is a meshless technique based on radial basis function. However, whether other spatial interpolation methods are available in VRA beamforming with arbitrary microphone configurations is still unclear. This paper adds several new spatial interpolation methods in VRA beamforming and detailedly compares the performances of these interpolation methods in simulations. The simulating results demonstrated that all these interpolation methods are successfully applied in VRA beamforming with arbitrary microphone configurations. Inverse distance weighting interpolation method owns the best performance in rotating sound source localization. In addition, all these interpolation methods have poor spectrum construction capability and sound source strength precision.

Unbalanced regional development is an inevitable trend in the development of all countries in the world. The rapid development of the Internet of Things (IoT) technology has created tools for the study of regional development issues. IoT has many advantages and thus owns a very wide range of applications. This paper makes use of geographic information system (GIS) technology, which can be viewed as one of the IoT sensing information. Changes in spatial regional economic differences and space and the evolution of the structure are particularly examined by processing spatial information such as maps, analyzing phenomena and events that exist on the earth, and exploiting Kriging and inverse distance weighting (IDW). The numerical results in this paper justify that the introduction of GIS technology to the study of economic diversity can upgrade regional economic research from a traditional qualitative and statistical level to a quantitative and spatial visualization level.

In many GIS applications, it is necessary to identify the corresponding objects in the datasets with different sources, scales, and precisions. In this respect, similarity measures used to identify corresponding objects in the datasets with a high percentage of ambiguity might have low efficiency. Therefore, in this paper, we have tried to improve the precision of matching problems in complex conditions by presenting a polygon objects matching method based on the geographic context similarity. The proposed geographic context measure investigates the spatial relationships of the distance and the orientation of each object with the landmarks. Furthermore, instead of using all the identified landmarks for each object, only effective landmarks have been used while weights are assigned to them using the Inverse Distance Weighting (IDW) method. Implementation of the proposed method on three separate datasets shows the high efficiency of the proposed method in different spatial conditions. Finally, comparing the proposed method with the latest methods in the field of the study indicates an improvement according to the $${F}_{\mathrm{Score}}$$ criterion. The results reveal that the matching precision has an average improvement of 10.43% on the studied datasets.

Initial assessment of landslide susceptible areas is important in designing landslide mitigation measures. This study, a part of our study on the developing a landslide spatial model, aims to identify landslide susceptible areas using hard soil depth. In here, hard soil depth, defined as the depth interpreted from cone penetration test where the tip resistance reaches up to 250 kg/cm2, was used to identify landslide susceptible areas in a relatively small mountainous region in the middle western Central Java where landslides frequently occur. To this end, hard soil depth was interpolated using two different methods: inverse distance weighting and ordinary kriging (OK). The method producing the least errors and the most similar data distribution was selected. The result shows that OK is the best fitting model and exhibits clear pattern related to the recorded landslide sites. From interpolated hard soil depth in the landslide sites, it can be surmised that landslide susceptible areas are places possessing hard soil depth of 2.6–13.4 m. This finding is advantageous for policy makers in planning and designing efforts for landslide mitigation in middle western Central Java and should be applicable for other regions.

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, SoLIM-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.

The knowledge about spatial variation of annual rainfall is important for many applications ranging from agriculture planning to flood risk management in a tropical low-lying country like Bangladesh. The remotely sensed data has emerged as a suitable addition to the data source which is often suggested for use at ungauged conditions. This study investigates whether the remotely sensed outputs on its own or its incorporation as a covariate can outperform the mapping estimate of annual average rainfall. The work primarily considers a multivariate kriging approach, kriging with external drift (KED), which can take covariates to good effect for the spatial interpolation. Other than remotely sensed annual average rainfall (RAAR), the study includes easily accessible: geographical coordinates (LON, LAT) and elevation as potential covariates. The suitability of the KED model is assessed against the widely used classical univariate, ordinary kriging (OK), and the inverse distance weighting (IDW) methods. The annual average rainfall calculated at 34 stations based on observed daily rainfall data from 1970 to 2016 was used for the assessment. Based on cross-validation techniques, the KED with LON is identified as the best interpolation method. The IDW performed poorly and came last among all the interpolation methods. The performance of remotely sensed outputs on its own is not as good as the interpolation estimate; in fact, it is outperformed by the IDW quite convincingly. The integration of RAAR as a covariate with the KED performed superior to IDW but could not outperform the chosen KED (LON) model. Overall, remotely sensed data could be served better with the integration of an appropriate kriging approach rather than to be used as model outputs.

With the enrichment of land subsidence monitoring means, data fusion of multisource land subsidence data has gradually become a research hotspot. The Interferometry Synthetic Aperture Radar (InSAR) is a potential Earth observation approach, and it has been verified to have a variety of applications in measuring ground movement, urban subsidence, and landslides but similar to Global Positioning System (GPS). The InSAR observation accuracy and measurements are affected by the tropospheric delay error as well as by the Earth’s ionospheric and tropospheric layers. In order to rectify the InSAR result, there is a need to interpolate the GPS-derived tropospheric delay. Keeping in view of the above, this research study has presented an improved Inverse Distance Weighting (IIDW) interpolation method based on Inverse Distance Weighting (IDW) interpolation by using Sentinel-1 radar satellite image provided by European Space Agency (ESA) and the measured data from the Continuously Operating Reference Stations (CORS) provided by the Survey and Mapping Office of the Lands Department of Hong Kong. Furthermore, the corrected differential tropospheric delay correction is used to correct the InSAR image. The experimental results show that the correction of tropospheric delay by IIDW interpolation not only improves the accuracy of Differential Interferometry Synthetic Aperture Radar (D-InSAR) but also provides a new idea for the solution of InSAR and GPS data fusion.

Drought is considered to be one of the most devastating natural disasters. In recent years, determination of historical droughts has gained more importance. This can be attributed to the fact that once the trend of historical droughts is determined, it should be possible to struggle against drought more effectively. In this study, the drought analysis is performed in the Southeastern Anatolia Project (GAP) region, which is Turkey's biggest integrated project, using the monthly total precipitation data from 15 stations in nine provinces. Standardized precipitation index (SPI), being one of the most frequently used methods in the literature, is used to determine the drought indices. Temporal drought and occurrence of drought are calculated for 3, 6 and 12 month time scales. The non-parametric Mann–Kendall and Mann–Kendall Rank Correlation tests are used to determine the monotonic trends of drought indices and its year of initiation. The “Pre-whitened” method is used to remove serial correlation from time series before analysis. Linear slope of the trend is determined by Sen's Slope method, and Inverse Distance Weighting method is used for the spatial analysis. According to trends of temporal drought of the GAP region, a decreasing trend is found in 30% of the stations for 12-month SPI values with a statistical significance. Based on the results obtained from spatial analysis, there is a decreasing trend in most of the region for almost all time scales, and the slopes of trend are relatively higher in the north and south of the region.

Precise adjustments of farm management activities, such as irrigation and soil treatment according to site-specific conditions, are crucial. With advances in smart agriculture and sensors, it is possible to reduce the cost of water and soil treatment inputs but still realize optimal yields and high-profit returns. However, achieving precise application requirements cannot be efficiently practiced with spatially disjointed information. This study assessed the potential of using an electromagnetic induction device (EM38-MK) to cover this gap. An EM38-MK was used to measure soil apparent electrical conductivity (ECa) as a covariate to determine soil salinity status and soil water content θ post irrigation at four depth layers (Hz: 0–0.25 m; Hz: 0–0.75 m; Vz: 0.50–1 m). The inverse distance weighting method was used to generate the spatial distribution thematic layers of electrical conductivity. The statistical measures showed an R2 = 0.87; r > 0.7 and p ≤ 0.05 on correlation of ECa and SWC. Based on the South African salinity class of soils, the area was not saline ECa < 200 mS/m. The EM38-MK can be used to estimate soil salinity and SWC variability using ECa as a proxy, allowing precise estimations with depths and in space. These findings provide key information that can aid in irrigation scheduling and soil management.

Rainfall dynamics play a vital role in tropical peatland by providing sufficient water to keep peat moist throughout the year. Therefore, information of rainfall data either historical or forecasting data has risen in recent decades especially for an alert system of fire. Here the Weather and Research Forecasting (WRF) model may act as a tool to provide forecasting weather data. This study aims to do parameterization on WRF parameters for peatland in Sumatra, and to perform bias correction on the WRF’s rainfall output with observed data. We performed stepwise calibration to choose the best five physical schemes of WRF for use in the study area. The output WRF’s rainfall was bias corrected by spatially observed rainfall data for 2019 at day resolution. Our results showed the following schemes namely (i) Eta scheme for cloud microphysical parameters; (ii) GD scheme for cumulus cloud parameters, (iii) MYJ scheme for planetary boundary layer parameters; (iv) RRTM for longwave radiation; and (v) New Goddard schemes for shortwave radiation are best combination for being used to predict rainfall in maritime continent. The spatially interpolated observed rainfall with the Inverse Distance Weighting (IDW) was outperformed for calibration process of WRF’s rainfall as shown by statistical indicators used in this study. Further, the findings have contributed to advance knowledge of rainfall forecasting in maritime continent, particularly in providing data to support the development of fire danger rating system for Indonesian peatland.

Probable maximum precipitation (PMP) is considered as a principal factor for estimating probable maximum floods (PMF) when designing various hydraulic structures. The estimation of PMP by the Hershfield statistical method was adopted in this paper for 112 rainfall stations in the three Algerian coastal basins (coastal region). This method is based on the adequate estimation of the frequency factor ( $${K}_{\mathrm{m}}$$ ) using some envelope curves. Several researchers have used different types of envelope curves in their works such as the linear, exponential, and polynomial type. In this work, a new linear envelope curve was introduced and adopted. This paper will perform a comparison between linear, exponential, second degree polynomial, composite and proposed linear envelope curves. On the basis of the coefficient of determination (R2) the proposed linear envelope curve was chosen to obtain reliable PMP values, thus avoiding underestimation and overestimation. The obtained PMP values vary from a minimum of 152.21 mm (station 109, BV04) to a maximum of 436.15 mm (station 47, BV02) with a mean value of 253. 32 mm. To comprehend the PMP spatial distribution in Algerian coastal region, the Inverse Distance Weighting (IDW) method was used with ArcGIS 10.5 software from the Environmental Systems Research Institute (ESRI).

Prediction methods are important for many applications. In particular, an accurate prediction for the total number of cases for pandemics such as the Covid-19 pandemic could help medical preparedness by providing in time a sucient supply of testing kits, hospital beds and medical personnel. This paper experimentally compares the accuracy of ten prediction methods for the cumulative number of Covid- 19 pandemic cases. These ten methods include three types of neural networks and extrapola- tion methods based on best fit quadratic, best fit cubic and Lagrange interpolation, as well as an extrapolation method proposed by the second author. We also consider the Kriging and inverse distance weighting spatial interpolation methods. We also develop a novel spatiotemporal prediction method by combining temporal and spatial prediction methods. The experiments show that among these ten prediction methods, the spatiotemporal method has the smallest root mean square error and mean absolute error on Covid-19 cumulative data for counties in New York State between May and July, 2020. © This article is published under the terms of the Creative Commons Attribution License 4.0 https://creativecommons.org/licenses/by/4.0/deed.en_US

Background Emergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors. Methods Data on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016–2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital’s catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit. Results The number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE’s were 8.7%). Conclusions Because of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.

Ordinary Least Square (OLS) is general method to estimate Generalized Space Time Autoregressive (GSTAR) parameters. Parameter estimation by using OLS for GSTAR model with correlated residuals between equations will produce inefficient estimators. The method that appropriate to estimate the parameter model with correlated residuals between equations is Generalized Least Square (GLS), which is usually used in Seemingly Unrelated Regression (SUR). This research aims to build the seasonal GSTAR SUR model as model of rice yield forecasting in three locations by using the best weighting. Weights used are binary weights, inverse distance and normalization of cross correlation. Data which used in this research are the data of rice yield per quarter in three districts in Central Java, namely Banyumas, Cilacap and Kebumen. The data from the period of January 1981 to December 2014 as training data and the period of January 2015 to December 2018 as validation data. The resulting is a model that has a seasonal effect with the autoregressive order and the spasial order limited to 1 so the model formed is SGSTAR (4 1 )-I(1)(1) 3 . The best model produced is the SGSTAR SUR (4 1 )-I(1)(1) 3 model with inverse distance weighting because it fulfills both assumptions, residuals white noise and residuals normally multivariate distribution. Additionally, it has the smallest MAPE value when compared the other weighting, that is 20%. This MAPE value indicates that the accuracy rate of forecast is accurate. Keywords : Rice yield, Seasonal, GSTAR, SUR.

Mapping soil organic carbon (SOC) and its uncertainty are essential for agricultural soil management. The current study was carried out to quantify and map the spatial variability of SOC in an Agro-Ecosystem region (~ 170 km2) in Northeast Iran using Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), and Inverse Distance Weighting (IDW) techniques. In the study area, a total of 288 soil surface samples (0–20 cm depth) were collected. Results showed that the mean SOC was 0.728% with high variability (CV = 44.78%), and also SOC was found to be deviant from a normal distribution as revealed by Kolmogorov–Smirnov (K-S) test, with positive skewness. Due to the deviation from the normal distribution, and for the purpose of modeling, the data were log-transformed to approximate a normal distribution. The best fit empirical variogram was the Exponential model. Among the various interpolation techniques, estimation with EBK approach was the fairly reliable (RMSE = 0.3156; ME = 0.004), followed by OK (RMSE = 0.3162; ME = 0.003), and IDW (RMSE = 0.3199; ME = 0.005). Moreover, results showed that the spatial analysis of SOC had a strong spatial dependency for this study. Thus, the results revealed that the simple interpolation approach was a fast method and highly suitable for the spatial prediction of SOC in this study.

The relationship between precipitation and elevation is a well-known topic in the field of geography and meteorology. Radar-based precipitation data are often used in hydrologic models, however, they have several inaccuracies, and elevation can be one of the additional parameters that may help to improve them. Thus, our aim in this article is to find a quantitative relationship between precipitation and elevation in order to correct precipitation data input into hydrologic models. It is generally accepted that precipitation increases with elevation, however, the real situation is much more complicated, and besides elevation, the precipitation is dependent on several other topographic factors (e.g., slope, aspect) and many other climatic parameters, and it is not easy to establish statistically reliable correlations between precipitation and elevation. In this paper, we examine precipitation-elevation correlations by using multiple regression analysis based on monthly climatic data. Further on, we present a method, in which these regression equations are combined with kriging or inverse distance weighting (IDW) interpolation to calculate precipitation fields, which take into account topographic elevations based on digital terrain models. Thereafter, the results of the different interpolation methods are statistically compared. Our study areas are in the hilly or low mountainous regions of Hungary (Bakony, Mecsek, Börzsöny, Cserhát, Mátra and Bükk montains) with a total of 52 meteorological stations. Our analysis proved that there is a linear relationship between the monthly sum of precipitation and elevation. For the North Hungarian Mountains, the correlation coefficients were statistically significant for the whole study period with values between 0.3 and 0.5. Multivariate regression analysis pointed out that there are remarkable differences among seasons and even months. The best correlation coefficients are typical of late spring-early summer and October, while the weakest linear relationships are valid for the winter period and August. The vertical gradient of precipitation is between one and four millimetres per 100 metres for each month. The statistical comparison of the precipitation interpolation had the following results: for most months, co-kriging was the best method, and the combined method using topography-derived regression parameters lead to only slightly better results than the standard kriging or IDW.

Accurate road marking extraction and classification play a significant role in the development of autonomous vehicles (AVs) and high-definition (HD) maps. Due to point density and intensity variations from mobile laser scanning (MLS) systems, most of the existing thresholding-based extraction methods and rule-based classification methods cannot deliver high efficiency and remarkable robustness. To address this, we propose a capsule-based deep learning framework for road marking extraction and classification from massive and unordered MLS point clouds. This framework mainly contains three modules. Module I is first implemented to segment road surfaces from 3D MLS point clouds, followed by an inverse distance weighting (IDW) interpolation method for 2D georeferenced image generation. Then, in Module II, a U-shaped capsule-based network is constructed to extract road markings based on the convolutional and deconvolutional capsule operations. Finally, a hybrid capsule-based network is developed to classify different types of road markings by using a revised dynamic routing algorithm and large-margin Softmax loss function. A road marking dataset containing both 3D point clouds and manually labeled reference data is built from three types of road scenes, including urban roads, highways, and underground garages. The proposed networks were accordingly evaluated by estimating robustness and efficiency using this dataset. Quantitative evaluations indicate the proposed extraction method can deliver 94.11% in precision, 90.52% in recall, and 92.43% in F1-score, respectively, while the classification network achieves an average of 3.42% misclassification rate in different road scenes.

To study the spatial distribution of soil salinization, the temporal and spatial variations of groundwater depth, along the Yellow River saline–alkali land, were analyzed. GIS technology was used to conduct spatial interpolation and quantitative analysis in Shahao, including salinization data obtained before summer irrigation, and groundwater depth data for one hydrological year from long-term observation wells. The results indicate that the groundwater depth gradually shallows from south to north, consistent with the topography of the area, and soil salinization is greater in the central and northern parts of the study area. Alkaline soils are widespread in the surface-to-middle soil, accounting for 55.2–62.5% of the total land area. A total of 28.6% of the surface layer is heavily salinized. The crop emergence rate of heavily salinized cropland is only 30–50%. The high salt content of the Yellow River itself, freeze–thaw change and poor drainage are three important factors causing the high soil salinity along the Yellow River saline–alkali land. The changes of groundwater depth are divided into autumn irrigation, the freezing period and the crop-growth period. The return of soil salt in spring caused by autumn irrigation mainly affects the uppermost soil layer (0–100 mm depth).

This paper describes an efficient implementation of an image interpolation algorithm based on inverse distance weighting (IDW). The time-consuming search for support pixels bordering the voids to be filled is facilitated through gapless sweeps of different directions over the image. The scanlines needed for the sweeps are constructed from a path prototype per orientation whose regular substructures get reused and shifted to produce aligned duplicates covering the entire input bitmap. The line set is followed concurrently to detect existing samples around nodata patches and compute the distance to the pixels to be newly set. Since the algorithm relies on integer line rasterization only and does not need auxiliary data structures beyond the output image and weight aggregation bitmap for intensity normalization, it will run on multi-core central and graphics processing units (CPUs and GPUs). Also, occluded support pixels of non-convex void patches are ignored, and over- or undersampling close-by and distant valid neighbors is compensated. Runtime and accuracy compared to generated IDW ground truth get evaluated for the CPU and GPU implementation of the algorithm on single-channel and multispectral bitmaps of various filling degrees.

This study included the testing of normal (Gaussian) distribution of input data and, consequently, spatially interpolating maps of chemical components and cement modules in the flysch. This deposit contains the raw material for cement production. The researched area is located in southern Croatia, near Split, as part of the exploited field “St. Juraj–St. Kajo”. There are six lithological units: (1) alternation of marls and sandstones with inclusions of conglomerates, (2) marl, (3) calcsiltite, (4) calcarenite, (5) marl with nummulites, (6) debrites, and (7) clayey marl. All of them are deposited in the (a) northern and (b) southern beds. Only debrites are divided into the (a) western and (b) eastern layers. Those lithological units were divided technologically based on their cement modules (lime saturation factor (LSF), silicate module (SM), and aluminate module (AM)). The average thicknesses were analysed, followed by normality tests (Kolmogorov–Smirnov (K–S) and Shapiro–Wilk (S–W)) of the chemical analyses: CaO, SiO2, Al2O3, Fe2O3, MgO, SO3, Na2O, K2O, CaCO3 (%) and three cement modules (LSF, SM, AM), available in the six lithological units. The normality tests were applied based on a number of input data. The further interpolation was performed using two methods, kriging and inverse distance weighting, mapping CaO (%), SiO2 (%), and LSF (−) in three different lithological units. The interpolation methods were selected based on two criteria: (a) normality test pass or fail and (b) the amount of data. In total, 144 tests were calculated, including sets from 7 to 36 points. The results show the current situation in the quarry, after decades of production, making reliable the future predictions of cement raw material exploitation.

The land use land cover changes (LULCC) in and around wildlife habitat are one of the major driving forces behind frequent human elephant conflict (HEC). The present study aims to determine the spatiotemporal LULC changes and its impact on Human – Elephant conflict in and adjoining Buxa Tiger Reserve (BTR). Landsat TM and OLI images of 1990, 2000, 2010, and 2019 were used and supervised classification using maximum likelihood classification algorithm applied to classify the LULC attributes. Each sign of elephant presence was recorded using GPS used for corridor mapping. The recorded sign of conflict was collected, used for settlement based human elephant conflict probability zone identification mapping by areal interpolation method. We found that (1990 - 2019) dense forest, surface water bodies, scrub land and agricultural fallow land decreased by 184.04 sq km, 21.94 sq km, 142.48 sq km, and 130.21 sq km respectively. On the other hand crop land, sparse vegetation, tea garden, open forest, degraded forest and built up areas increased by 119.74 sq km, 90.96 sq km, 87.05 sq km, 57.02 sq km, 19.07 sq km and 32.21 sq km respectively. The visual overlay method was performed between LULC map, and settlement based HEC probability map, therefore impact of spatiotemporal LULC change on HEC was determined. It finds out moderate to very high HE conflict settlement zone lies where nearby forest tract either degraded or large areas of open forest, scrub land replaced into crop fields and settlements. In addition, elephant sign along the broad gauge rail line were collected on foot, recorded using GPS used to prepared accident prone susceptibility map by Inverse Distance Weighting (IDW) interpolation technique to estimate where accidents were more likely to occur due to the concentration of elephant activity. It finds out high to very high accident prone susceptible zone lies in between Rajabhatkhawa station (st) – Damanpur st, Gorapara st- Rarabhatkhawa st, and railway line within Hamiltonganj range. The study wants to draw attention to the concerned authority and policy makers on that there is a need of long term land use planning to save BTR forest and its biodiversity as well as need for proper mitigate method for HE conflict for peaceful coexistences of both the species.

The spatial interpolation of meteorological data have important applications in ecological environment monitoring, such as soil erosion, ecological vulnerability evaluation. However, there are significant differences in the interpolation accuracy of climatic elements under different topographic and geomorphic conditions. Based on data of 810 meteorological stations across the country, five typical interpolation methods, namely ordinary Kriging method, inverse distance weight method, spline function method, natural neighborhood method and trend surface method, were utilized in this paper to analyze and compare the interpolation accuracy of five climate factors, namely temperature, precipitation, accumulated temperature(>10°),wind speed and sunshine hours, under different topographic and geomorphological conditions. The results showed that: (1) Ordinary kriging method of temperature had better applicability in plain, hill and medium-large undulating mountain areas for temperature while the inverse distance weight method and spline function method had higher interpolation accuracy in the platform area and the small undulating mountain area, respectively. (2) The optimal interpolation of the precipitation in plain, platform and medium-large undulating mountain areas was ordinary kriging method while the inverse distance weight method and spline function method had better applicability in small undulating mountain area. (3) For accumulated temperature (>10 °C), the spline function method had higher interpolation accuracy in plain and platform areas, while ordinary kriging method had better applicability in hilly, small and medium-large undulating mountain area al (4) The optimal spatial interpolation of wind speed in plain and hilly areas was the inverse distance weight method. The natural neighborhood method and the spline function method had the best applicability in plateau, medium-large undulating mountainous areas and small undulating mountain areas, respectively. (5) For sunshine hours, the optimal spatial interpolations in plain and hilly areas were natural neighborhood method and spline function method, respectively, while the ordinary kriging method and the inverse distance weighting method had better applicability in platform, large undulating mountainous areas and small undulating mountainous areas, respectively .

Harvester-mounted yield monitor systems are increasingly used to document corn (Zea mays L.) yield. The three most commonly used spatial estimation methods to convert point data gathered with yield monitors to regular, grid-based, raster maps include nearest neighbor (NN), inverse distance weighting (IDW) and kriging. Seven spatial estimation methods (NN, IDW using 10, 20, 30 and all data points and kriging with exponential and Matern covariance functions) were evaluated to determine the method that most accurately captures intra-field spatial variability of corn silage and corn grain yield in New York. Yield monitor data from two dairy farms and two grain operations were cleaned using Yield Editor prior to spatial analyses. The dataset included 7–10 years of data per farm for a combined 7484 ha (245 fields) of silage and 6971 ha (253 fields) of grain. Data were split into training (80%) and cross-validation datasets (remaining 20% of the data). Normalized root mean squared error (NRMSE) was used to evaluate the accuracy of the spatial estimation methods. Kriging with the Matern covariance function resulted in the most accurate corn silage and grain yield raster maps both at the farm and field level. There were statistically significant differences in NRMSE between kriging with the Matern isotropic covariance function and all other models for both corn silage and grain, regardless of field size, year when data were obtained or farm that supplied the data. These results are beneficial to ensure accurate and precise spatial mapping of yield products toward optimized corn growth management.

Soil thickness determines the soil productivity in the black soil region of northeast China, which is important for national food security. Existing information on the spatial variation of black soil thickness is inadequate. In this paper, we propose a model framework for spatial estimation of the black soil thickness at the watershed scale by integrating field observations, unmanned aerial vehicle variations of topography, and satellite variations of vegetation with the aid of random forest. We sampled 141 sample profiles over a watershed and identified the black soil thickness based on indices of the mollic epipedon. Topographic variables were derived from a digital elevation model and vegetation variables were derived from Landsat 8 imagery. Random forest was used to determine the relationship between black soil thickness and environmental variables. The resulting model explained 61% of the black soil thickness spatial variation, which was more than twice that of traditional interpolation methods (ordinary kriging, universal kriging and inverse distance weighting). Topographic variables contributed the most toward explaining the thickness, followed by vegetation indices. The black soil thickness over the watershed had a clear catenary soil pattern, with thickest black soil in the low depositional areas and thinnest at the higher elevations that drain into the low areas. The proposed model framework will improve estimates of soil thickness in the region of our study.

The Lago Maggiore catchment is characterized by medium to high altitude (up to 4633 m a.s.l. with a median of 1270 m a.s.l.), high precipitation (~ 1700 mm/yr), and brittle tectonic deformation of impermeable rocks, such as granite and gneiss, that are characterized by a predisposition to slope failure. We analysed daily rainfall data associated with 38 landslides that occurred between 1980 and 2017 from meteorological stations placed into four sub-basins. The purpose was to determine whether or not extreme rainfall events exceeded landslides thresholds reported by previous studies. A statistical analysis using the RClimDex package was done, to verify changes in extreme rainfall over time. A spatial approach using Inverse Distance Weighting (IDW) in QGIS was used to extrapolate rainfall data specific to landslide areas, as well as GIS techniques and processing tools to conduct geomorphic analyses. Finally, a multivariate analysis, (general linear model), was used to understand associations between variables (landslide types, lithology, valley, elevation, slope, land use, rainfall, and the presence of rivers, roads, paths, and buildings), known to affect the generation of landslides. Results show extreme rainfall events to be a secondary factor in the triggering of landslides, whereas the most significant factors are presence of building, proximity to rivers and lithology. It was found that intense rainfall is a concomitant cause to landslides in some instances but does not play a role in others.

The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world including Bangladesh. We conducted this study to assess how COVID-19 cases clustered across districts in Bangladesh and whether the pattern and duration of clusters changed following the country's containment strategy using Geographic information system (GIS) software. We calculated the epidemiological measures including incidence, case fatality rate (CFR) and spatiotemporal pattern of COVID-19. We used inverse distance weighting (IDW), Geographically weighted regression (GWR), Moran's I and Getis-Ord Gi* statistics for prediction, spatial autocorrelation and hotspot identification. We used retrospective space-time scan statistic to analyse clusters of COVID-19 cases. COVID-19 has a CFR of 1.4%. Over 50% of cases were reported among young adults (21-40 years age). The incidence varies from 0.03 - 0.95 at the end of March to 15.59-308.62 per 100,000, at the end of July. Global Moran's Index indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran's I analysis stated a distinct High-High (HH) clustering of COVID-19 cases among Dhaka, Gazipur and Narayanganj districts. Twelve statistically significant high rated clusters were identified by space-time scan statistics using a discrete Poisson model. IDW predicted the cases at the undetermined area, and GWR showed a strong relationship between population density and case frequency, which was further established with Moran's I (0.734; p ≤ 0.01). Dhaka and its surrounding six districts were identified as the significant hotspot whereas Chattogram was an extended infected area, indicating the gradual spread of the virus to peripheral districts. This study provides novel insights into the geostatistical analysis of COVID-19 clusters and hotspots that might assist the policy planner to predict the spatiotemporal transmission dynamics and formulate imperative control strategies of SARS-CoV-2 in Bangladesh. The geospatial modeling tools can be used to prevent and control future epidemics and pandemics.

This study addressed the problems of difficult parameter selection of ground point filtering algorithm, slow speed, and susceptibility to wrong classification points of the traditional interpolation algorithm in extracting canopy height model from dense point cloud produced by structure from motion. Finding the true ground is vital for canopy height estimation using unmanned aerial vehicle red, green, and blue point cloud, especially for semiarid area where cloud points of short vegetation like shrubs and dwarf trees generally mixed up with the ground points. This article proposes a ground point extraction strategy that combines the advantages of structural and spectral filtering. Specifically, spectral filtering simplifies the threshold selection for structural filtering, whereas structural filtering removes the outliers from spectral filtering. Considering the misclassified points in the ground filtering algorithm, a fast, nonparametric ground point interpolation algorithm was used to suppress the wrong classification points. This novel algorithm is based on the basic idea of least square quadratic fitting and prediction of terrain profile (PF). The quantitative results show that compared to inverse distance weighting (IDW), radial basis function (RBF), and ordinary kriging (OK), it takes less time (PF: 122 s, IDW: 518 s, RBF: 1374 s, OK: 1129 s), and has lower RMSE (PF: 0.301 m, IDW: 0.549 m, RBF: 0.903 m, OK: 0.427 m).

With the industrial revolution, environmental problems have diversified and have increased significantly, affecting both the natural and the human environment. Air pollution, which is among the most important of these environmental problems, has been a serious problem today as it was in the past. In this study, which examines the human factors affecting air quality and pollution between 2007-2019 in the provinces of Southeast Anatolia Region, pollution shows continuity especially in certain provinces. Depending on this continuity, our aim is to detect air pollution in the provinces of the region and offer solutions to them. IDW (Inverse Distance Weighting) analysis was used through Geographic Information Systems to detect the spatial change of air pollution. With this analysis method, interpolation was provided between the measurements. The provinces with the highest SO2 concentration are Şanlıurfa and Siirt in 2007, Şırnak in 2012, Şanlıurfa and Siirt in 2017 and Şanlıurfa in 2019. PM10 annual average concentration is high in Batman in 2007, Gaziantep, Batman and Siirt in 2012, Şanlıurfa in 2017 and Şırnak in 2019. The number of population living in the city, the type and quality of fuel used in residences and industry, the number of industrial enterprises and the number of vehicles are among the important human factors affecting pollution. In this sense, local and national administrations should take serious measures in the air pollution that causes human health to deteriorate or even die., Sanayi devrimiyle birlikte çevre sorunları çeşitlenmiş ve önemli ölçüde artış göstererek hem doğal hem de beşerî çevreyi etkilemiştir. Bu çevre sorunlarının en önemlileri arasında yer alan hava kirliliği geçmişte olduğu gibi günümüzde de ciddi bir sorun olmuştur. Güneydoğu Anadolu Bölgesi illerinde 2007-2019 yılları arasında hava kalitesi ve kirliliğine etki eden beşeri unsurların incelendiği bu çalışmada, kirlilik özellikle belirli illerde süreklilik göstermektedir. Bu sürekliliğe bağlı olarak amacımız bölge illerindeki hava kirliliğini tespit etmek ve bunlara çözüm önerisi sunabilmektir. Hava kirliliğinin mekânsal değişimini tespit etmek için Coğrafi Bilgi Sistemleri aracılığıyla IDW (Inverse Distance Weighting) analizi kullanılmıştır. Bu analiz yöntemiyle ölçümler arasında enterpolasyon sağlanmıştır. SO2 konsantrasyonun en fazla olduğu iller 2007’de Şanlıurfa ve Siirt, 2012’de Şırnak, 2017’de Şanlıurfa ve Siirt ve 2019’da da Şanlıurfa’dır. PM10’de ise 2007’de Batman, 2012’de Gaziantep, Batman ve Siirt, 2017’de Şanlıurfa ve 2019’da da Şırnak’ta yıllık ortalama konsantrasyon fazladır. Kirliliğin artmasında en önemli beşeri unsurlar; şehirde yaşayan nüfus miktarı, konutlarda ve sanayide kullanılan yakıt türü ve kalitesi, sanayiye bağlı işletme sayısı ve araç sayısıdır. Bu manada insan sağlığının bozulmasına hatta ölümlere neden olan hava kirliliğinde yerel ve ulusal yönetimlerin ciddi önlem alması gerekmektedir.

Soil organic carbon (SOC) plays a significant role in ecosystem protection and sustainable agriculture. The present study aims to estimate the spatial distribution of SOC using three different interpolation methods: ordinary kriging (OK), cokriging (COK), and inverse distance weighting (IDW). Sixty (n = 60) soil samples were collected from the depth of 0 30 cm and analyzed for SOC. The digital elevation model of the site was obtained from USGS explorer at 30 m spatial resolution and processed. Ten (10) terrain attributes were obtained, and a correlation matrix was conducted between SOC and terrain derivatives. The whole dataset was used to evaluate the model accuracy; root mean square error (RMSE) and mean error (ME) were the criteria adopted. Mean value of the SOC of the study area was generally low when compared to the standard rating for tropical soils (< 2%). SOC was significantly (p < 0.01) correlated with LS-factor (r = 0.34*), negatively correlated with elevation (r = 0.30*) and profile curvature (r = 0.30*). IDW performed better (RMSE = 0.75, ME = 0.004) followed by OK (RMSE= 0.78, ME = 0.004) and then COK (RMSE = 0.94, ME = 0.067). Conversely, COK produced the model with the smallest ME with terrain attributes (elevation, LS-factor, and profile curvature). The findings in the study showed that IDW is superior in SOC estimation. COK with the terrain attributes proved to have the capacity as a useful ancillary variable for improving the spatial structure of SOC maps of southeastern Nigeria.

China Geodetic Coordinate System 2000 (CGCS2000), as the formal national coordinate reference frame, has been used for 20 years. The coordinates of all Global Navigation Satellite System (GNSS) stations in China need referring to this system. To this end, the first step is to align the coordinates of all stations, usually included in a regional GNSS network, with a given International Terrestrial Reference Frame (ITRF), then these coordinates are corrected to the CGCS2000 in consideration of plate movement. For a better alignment result, regional control stations are needed and their coordinates were estimated from the combination of constraint-free normal equation systems provided by several International GNSS Service (IGS) analysis centers. The effect in using these refined coordinates, which determine a regional coordinate datum, on the alignment result should be evaluated by the coordinate corrections of the regional control stations to the regional coordinate datum, i.e. smaller corrections mean better alignments of the two associated frames. The test results show that the refined coordinates are more accurate than the ones calculated from the station's velocity, and are well aligned with the ITRF2005. Moreover, for obtaining the coordinates of GNSS stations in an updated CGCS2000 frame, a gridded linear velocity field based on the estimated velocities at 1025 CGCS2000 stations was generated for mainland China using the optimal interpolation method, the inverse distance weighting, which is selected from five interpolation methods. The overall precisions of the constructed velocity field at all stations in the East (E) and, North (N) directions are 0.78 mm/a and 0.95 mm/a, respectively. For evaluating the accuracy of the updated CGCS2000 frame, monthly solutions for the coordinates of some CGCS2000 CORS stations in the ITRF2014 during the period from 2000.0 to 2018 were obtained and the Root Mean Square (RMS) of the differences between the coordinates corrected to the CGCS2000 and the known coordinates at these stations are about 2–3 cm.

In order to display the three-dimensional spatial distribution of soil organic carbon density (SOCD) and analyze the relationship between SOCD and topography, this study took 290 soil samples at the depth of 0 to 100 cm in the Nangou watershed in the loess hilly region of China, simulated the spatial distribution of SOCD by the three-dimensional inverse distance weighting method, and compared the SOCD of several soil sections of different slope shapes and different slope aspects when the land use were all forests. The results showed that the SOCD tended to decrease with increasing soil depth; the mean varied from 0.52 kg m–2 (60 to 100 cm) to 1.03 kg m–2 (0 to 10 cm), and there was obvious stratification in the vertical direction. The variation coefficients of the SOCD were between 23.43% (60 to 100 cm) and 41.15% (10 to 20 cm), which were relatively close in the topsoil of the depth of 0 to 30 cm and gradually decreased with soil depth. The SOCD in the watershed was higher in the northeast, followed by the southwest and lower in the middle. The three-dimensional spatial distribution of the SOCD contained more information, and its prediction accuracy was higher than that of the corresponding two-dimensional method. In slopes where the land use were all forests, the SOCD was ranked as concave slope > straight slope > convex slope and shady slope > sunny slope, and its complexity was ranked as concave slope > convex slope > straight slope and shady slope > sunny slope. Our results highlighted the carbon (C) sequestration ability of concave slopes on the shady side and provided scientific reference for the development of slope C sequestration policies in the loess hilly region of China.

Aquatic environmental problems, such as algae, turbid water, and poor oxygen content, have become increasingly common. In river analysis, hydrological and water quality characteristics are used for evaluating aquatic ecological health, which necessitates continuous monitoring. In addition, because measurements are conducted using a fixed measurement method, the hydrological and water quality characteristics are not investigated for the entire river. Furthermore, obtaining high-resolution data is tedious, and the measurement area and time are limited. Hence, low-resolution data acquisition is generally preferred; however, this requires an appropriate interpolation method to obtain a wide range of data. Therefore, a 3D interpolation method for river data is proposed herein. The overall hydraulic and water quality information of a river is presented by visualizing the low-resolution measurements using spatial interpolation. The Kriging technique was applied to the river mapping to improve the mapping precision through data visualization and quantitative evaluation.

Weighted averages of air pollution measurements from monitoring stations are commonly assigned as air pollution exposures to specific locations. However, monitoring networks are spatially sparse and fail to adequately capture the spatial variability. This may introduce bias and exposure misclassification. Advanced methods of exposure assessment are rarely practicable in estimating daily concentrations over large geographical areas. We propose an accessible method using temporally adjusted land use regression models (daily LUR). We applied this to produce daily concentration estimates for nitrogen dioxide, ozone, and particulate matter in a healthcare setting across England and compared them against geographically extrapolated measurements (inverse distance weighting) from air pollution monitors. The daily LUR estimates outperformed IDW. The precision gains varied across air pollutants, suggesting that, for nitrogen dioxide and particulate matter, the health effects may be underestimated. The results emphasised the importance of spatial heterogeneity in investigating the societal impacts of air pollution, illustrating improvements achievable at a lower computational cost.

A gridded maximum and minimum (Tx and Tn) daily temperature dataset derived by spatial downscaling and bias correction of the ERA5-Land (ERA5L) for the period 1981–2010 is presented. Observed daily Tx and Tn at 154 stations in Ethiopia covering record lengths of 5–30 years were used as a reference. The statistics that define the Gaussian distribution (mean and standard deviation) of Tx and Tn from the station observations were interpolated in space to create a monthly climatology and interannual statistics at 0.05° × 0.05° resolution using a hybrid interpolation approach that combines linear regression with topographic and location attributes, and non-Euclidean inverse distance weighting interpolation. The interpolated monthly and interannual statistics were then used to debias the ERA5L Tx and Tn using a quantile mapping approach. Leave-one-out cross-validation showed that the mean absolute errors in the corrected and downscaled daily temperatures are about 0.7 °C for Tx and 1.1 °C for Tn, reducing the statistical biases in the ERA5L Tx and Tn by 68% and 25% respectively. For monthly climatology, 40–64% of the biases were removed for Tx while for Tn the reductions range from 19% to 32%. The correction also improved commonly used indices for extremes like the probability of warm days, cold days, and warm nights, but overestimated the probability of cold nights. The presented open-access Tx and Tn dataset is a substantial improvement over existing gridded temperature datasets for Ethiopia, such as ERA5L and the Climate Hazards Infrared Temperature with Station (CHIRTS), and we suggest it is suitable for a wide range of environmental applications, e.g. in the fields of hydrology, agriculture, and ecology., Data in Brief, 46, ISSN:2352-3409

Runoff coefficient is an index for losses in rainfall required in most hydrological models and water resources projects. Generally, in the Balakayety water shade in Kurdistan region of Iraq, no runoff data are available as almost basins are ungagged. The Geographic Information System (GIS) techniques and available data for a small basin in Balakayety watershed within the Erbil governorate (Kurdistan Region of Iraq) were used to estimate the coefficient of potential runoff coefficient (PRC). The estimated PRC will be then used to calculate the depth of runoff. The Satellite Imagery (Landsat 7 ETM+) for 2015 was procured to create land cover classes, the classifications accuracy equal to 88.4%, and the Kappa index is 83.7%. The slope map developed from the digital elevation model with 30 m resolution. The area’s hydrologic soil map is digitized from the Iraqi soil map. The inverse distance weighting method was used to interpolate the rainfall of the study area into the Arc GIS. It was found that the most study area covered by moderate to high (PRC) values ranged from 55% to 83%, and runoff depth values vary from 398.1 mm to 610.3 mm. The present study indicates that the integration of all obtained thematic maps into GIS provides a powerful tool for calculating the runoff coefficient for catchments lacks streamflow data which can result in flood/drought predictions that will be not accurate. These findings are helpful in identifying flood areas, selecting proper sites for water harvesting, and enhancing water resources management programs.

The Al-Najaf sea is a natural groundwater depending depression reservoir. The increase in uncontrolled dumping of solid waste in nearby areas and the use of sewage and animal manure as agricultural enhancing agents raise the fear of threatening the natural life in this lake by increasing metal concentrations in sediments. Forty-eight samples of sediments were collected, and the concentrations of eight metals (Fe, Zn, Cr, Mn, Co, Pb, Cd, and Ni) were measured in each sample. Five pollution indices (contamination factor, degree of contamination, pollution load index, enrichment factor and geo - accumulation index) were calculated. Spatial distributions with the pollution indices were studied to detect the specific metals that may contaminate the sediments and their probable source from the pattern of their distribution. The study revealed that, for most metals, concentrations at the lake shore were higher than those in the interior of the lake. Moreover, all the pollution indices were higher than the critical value on shores and slightly less than the critical level inside the lake, which means that the use of spatial distribution of metals in combination with pollution indices are beneficial in the detection of metal polluted sediments and their possible sources from their transmission patterns.

A água subterrânea é de grande importância como reserva hídrica e o uso de Sistemas de Informações Geográficas tem se mostrado eficaz para a compreensão da ocorrência e movimentação deste recurso. O objetivo do estudo foi avaliar métodos de interpolação aplicados a dados hidrogeológicos com a verificação do impacto gerado pelo processo de estimativa. Os métodos avaliados foram os interpoladores Inverso da Distância Ponderada, Triangulação e Topo to Raster e os dados considerados foram os parâmetros hidrogeológicos dos aquíferos sedimentares no estado do Paraná: Furnas, Itararé, Rio Bonito, Passa Dois, Guarani e Caiuá. Os resultados foram avaliados de acordo com a ocorrência de estimativas positivas e negativas, normalized root mean square e precisão do interpolador ao gerar resultados mais próximos dos valores observados. Esse estudo mostrou o efeito da interpolação em dados hidrogeológicos indicando que o Inverso da Distância Ponderada foi o método mais adequado para os parâmetros e aquíferos focos do estudo, pois apresentou as menores variações entre os valores observados e os estimados e os menores valores de normalized root mean square (entre 0 e 0,108). Esse estudo demonstra a importância em escolher o método adequado para intepretação de dados em regiões não amostradas devido às grandes variações em interpolar dados.

Iran is located in an arid and semi-arid region of the world; however, more than 90% of Iran’s total water is allocated to agricultural sector. Owing to the climatic limitations of the country, agricultural water management has become one of the country’s greatest concerns. Therefore, the present study has attempted to identify optimal locations in terms of high yield and low total water footprint (WF) (m3/$) in the northwest and southeast provinces of the country to produce main cereals such as wheat, barley, and maize. In this regard, inverse distance weighting (IDW) method was first employed 150 sample coordinates to prepare interpolation maps of evapotranspiration (ETc) (mm), irrigation requirements (IR) (mm), nitrogen application (kg/ha), and effective precipitation (Peff) (mm). Subsequently, green WF, blue WF, gray WF, total WF (TWF) (m3/ton), and economical total WF (ETWF) (m3/$) maps were prepared for each crop. Multiple linear regression (MLR) was then applied to determine the correlation between ETWF (m3/$) and other parameters. ETWF (m3/$) was later predicted using multiple linear regression (MLR), in conjunction with artificial neural networks (ANNs) including multilayer perceptron (MLP) and radial basis function (RBF). Findings showed that IDW can be applied with a relatively low error so that an interpolation map for each parameter can be obtained and the changes can be examined. The results of MLR indicated that for assessing the yield of barley and wheat the TWF possesses and for assessing the yield of maize, the IR model had the highest rate of regression. The results of ANN indicated that the RBF (R2 = 0.901), the RBF (R2 = 0.882), and the MLP (R2 = 0.947) were the most appropriate models for the prediction and evaluation of ETWF and yield with respect to measured estimates of ETWF (m3/$) and yield of wheat, barley, and maize (in order of appropriability).

Elevation is a basic information of the earth, and different elevation models are provided to better understanding the earth and its different functions. However, it is not always possible to conduct a comprehensive survey in big areas and calculate all surface points. The best way is survey some points, then the elevation estimation is done using these points in each part of study area. The purpose of this paper is to use interpolation methods to estimate elevation. In such cases, different methods are used to interpolate and estimate points with an uncertain height. In this paper, the three usual methods are chosen and introduced then their performance are compared. These methods including: Inverse Distance Weighting (IDW), the Krige method or Kriging, and Artificial Neural Network (ANN). The results show that Artificial Intelligence with RMS = 5.9m is better in compare to Kriging with RMS = 7.2 and IDW with RMS = 9. The obtained result presents that in despite of its convenience, ANN provides DEMs with minimum errors.

Priatna SJ, Rahman D, Supriyadi S. 2020. Land suitability assessment for some carbohydrate food crops at wetland area in Arisan Jaya. Jurnal Lahan Suboptimal : Journal of Suboptimal Lands 9(2): 117-126.The nature condition and lack of knowledge about soil characteristics have become a limitation for crops cultivation and development in Arisan Jaya. This study aimed to determine the type of crops that has potential to be developed in site. The assessment was carried out in Arisan Jaya, Pemulutan Barat Sub-District, Ogan Ilir Regency, South Sumatera from April to August 2015. The study method is 1:30.000 semi-detailed survey. The location of the sample was determined by grid method with one sample for every 1.000 meters with 10 sample sites. A disturbed soil sample was taken as deep as 150 cm from the ground surface. Land characteristics data were matched with crops growth requirements based on the suitability classes set for wetland rice, dry land rice, corn, cassava and sweet potato. The distribution of soil properties was known by IDW (Inverse Distance Weighting) interpolation method, which was overlapped to determine the distribution of land suitability classes. Very acidic soil conditions was a major limiting factor for crops cultivation in general (the actual suitability class is Nf). Wetland rice was relatively more suitable to be cultivated than dryland rice in the site. Corn and cassava could be planted as rotational crops before the rainy season or after rice season, although the productivity would not be optimal (S2 potential suitability class). Climatic conditions was another limiting factor for the development of sweet potato at the site (S3 potential suitability class).

The air pollution level can be assessed using air quality index - AQI calculated from the concentration of some gases and particle matters which are measured at ambient air quality monitoring stations. The calculated AQI values are characterized by temporal continuity but spatial discontinuity. However, AQI values of each monitoring station is interpolated by the IDW (Inverse Distance Weighting) method in GIS which helps us to assess the air quality at a detailed and specific level for every location in the study area by establishing distribution maps of air pollution. The interpolation of AQI values for zoning air quality in several urban districts of Hanoi during the Winter (October, November, December 2019) shows that in general, the areas with a very bad level of air quality occupied an important surface in the Northwest of urban districts (on the territory of Bac Tu Liem, Ba Dinh, Tay Ho, Cau Giay) for last 3 months of the year. The areas with a bad level of air quality occupied a large surface in the Southeast in October and December, but its surface became narrow in November. But in November, areas having a bad level of air quality were expanded to the Southeast while they occupied only a small surface at the center of the study area in October and December. Although the distribution of each level vary in terms of coverage, their common pattern has been conserved during three months of Winter. The distribution map of air quality provides the complete picture of the air pollution situation and it helps to adequately evaluate this issue in the urban districts of Hanoi city.

Spatial interpolation is important for visualizing changes of parameters over space. Interpolation methods for the spatiotemporal analysis of rainfall pattern changes of Peninsular Malaysia due to climate change were evaluated. Inverse Distance Weighting (IDW) and Ordinary Kriging (OK), Geographical Weighted Regression (GWR) and Multi-scale Geographical Weighted Regression (MGWR) methods were used. Based on the statistic results of RMSE, MAE and R2, the MGWR was the best performing model. To investigate the spatial interpolations with the MGWR, the period considered was arbitrarily and conveniently divided into six 5-year sub-periods for spatiotemporal analysis. Monthly Rainfall, Number of Wet Days and Maximum Daily Rainfall were parameters identified for the analyses. The results showed that the Peninsular Malaysia generally receives relatively higher rainfall amounts and intensities over the northern part of the East Coast region, while lower values were commonly received at the Central region. Changes in spatial pattern were also observed in the maps generated for the onset and withdrawal months of both the Northeast Monsoons and the Southwest Monsoons. The sub-period analyses also showed that Peninsular Malaysia has gradual increase of rainfall intensity due to climate change and is susceptible and vulnerable to El Nino/La Nina events. Hence, in the future Peninsular Malaysia is likely to have increasing occurrence of extreme rainfall events.

The study of the hydric regime of rainfall helps in management analysis and decision-making in hydrographic basins, but a fundamental condition is the need for continuous time series of data. Therefore, this study compared gap filling methods in precipitation data and validated them using robust statistical techniques. Precipitation data from the municipality of Itirapina, which has four monitoring stations, were used. Four gap filling techniques were used, namely: normal ratio method, inverse distance weighting, multiple regression and artificial neural networks, in the period from 1979 to 1989. For validation and performance evaluation, the coefficient of determination (R²), mean absolute error (MAE), mean squared error (RMSE), Nash-Sutcliffe coefficient (Nash), agreement index (D), confidence index were used (C) and through non-parametric techniques with Mann-Witney and Kruskal-Wallis test. Excellent performances of real data were verified in comparison with estimated data, with values above 0.8 of the coefficient of determination (R²) and of Nash. Kruskal-Wallis and Mann-Whitney tests were not significant in Stations C1 and C2, demonstrating that there is a difference between real and estimated data and between the proposed methods. It was concluded that the multiple regression and neural network methods showed the best performance. From this study, efficient tools were found to fill the gap, thus promoting better management and operation of water resources. Resumo O estudo do regime hídrico das chuvas auxilia nas análises de gestão e em tomadas de decisão nas bacias hidrográficas, mas uma condição fundamental é a necessidade de séries temporais contínuas de dados. Diante disso, o objetivo do presente estudo foi realizar a comparação entre os métodos de preenchimento de falha em dados de precipitação e validá-los através de técnicas estatísticas robustas. Foram utilizados dados de precipitação localizados no município de Itirapina que conta com quatro estações de monitoramento. Foi empregado quatro técnicas de preenchimento de falhas, sendo: método razão normal, ponderação distância inversa, regressão múltipla e redes neurais artificiais, no período de 1979 a 1989. Para validação e avaliação do desempenho utilizou-se o coeficiente de determinação (R²), erro absoluto médio (MAE), erro quadrático médio (RMSE), coeficiente de Nash-Sutcliffe (Nash), índice de concordância (D), índice de confiança (C) e através de técnicas não paramétricas com teste de Mann-Whitney e Kruskal-Wallis. Foram verificados ótimos desempenhos dos dados reais em comparação aos dados estimados, com valores acima de 0,8 do coeficiente de determinação (R²) e de Nash. Para os testes de Kruskal-Wallis e Mann-Whitney não foram significativos nas estações C1 e C2, demonstrando que existe diferença entre os dados reais e estimados e entre os métodos propostos. Pôde-se concluir que os métodos de regressão múltipla e redes neurais apresentaram os melhores desempenhos. A partir desse estudo verificou-se ferramentas eficientes para o preenchimento de falha promovendo assim, uma melhor gestão e operação dos recursos hídricos.

In the field of indoor positioning, Wi-Fi FTM is a new technology for realizing high-precision positioning. However, errors caused by clock drift and non-line-of-sight (NLOS) signals affect its positioning accuracy. When receiving NLOS signals, most existing positioning algorithms only delete these signals, which decreases the number of nodes and may decrease accuracy. To address this issue, this paper proposed an adapted weighted positioning method under the NLOS environment. First, this method includes a compensation model to decrease the error caused by clock drift and multipath. Additionally, it can evaluate ranging results and improving the positioning accuracy by assigning greater weight to better ranging results. To verify the effectiveness and feasibility of the proposed method, a positioning experiment is performed under an NLOS environment. The results show that our proposed method is suitable for positioning in a completely NLOS environment and effectively improves the positioning accuracy. Compared with the traditional least squares-based method and the inverse distance weighting-based positioning method, the mean error of the proposed method outperformed by approximately 30% and 20% respectively.

With the massive increase in usage of some parts of the electromagnetic spectrum during the last decades, the ability to create real time maps of signal coverage is now more important than ever before. This Masters project is designed to test two different methods of generating such maps with a one second limit to processing time. The interpolation methods under consideration are known as inverse distance weighting and kriging. Several different variants of kriging are considered and compared some of which were implemented specif cally for the project and one variant designed by a third party.The data used is acquired from an antenna array inside a laboratory room at LTU rather than being simulated. The data collection is done with the transmitter at several different positions in the room to make sure the interpolation works consistently. The results show only small differences in both the mean and median of the absolute error when comparing inverse distance weighting and kriging and the variations between transmitter positions are signifcant enough that no single variant is consistently the best using that metric. Using a resolution with 25cm2 pixel size there were no problems reaching significantly lower than the 1sec time limit. If the resolution is increased to apixel size of 1cm2 neither method is able to consistently update the map at the required pace. Kriging however showed that it can generate values outside the range of observed values which could make the extra effort required to implement it worth it since such a characteristic might be very useful for finding the transmitter.

High spatial and thematic resolution of Land Use/Cover (LU/LC) maps are central for accurate watershed analyses, improved species, and habitat distribution modeling as well as ecosystem services assessment, robust assessments of LU/LC changes, and calculation of indices. Downscaled LU/LC maps for Switzerland were obtained for three time periods by blending two inputs: the Swiss topographic base map at a 1:25,000 scale and the national LU/LC statistics obtained from aerial photointerpretation on a 100 m regular lattice of points. The spatial resolution of the resulting LU/LC map was improved by a factor of 16 to reach a resolution of 25 m, while the thematic resolution was increased from 29 (in the base map) to 62 land use categories. The method combines a simple inverse distance spatial weighting of 36 nearest neighbors’ information and an expert system of correspondence between input base map categories and possible output LU/LC types. The developed algorithm, written in Python, reads and writes gridded layers of more than 64 million pixels. Given the size of the analyzed area, a High-Performance Computing (HPC) cluster was used to parallelize the data and the analysis and to obtain results more efficiently. The method presented in this study is a generalizable approach that can be used to downscale different types of geographic information.