A Comparison of the Performance of Different Interpolation Methods in Replicating Rainfall Magnitudes under Different Climatic Conditions in Chongqing Province (China)

This paper presented the levels of PM2.5 and PM10 in different stations at the city of Sabzevar, Iran. Furthermore, this study was an attempt to evaluate spatial interpolation methods for determining the PM2.5 and PM10 concentrations in the city of Sabzevar. Particulate matters were measured by Haz-Dust EPAM at 48 stations. Then, four interpolating models, including Radial Basis Functions (RBF), Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Universal Kriging (UK) were used to investigate the status of air pollution in the city. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were employed to compare the four models. The results showed that the PM2.5 concentrations in the stations were between 10 and 500μg/m3. Furthermore, the PM10 concentrations for all of 48 stations ranged from 20 to 1500μg/m3. The concentrations obtained for the period of nine months were greater than the standard limits. There was difference in the values of MAPE, RMSE, MBE, and MAE. The results indicated that the MAPE in IDW method was lower than other methods: (41.05 for PM2.5 and 25.89 for PM10). The best interpolation method for the particulate matter (PM2.5 and PM10) seemed to be IDW method.•The PM10 and PM2.5 concentration measurements were performed in the period of warm and risky in terms of particulate matter at 2016.•Concentrations of PM2.5 and PM10 were measured by a monitoring device, environmental dust model Haz-Dust EPAM 5000.•Interpolation is used to convert data from observation points to continuous fields to compare spatial patterns sampled by these measurements with spatial patterns of other spatial entities.

This study compared the performance of different interpolation methods for mapping soil salinity of three different agricultural fields having the same land use but different dataset characteristics. Four common spatial interpolation methods including global polynomial interpolation (GPI), inverse distance weighted (IDW), ordinary kriging (OK), and radial basis functions (RBF) were employed for mapping soil EC. The performance of interpolation methods in predicting soil EC was evaluated based on mean bias error, root mean square error, mean absolute percentage error, and coefficient of determinations criteria. Results showed that dataset characteristics, including central tendency and distribution, were significantly different among the studied fields. Experimental semivariogram and fitted model parameters indicated that three studied fields were also different in their spatial dependence strength. Considering all of the performance assessment measures used, the best interpolation method for fields A and C was OK and IDW for field B. The performance of interpolation methods was found to be affected by data characteristics of the studied fields, which were mostly ascribed to management practices. This study suggests in order to obtain accurate mapping of soil salinity in agricultural fields, it is essential to first find the best spatial interpolation method compatible with the characteristics of the collected data from the selected agricultural land.

It is vital to accurately map the spatial distribution of precipitation, which is widely used in many fields such as hydrology, climatology, meteorology, ecology, and agriculture. This study aimed to reveal the spatial distribution of seasonal, long-term average precipitation in the Euphrates Basin with various interpolation methods. For this reason, Simple Kriging, Ordinary Kriging, Universal Kriging, Ordinary CoKriging, Empirical Bayesian Kriging, Radial Basis Functions (Completely Regularized Spline, Thin Plate Spline, Multiquadratic, Inverse Multiquadratic, Spline with Tensor), Local Polynomial Interpolation, Global Polynomial Interpolation, and Inverse Distance Weighting methods have been applied in the Geographical Information Systems environment. Long-term seasonal precipitation averages between 1966 and 2017 are presented as input for predicting precipitation maps. The accuracy of the precipitation prediction maps was based on linear regression analysis, root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), and determination coefficient (R2) values obtained from the cross-validation tests. The most suitable method was chosen for the interpolation method that gives the lowest RMSE, MAE, and the largest R and R2. As a result of the study, Ordinary CoKriging in spring and winter precipitation, Local Polynomial Interpolation in summer precipitation, and Ordinary Kriging in autumn precipitation were the most appropriate estimation methods.

Zhang, R.; Yang, S.; Wang, Y., and Yin, Y., 2020. Regional ocean current field construction based on an empirical Bayesian kriging algorithm using multiple underwater gliders. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 41-47. Coconut Creek (Florida), ISSN 0749-0208.The ocean current is an important component of the complex marine environment. Ocean current forecasting data mainly come from computational simulation, which merges satellite remote sensing data and in situ data measured from marine vehicles. Having a deep understanding of ocean currents is essential for ocean observation and path guidance of ships or other marine vehicles. In this paper, the regional depth-averaged flow is deduced according to the motion characteristics of underwater glider. Through virtual mooring observations of three underwater gliders in the northern South China Sea, sea trial data from June 25th to July 11th 2019 are obtained. After denoising and filtering, the depth-averaged flow data are used to construct the depth-averaged flow field with the spatial interpolation method. Then, the inverse distance weight (IDW) method, radial basis interpolation (RBI) method and empirical Bayesian kriging (EBK) method are adopted to construct the depth-averaged flow field. Thus, the results from the three constructed methods are compared with the real flow field data, demonstrating that the EBK method shows a better balance in interpolation accuracy and performance than the other methods. More specifically, for the EBK method, the mean error value is 0.00441, and the root-mean-square of error is 0.14977, which are lower than those of the other two methods. Furthermore, the evaluation results thoroughly verify the validity of the EBK method and prove that an underwater glider is a useful tool for observing ocean currents.

The objective of the industry in general, and of the chemical industry in particular, is to satisfy consumer demand for products and the best way to satisfy it is to forecast future sales and plan its operations.Considering that the choice of the best sales forecast model will largely depend on the accuracy of the selected indicator (Tofallis, 2015), in this work, seven techniques are compared, in order to select the most appropriate, for quantifying the error presented by the sales forecast models. These error evaluation techniques are: Mean Percentage Error (MPE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Symmetric Mean Absolute Percentage Error (SMAPE) and Mean Absolute Arctangent Percentage Error (MAAPE). Forecasts for chemical product sales, to which error evaluation techniques are applied, are those obtained and reported by Castillo, et. al. (2016 & 2020).The error measuring techniques whose calculation yields adequate and convenient results, for the six prediction techniques handled in this article, as long as its interpretation is intuitive, are SMAPE and MAAPE. In this case, the most adequate technique to measure the error presented by the sales prediction system turned out to be SMAPE.

Optimal interpolation approach for groundwater depth estimation

<span>The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grundulu fault, the research employs Random Forest (RF), Extreme Gradient Boosting (XGB), Neural Network (NN), AdaBoost (AB), and Support Vector Machine (SVM) methods. Utilizing real-time radon gas concentration measurements, the study aims to refine earthquake magnitude prediction, crucial for disaster preparedness. The evaluation involves multiple metrics like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Squared Error (MSE), Symmetric Mean Absolute Percentage Error (SMAPE), and cnSMAPE. XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.</span>

BackgroundInfections caused by multidrug-resistant organisms (MDROs) continue to pose serious challenges for hospital infection control, often resulting in longer hospitalizations, increased patient morbidity, and higher healthcare costs. While time series forecasting has gained traction as a tool for anticipating MDROs trends, there remains a lack of real-world studies comparing the effectiveness of different modeling approaches using hospital-based data.ObjectiveThis study aimed to evaluate and compare the predictive performance of four time series models—SARIMA, ETS, Prophet, and NNETAR—using monthly MDROs infection data collected from a tertiary hospital in China between 2014 and 2023, with the goal of forecasting trends for 2024.MethodsMonthly MDROs infection rates from January 2014 to December 2023 were analyzed using R software. Stationarity was assessed through unit root tests, and appropriate differencing was applied as needed. Each model was fitted to the training dataset and used to forecast infection rates for the year 2024. Model accuracy was assessed by comparing forecasted values with actual 2024 data using root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), symmetric mean absolute percentage error (sMAPE), and mean absolute scaled error (MASE).ResultsAmong the models, SARIMA produced the most consistent and reliable forecasts (RMSE = 0.0469, MAE = 0.0424, MAPE = 20.74%, sMAPE = 21.27%, MASE = 0.932), with residuals satisfying tests for independence and normality. Although the ETS model achieved lower numerical point errors (RMSE = 0.0367, MAE = 0.0305, MAPE = 14.46%, sMAPE = 14.81%, MASE = 0.670), its residual diagnostics raised concerns regarding robustness. The Prophet (RMSE = 0.0499, MAE = 0.0439, MAPE = 20.41%, sMAPE = 22.15%, MASE = 0.563) and NNETAR (RMSE = 0.0697, MAPE = 30.60%, sMAPE = 30.60%, MASE = 0.072) models captured certain aspects of the data dynamics but showed lower overall robustness compared with SARIMA.ConclusionBased on its overall robustness and diagnostic consistency, SARIMA is recommended for short- to medium-term forecasting of MDROs infection trends. The other models, while less reliable on their own, may still be valuable for validating trends and conducting sensitivity analyses to support hospital infection control planning.

Establishing the spatial distribution of soil properties in agro-ecosystems is critical to increasing the productivity of intensive tillage soils and reducing environmental pressure. Thus, a comprehensive understanding of soil’s heavy metal distribution characteristics is important for optimal management. The goal of this study was to determine the most accurate soil Cd interpolation method for the study area and analyze the variation and spatial distribution of Cd content with depth. In this work, we employed two common spatial interpolation methods—inverse distance weighting (IDW) and radial basis function (RBF)—in conjunction with a geographic information system (GIS) to interpolate sampling data of the heavy metal cadmium (Cd) throughout Santai County; a locale in the lower part of Fujiang River, Mianyang, in the northeast of Sichuan Province. The results of our investigation are as follows. (1) At depths of 0–50 cm, the Cd content decreased with depth while the coefficient of variation (Cv) increased with depth. The exponential model and the Gauss model adequately described the Cd content’s spatial autocorrelation and the determination coefficient (R2) varied within 0.747–0.962, indicating that the model had high precision. (2) With respect to the accuracy of the IDW and RBF methods, IDW was more accurate than the RBF in the 0–20 cm soil layer, yet the reverse was found in the 20–50 cm soil layer. (3) Three-dimensional spatial distribution maps—constructed using the RBF and IDW methods at different soil depths—showed that the RBF method provided a greater smoothing effect than the IDW method. The spatial variability of soil Cd content shows that: (1) the Cd content’s spatial autocorrelation was strong in the 0–50 cm soil layer. (2) The IDW method is suitable for areas that are greatly influenced by human or farming activities while the RBF method is appropriate for areas that are greatly influenced by internal soil factors. The RBF is superior for evaluating the spatial distribution of soil Cd content in this area.

BackgroundArtificial intelligence tools, including large language models such as ChatGPT, are increasingly integrated into clinical and primary care research. However, their ability to assist with specialized statistical tasks, such as sample size estimation, remains largely unexplored.MethodsWe evaluated the accuracy and reproducibility of ChatGPT-4.0 and ChatGPT-4o in estimating sample sizes across 24 standard statistical scenarios. Examples were selected from a statistical textbook and an educational website, covering basic methods such as estimating means, proportions, and correlations. Each example was tested twice per model. Models were accessed through the ChatGPT web interface, with a new independent chat session initiated for each round. Accuracy was assessed using mean and median absolute percentage error compared with validated reference values. Reproducibility was assessed using symmetric mean and median absolute percentage error between rounds. Comparisons were performed using Wilcoxon signed-rank tests.ResultsFor ChatGPT-4.0 and ChatGPT-4o, absolute percentage errors ranged from 0% to 15.2% (except one case: 26.3%) and 0% to 14.3%, respectively, with most examples showing errors below 5%. ChatGPT-4o showed better accuracy than ChatGPT-4.0 (mean absolute percentage error: 3.1% vs. 4.1% in round#1, P-value = .01; 2.8% vs. 5.1% in round#2, P-value =.02) and lower symmetric mean absolute percentage error (0.8% vs. 2.5%), though not significant (P-value = .18).ConclusionsChatGPT-4.0 and ChatGPT-4o provided reasonably accurate sample size estimates across standard scenarios, with good reproducibility. However, inconsistencies were observed, underscoring the need for cautious interpretation and expert validation. Further research should assess performance in more complex contexts and across a broader range of AI models.

1. Adhikary P.P., Dash J., Bej R., and Chandrasekaran H., 2011. Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India. Environ. Monit. Assess., 176, 663-676. Google Scholar

Optimized management of water resources, conservation and their quality increase is needful with data existence in basis of situation, amount and distribution of water chemical factors for example; electrical conductivity (EC) in determined geographical region. Accuracy of interpolation appropriate methods and variation map preparation of groundwater quality variables is independent to region conditions and existence of enough data. That is true selection of interpolation methods is basic and important step in management of groundwater resources. EC is one of the important indicators for groundwater quality evaluation. The objective of this research was to determine the most suitable interpolation method and their accuracy for analysis and checking spatial variation of groundwater EC amount in central regions of Guilan province, northern Iran. This investigation evaluated the inverse distance weighting (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI), radial basis function (RBF) and ordinary kriging (OK) methods for estimation of groundwater EC in paddy fields. In IDW method, for variable estimation used power value 1–5 that power value equal 1 was exact. Gaussian model was the best one fitted on empirical semivariogram of variable data in OK method. Standard statistical performance evaluation criteria include root mean square error (RMSE), correlation coefficient (R) and mean absolute error (MAE) were used to control the accuracy of the prediction capability of the developed methods. Results showed that the best estimator was OK method which was the most exact with regard to other methods for estimation groundwater electrical conductivity.

The qualities of different groundwater parameters rely on the sources of groundwater recharge, industrial effluent, urban interactions, agriculture, aquifer pumping, and waste disposal. Domination of any of these factors alters the groundwater quality. GIS enables the tracking of changes throughout time within an area/ watershed and correlating changes in water quality with these alterations. Choosing an appropriate GIS-spatial interpolation technique is a critical success element in surface analysis since different interpolation methods might result in different surfaces and, eventually, different outcomes. Though no particular interpolation technique is completely optimum, the best interpolation method for a given circumstance can only be determined by comparing their results. An automated tool has been developed to assess popular interpolation techniques like inverse distance weightage, radial basis function, ordinary kriging, universal kriging, empirical Bayesian kriging, and kernel interpolation in terms of root mean square error and mean absolute error. The model provides researchers and official groundwater quality monitoring and assessment teams with the help to pick the optimal algorithm.KeywordsGroundwater qualityGISSpatial interpolationGeoprocessing modelAutomated toolAccuracy assessment

Comparing interpolation methods to predict soil total phosphorus in the Mollisol area of Northeast China

Accurate and reliable interpolation of groundwater depth over a region is a pre-requisite for efficient planning and management of water resources. The performance of two deterministic, such as inverse distance weighting (IDW) and radial basis function (RBF) and two stochastic, i.e., ordinary kriging (OK) and universal kriging (UK) interpolation methods was compared to predict spatio-temporal variation of groundwater depth. Pre- and post-monsoon groundwater level data for the year 2006 from 110 different locations over Delhi were used. Analyses revealed that OK and UK methods outperformed the IDW method, and UK performed better than OK. RBF also performed better than IDW and OK. IDW and RBF methods slightly underestimated and both the kriging methods slightly overestimated the prediction of water table depth. OK, RBF and UK yielded 27.52, 27.66 and 51.11 % lower RMSE, 27.49, 35.34 and 51.28 % lower MRE, and 14.21, 16.12 and 21.36 % higher R2 over IDW. The isodepth-area curves indicated the possibility of exploitation of groundwater up to a depth of 20 m.