Spatial Modeling of Mean Annual Temperature in Iran: Comparing Cokriging and Geographically Weighted Regression

Khosravi, Younes; Balyani, Saeed

doi:10.1007/s10666-018-9623-5

Cited by 10 publications

(6 citation statements)

References 42 publications

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“…The high efficiency of the ordinary cokriging method in solving problems using remote sensing has been proved in comparison with alternative methods. This method allows increasing the efficiency of the analysis of the studied indicator (ground based measurements) due to the information obtained using remote sensing, and this may be several sets of optical data that correlate with the studied indicator [20][21][22].…”

Section: Resultsmentioning

confidence: 99%

Spatial distribution prediction of agro-ecological parameter using kriging

et al. 2020

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In modern agroecology, one of the most pressing problems is the problem of spatial data mapping. The development of information technology opens up a wide range of approaches for solving this problem. One of these approaches is based on the use of geostatistical methods. This study was carried out with the aim of developing ideas about the applicability of the ordinary kriging method for predicting the spatial distribution of the agro-ecological indicator with identifying the boundaries of in-field heterogeneity according to remote sensing data. For the model computational experiment, aerial photographs of the agricultural field in the red and near infrared ranges were used, which made it possible to obtain sets of uniformly distributed values of the vegetative index NDVI that were randomly generated. The high spatial resolution of the images allowed us to analyze the observational data for the studied agricultural field.

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Section: Resultsmentioning

confidence: 99%

Spatial distribution prediction of agro-ecological parameter using kriging

et al. 2020

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“…In order to determine the accuracy of the obtained results from the noise map, the Leq of the specific locations were compared to the calculations obtained by Equation 3. In order to calculate the precision of the applied interpolation methods for the development of the noise map in the ArcGIS software, the leave-one-out cross-validation technique was used [18]. According to the cross-validation, one point was temporarily removed from its neighboring areas and estimated.…”

Section: Determining the Accuracy Of The Resultsmentioning

confidence: 99%

Determination of the Equivalent Continuous Sound Level (Leq) in Industrial Indoor Space Using GIS-based Noise Mapping

Majidi

Khosravi

Abedi

2019

J. Hum. Environ. Health Promot

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Background: The present study aimed to replace the integrated sound level meter by the noise map of a work environment in order to estimate the equivalent continuous sound level (Leq) as an important quantity in the noise monitoring of continuous noise sources. Methods: In this theoretical-experimental study, the grid method was initially used. Sound pressure level (SPL) was measured at the selected stations in three noisy industrial halls. Data analysis was performed in ArcGIS 10.2 software, and the noise map was plotted for each hall separately. Afterwards, the different zones with various SPL intervals were calculated on each noise map, and Leq was determined. For the comparisons, Leq was also calculated using logarithmic equations, based on which the integrated sound level meters were programmed. Results: The proposed method was highly accurate with the relative error of less than 2%. Furthermore, it decreased the number of mathematical operations 7-15 times compared to the conventional logarithmic method. Conclusion: According to the results, the available GIS-based software could be accurately replaced by the routine Leq measurement hardware to estimate the Leq spatial noise in noisy industrial environments.

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“…(1996). GWR can be computed as (Bostan et al ., 2012; Georganos et al ., 2017; Khosravi and Balyani, 2018):

Z_{i} = β_{0} (x_{i}, y_{i}) + β_{k} (x_{i}, y_{i}) t_{italicik} + ε_{i}

where Z i , precipitation at the location i ; x i , y i , geographical coordinates of i ; β 0 , regression constant; β k , correlation coefficient of co‐variable; t ik , co‐variable; ε i , random error.…”

Section: Methodsmentioning

confidence: 99%

“…Additional information on GWR may be found in Fotheringham et al (2003) and Brunsdon et al (1996). GWR can be computed as (Bostan et al, 2012;Georganos et al, 2017;Khosravi and Balyani, 2018):…”

Section: Geographically Weighted Regressionmentioning

confidence: 99%

Enhanced precipitation prediction using DEM‐based predictors and satellite imagery

Antal

Dumitrescu

Cheval

et al. 2023

Intl Journal of Climatology

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Over the last decades, climatic changes have triggered considerable impacts across the globe with detrimental effects on all ecosystems. Given the complexity of topography and climate, Romania is one of the most exposed countries in the South‐Eastern Europe to extreme hydrological events. As a consequence, the spatial distribution of precipitation is of greater importance for future analysis. This study examines the performance of Empirical Bayesian Kriging Regression Prediction (EBKRP) and Geographically Weighted Regression (GWR) to predict the spatial distribution of annual and seasonal precipitation over Romania. Twelve co‐variables derived from topography, and remote sensing products data were used to improve the prediction. The co‐variable selection process was performed prior to the regression model to reduce the complexity and processing time, using the Boruta Algorithm (BA), which is an innovative approach. The performance of BA was compared with Gini Coefficient. Our findings confirmed that 10 co‐variables were relevant to predict annual precipitation and nine for seasonal. The overall prediction of precipitation is more influenced by topography (altitude, slope, surface roughness) and the distance to marine bodies (Black Sea and Adriatic Sea). Cross‐validation and five statistical metrics were applied to assess the performance of the regression models. The results show similar spatial distribution pattern of precipitation, whereas the highest annual precipitation is found in the Carpathian Mountains (EBKRP: 1,399.2 mm, GWR: 1,249.7 mm) and the lowest in the lowlands (EBKRP: 355.9 mm, GWR: 346.8 mm). For seasons, both methods predicted the highest precipitation in summer and lowest in autumn. In all seasons, the precipitation is underestimated by both methods, however, for annual, only GWR does so. Our study revealed GWR as the best method to predict annual and seasonal precipitation over Romania, as it yielded the highest correlation coefficient Spearman (S), Pearson (P), determination coefficient (R2) and lowest mean absolute error (MAE) and root mean square error (RMSE).

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Spatial Modeling of Mean Annual Temperature in Iran: Comparing Cokriging and Geographically Weighted Regression

Cited by 10 publications

References 42 publications

Spatial distribution prediction of agro-ecological parameter using kriging

Spatial distribution prediction of agro-ecological parameter using kriging

Determination of the Equivalent Continuous Sound Level (Leq) in Industrial Indoor Space Using GIS-based Noise Mapping

Enhanced precipitation prediction using DEM‐based predictors and satellite imagery

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