Comparative suitability of ordinary kriging and Inverse Distance Weighted interpolation for indicating intactness gradients on threatened savannah woodland and forest stands

Munyati, C.; Sinthumule, Ndidzulafhi Innocent

doi:10.1016/j.indic.2021.100151

Cited by 40 publications

(20 citation statements)

References 35 publications

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“…Many studies suggest that Kriging presents better results (Shukla et al, 2020;Keshavarzi et al, 2021;Munyati & Sinthumule, 2021), however, in some cases, where the spatial dependence is considered weak, IDW is preferred. However, it is important to note that data variation is a dominant factor in method accuracy; as variation increases, method accuracy decreases (Li & Heap, 2011).…”

Section: Resultsmentioning

confidence: 99%

Estimating soil loss by laminar erosion using precision agriculture computational tools

Krug

Gomes

Souza

et al. 2022

Rev. bras. eng. agríc. ambient.

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The study aimed to identify and evaluate the spatial variability in laminar erosion in areas using precision agriculture tools. Soil data from three properties in the western region of Paraná state, Brazil, were used: one in the municipality of Céu Azul (area A) and two in Serranópolis do Iguaçu (areas B and C). To identify discrepant data (outliers), analysis of the dispersion of quartiles was performed using a box-plot graph. Data normality was verified using the Kolmogorov-Smirnov test. A spatial analysis was performed using AgDataBox-Map software. The parameters of the universal soil loss equation were estimated and used with map algebra to produce a model to identify areas susceptible to erosion. Area A (soil loss estimate = 0-200 t ha-1 per year) presented greater susceptibility to erosion than areas B and C (soil loss estimate = 0-150 t ha-1 per year); however, all areas had a low susceptibility to erosion.

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Section: Resultsmentioning

confidence: 99%

Estimating soil loss by laminar erosion using precision agriculture computational tools

Krug

Gomes

Souza

et al. 2022

Rev. bras. eng. agríc. ambient.

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“…its regression function is Equation (7), which is calculated according to the objective Function (8) [49][50][51].…”

Section: Svr Modelmentioning

confidence: 99%

“…To estimate the heavy metal content in soil, spatial interpolation methods have been widely used. It can be divided into deterministic interpolation, radial basis function method [7,8], and geostatistical interpolation [9,10]. For example, Zhang et al [11] used the ordinary kriging method to estimate the spatial distribution of Cr in industrial areas and assess the risks to human health.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Heavy Metal Content in Soil Based on Machine Learning Models

Shi

Hou

et al. 2022

Land

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Heavy metal pollution in soil is threatening the ecological environment and human health. However, field measurement of heavy metal content in soil entails significant costs. Therefore, this study explores the estimation method of soil heavy metals based on remote sensing images and machine learning. To accurately estimate the heavy metal content, we propose a hybrid artificial intelligence model integrating least absolute shrinkage and selection operator (LASSO), genetic algorithm (GA) and error back propagation neural network (BPNN), namely the LASSO-GA-BPNN model. Meanwhile, this study compares the accuracy of the LASSO-GA-BPNN model, SVR (Support Vector Regression), RF (Random Forest) and spatial interpolation methods with Huanghua city as an example. Furthermore, the study uses the LASSO-GA-BPNN model to estimate the content of eight heavy metals (including Ni, Pb, Cr, Hg, Cd, As, Cu, and Zn) in Huanghua and visualize the results in high resolution. In addition, we calculate the Nemerow index based on the estimation results. The results denote that, the simultaneous optimization of BPNN by LASSO and GA can greatly improve the estimation accuracy and generalization ability. The LASSO-GA-BPNN model is a more accurate model for the estimate heavy metal content in soil compared to SVR, RF and spatial interpolation. Moreover, the comprehensive pollution level in Huanghua is mainly low pollution. The overall spatial distribution law of each heavy metal content is very similar, and the local spatial distribution of each heavy metal is different. The results are of great significance for soil pollution estimation.

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“…In general, if the total number of observations is lower than 15 or 20, it is difficult to construct a reliable variogram function in kriging-based models. In practical spatial prediction issues, more observations and hybrid approaches are required in kriging-based models due to the common uneven distributions of samples [39,40]. The uneven distributions of samples are also a critical issue for the air pollution data, including particulate matter, where samples are generally clustered in central urban regions and are sparse in rural and remote areas [38].…”

Section: Literature Reviewmentioning

confidence: 99%

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

Song

2022

Remote Sensing

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Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations.

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Comparative suitability of ordinary kriging and Inverse Distance Weighted interpolation for indicating intactness gradients on threatened savannah woodland and forest stands

Cited by 40 publications

References 35 publications

Estimating soil loss by laminar erosion using precision agriculture computational tools

Estimating soil loss by laminar erosion using precision agriculture computational tools

Estimation of Heavy Metal Content in Soil Based on Machine Learning Models

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

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