Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics

John, Kingsley; Afu, Sunday Marcus; Isong, Isong Abraham; Aki, E.E.; Kebonye, Ndiye Michael; Ayito, Esther O.; Chapman, Prince Ageyman; Eyong, M. O.; Penížek, Vít

doi:10.1007/s13762-020-03089-x

Cited by 28 publications

(13 citation statements)

References 44 publications

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“…In the studied area the slope and aspect were the most effective factor affecting topographic quality and comprised together 0.85 of the total weights. This result is in line with those obtained in previous works [42,58,59]. The slope and aspect showed a wide range, and hence the area showed a wide variation in TQI ranging from very high to very low.…”

Section: Topographysupporting

confidence: 93%

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Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

et al. 2021

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Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for usingremote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security.

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Section: Topographysupporting

confidence: 93%

“…Topographic factors, including slope, aspect, curvature, and TWI play a great role in soil development, productivity, and resistance to various degradation processes [58,59]. In the studied area the slope and aspect were the most effective factor affecting topographic quality and comprised together 0.85 of the total weights.…”

Section: Topographymentioning

confidence: 99%

Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

et al. 2021

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“…The results of this study's RMSE and MAE for predicting Ni using EBK were found to be better than those of John et al 54 for predicting S concentration in soil using cokriging using the same collected data. The EBK output of our study is related to those of Fabijaczyk et al 41 , Yan et al 79 , Beguin et al 80 , Adhikary et al 81 , and John et al 82 , especially K and Ni.…”

Section: Resultssupporting

confidence: 65%

“…EBK is a geostatistical interpolation approach that utilizes a spatial stochastic process that is localized as a non-stationary/stationary random field with a defined localize parameter on the field that allows for space variation 39 . EBK has been applied in a variety of studies, including the analysis of the distribution of organic carbon in agrogray soils 40 , soil contamination assessment 41 and mapping soil properties 42 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of nickel concentration in peri-urban and urban soils using hybridized empirical bayesian kriging and support vector machine regression

Agyeman

Kebonye

John

et al. 2022

Sci Rep

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Soil pollution is a big issue caused by anthropogenic activities. The spatial distribution of potentially toxic elements (PTEs) varies in most urban and peri-urban areas. As a result, spatially predicting the PTEs content in such soil is difficult. A total number of 115 samples were obtained from Frydek Mistek in the Czech Republic. Calcium (Ca), magnesium (Mg), potassium (K), and nickel (Ni) concentrations were determined using Inductively Coupled Plasma Optical Emission Spectroscopy. The response variable was Ni, while the predictors were Ca, Mg, and K. The correlation matrix between the response variable and the predictors revealed a satisfactory correlation between the elements. The prediction results indicated that support vector machine regression (SVMR) performed well, although its estimated root mean square error (RMSE) (235.974 mg/kg) and mean absolute error (MAE) (166.946 mg/kg) were higher when compared with the other methods applied. The hybridized model of empirical bayesian kriging-multiple linear regression (EBK-MLR) performed poorly, as evidenced by a coefficient of determination value of less than 0.1. The empirical bayesian kriging-support vector machine regression (EBK-SVMR) model was the optimal model, with low RMSE (95.479 mg/kg) and MAE (77.368 mg/kg) values and a high coefficient of determination (R2 = 0.637). EBK-SVMR modelling technique output was visualized using a self-organizing map. The clustered neurons of the hybridized model CakMg-EBK-SVMR component plane showed a diverse colour pattern predicting the concentration of Ni in the urban and peri-urban soil. The results proved that combining EBK and SVMR is an effective technique for predicting Ni concentrations in urban and peri-urban soil.

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“…27 EBK has being applied in diverse research such as investigation the distribution of organic carbon in agrogray soils (Samsonova et al, 2017) soil contamination assessment 29 and mapping soil properties (John et al, 2021). 30 On the other hand self organisinising map (SeOM) is a learning algoritm that has been applied in diverse articles such as Li et al, 31 Wang et al, 32 Hossain Bhuiyan et al, 33 and Kebonye et al, 34 to determine the spatial attributes and grouping of elements. Wang et al, 32 outlined that SeOM is a vigorous learning technique known for its capacity in grouping and imagining that is allowed to deal with nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Nickel Concentrations in Urban and Peri-Urban Soils: Application of a Hybridized Empirical Bayesian Kriging and Support Vector Machine Regression Approach

Kebonye

John

2021

Preprint

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Soil pollution is a big issue caused by anthropogenic activities. The spatial distribution of potentially toxic elements (PTEs) varies in most urban and peri-urban areas. As a result, spatially predicting the PTEs content in such soil is difficult. A total number of 115 samples were obtained from Frydek Mistek in the Czech Republic. Calcium(Ca), magnesium(Mg), potassium(K), and nickel (Ni) concentrations were determined using Inductively Coupled Plasma Optical Emission Spectroscopy. The correlation matrix between the response variable and the predictors revealed a satisfactory correlation between the elements. The prediction results indicated that support vector machine regression (SVMR) performed well although its estimated root mean square error (RMSE) (235.974) and mean absolute error (MAE) (166.946) were higher when compared with the other methods applied. Conversely, the hybridized model of empirical bayesian kriging -multiple linear regression (EBK-MLR) performed poorly as indicated by the measured coefficient of determination value below 0.1. The empirical bayesian kriging-support vector machine regression (EBK-SVMR) model was the best model, with low RMSE (95.479) and MAE (77.368) values and a high coefficient of determination (R2 = 0.637). EBK-SVMR modeling technique was visualized using self-organizing map. The clustered neurons of the hybridized model CakMg -EBK-SVMR component plane showed a diverse color pattern predicting the concentration of Ni in the urban and peri urban soil. The results proved that combining EBK and SVMR is an effective technique for predicting Ni concentrations in urban and peri-urban soil.

show abstract

Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics

Cited by 28 publications

References 44 publications

Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

Prediction of nickel concentration in peri-urban and urban soils using hybridized empirical bayesian kriging and support vector machine regression

Prediction of Nickel Concentrations in Urban and Peri-Urban Soils: Application of a Hybridized Empirical Bayesian Kriging and Support Vector Machine Regression Approach

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