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This study was performed to examine the separate and simultaneous influence of predictive models’ choice alongside sample ratios selection in soil organic matter (SOM). The research was carried out in northern Morocco, characterized by relatively cold weather and diverse geological conditions. The dataset herein used accounted for 1591 soil samples, which were randomly split into the following ratios: 10% (∼150 sample ratio), 20% (∼250 sample ratio), 35% (∼450 sample ratio), 50% (∼600 sample ratio) and 95% (∼1200 sample ratio). Models herein involved were ordinary kriging (OK), regression kriging (RK), multiple linear regression (MLR), random forest (RF), quantile regression forest (QRF), Gaussian process regression (GPR) and an ensemble model. The findings in the study showed that the accuracy of SOM prediction is sensitive to both predictive models and sample ratios. OK combined with 95% sample ratio performed equally to RF in conjunction with all the sample ratios, as the latter did not show much sensitivity to sample ratios. ANOVA results revealed that RF with a ∼10% sample ratio could also be optimum for predicting SOM in the study area. In conclusion, the findings herein reported could be instrumental for producing cost-effective detailed and accurate spatial estimation of SOM in other sites. Furthermore, they could serve as a baseline study for future research in the region or elsewhere. Therefore, we recommend conducting series of simulation of all possible combinations between various predictive models and sample ratios as a preliminary step in soil organic matter prediction.

Soil organic carbon (SOC) contents and stocks provide valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. Given this, remote sensing data coupled with advanced machine learning (ML) techniques have eased SOC level estimation while revealing its patterns across different ecosystems. However, despite these advances, the intricacies of training reliable and yet certain SOC models for specific end-users remain a great challenge. To address this, we need robust SOC uncertainty quantification techniques. Here, we introduce a methodology that leverages conformal prediction to address the uncertainty in estimating SOC contents while using remote sensing data. Conformal prediction generates statistically reliable uncertainty intervals for predictions made by ML models. Our analysis, performed on the LUCAS dataset in Europe and incorporating a suite of relevant environmental covariates, underscores the efficacy of integrating conformal prediction with another ML model, specifically random forest. In addition, we conducted a comparative assessment of our results against prevalent uncertainty quantification methods for SOC prediction, employing different evaluation metrics to assess both model uncertainty and accuracy. Our methodology showcases the utility of the generated prediction sets as informative indicators of uncertainty. These sets accurately identify samples that pose prediction challenges, providing valuable insights for end-users seeking reliable predictions in the complexities of SOC estimation.

Abstract 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.

Abstract Unhealthy soils in peri-urban and urban areas expose individuals to potentially toxic elements (PTEs), which have a significant influence on the health of children and adults. Hundred and fifteen (n = 115) soil samples were collected from the district of Frydek Mistek at a depth of 0–20 cm and measured for PTEs content using Inductively coupled plasma—optical emission spectroscopy. The Pearson correlation matrix of the eleven relevant cross-correlations suggested that the interaction between the metal(loids) ranged from moderate (0.541) correlation to high correlation (0.91). PTEs sources were calculated using parent receptor model positive matrix factorization (PMF) and hybridized geostatistical based receptor model such as ordinary kriging-positive matrix factorization (OK-PMF) and empirical Bayesian kriging-positive matrix factorization (EBK-PMF). Based on the source apportionment, geogenic, vehicular traffic, phosphate fertilizer, steel industry, atmospheric deposits, metal works, and waste disposal are the primary sources that contribute to soil pollution in peri-urban and urban areas. The receptor models employed in the study complemented each other. Comparatively, OK-PMF identified more PTEs in the factor loadings than EBK-PMF and PMF. The receptor models performance via support vector machine regression (SVMR) and multiple linear regression (MLR) using root mean square error (RMSE), R square (R2) and mean square error (MAE) suggested that EBK-PMF was optimal. The hybridized receptor model increased prediction efficiency and reduced error significantly. EBK-PMF is a robust receptor model that can assess environmental risks and controls to mitigate ecological performance.

This current study investigated the relationship between soil organic carbon (SOC) and terrain derivatives on soil developed on dissimilar lithology while comparing the best modelling approach. Sixty (n = 60) bulk soil samples were taken from the depth of 0–30 cm according to five identified basement complex materials and analyzed for SOC. The models considered are ordinary kriging (OK), principal components kriging (PCA_OK) and regression kriging (RK). For the prediction's accuracy, 2-fold, and leave one out (LOOCV) cross-validation was carried out. The study indicated SOC in soil developed on granite gneiss (2.12%) and Biottite hornblende gneiss (2.05%) to be significantly (p

Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty (n = 60) soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley–Black method. From these samples, 80% were used for model training and 21 auxiliary data were included as predictors. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), calcium to magnesium ratio (Ca_Mg), potassium to magnesium ratio (K_Mg), potassium to calcium ratio (K_Ca), elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST). Mean absolute error (MAE), root-mean-square error (RMSE) and R2 were used to determine the model performance. The result showed the mean SOC to be 1.62% with a coefficient of variation (CV) of 47%. The best performing model was RF (R2 = 0.68) followed by the cubist model (R2 = 0.51), SVM (R2 = 0.36), ANN (R2 = 0.36) and MLR (R2 = 0.17). The soil nutrient indicators, topographic wetness index and total catchment area were considered an indicator for spatial prediction of SOC in flat homogenous topography. Future studies should include other auxiliary predictors (e.g., soil physical and chemical properties, and lithological data) as well as cover a broader range of soil types to improve model performance.

Soil organic carbon (SOC) contents and stocks provide valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. Given this, remote sensing data coupled with advanced machine learning (ML) techniques have eased SOC level estimation while revealing its patterns across different ecosystems. However, despite these advances, the intricacies of training reliable and yet certain SOC models for specific end-users remain a great challenge. To address this, we need robust SOC uncertainty quantification techniques. Here, we introduce a methodology that leverages conformal prediction to address the uncertainty in estimating SOC contents while using remote sensing data. Conformal prediction generates statistically reliable uncertainty intervals for predictions made by ML models. Our analysis, performed on the LUCAS dataset in Europe and incorporating a suite of relevant environmental covariates, underscores the efficacy of integrating conformal prediction with another ML model, specifically random forest. In addition, we conducted a comparative assessment of our results against prevalent uncertainty quantification methods for SOC prediction, employing different evaluation metrics to assess both model uncertainty and accuracy. Our methodology showcases the utility of the generated prediction sets as informative indicators of uncertainty. These sets accurately identify samples that pose prediction challenges, providing valuable insights for end-users seeking reliable predictions in the complexities of SOC estimation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Human activities considerably contribute to polluting potentially toxic element (PTEs) levels in soils, especially agricultural soils. The consistent introduction of PTEs in the environment and the soil pose health-related risks to humans, flora and fauna. One hundred and fifteen samples were collected in the district of Frydek Mistek (Czech Republic) in a regular grid form. The soil samples were air-dried, and the concentrations of PTEs (i.e. lead, arsenic, chromium, nickel, manganese, cadmium, copper, and zinc) were determined by ICP-OES (inductively coupled plasma optical emission spectrometry). The purpose of this study is to create digitized soil maps that expose the human-related health risks posed by PTEs, estimate pollution indices, ascertain the spatially distributed patterns of PTEs, source apportionment and quantify carcinogenic and non-carcinogenic health risks using the sample location approach. Results: The results revealed that the pollution assessment of the soils in the study area using diverse pollution assessment indexes (pollution index, pollution load index, ecological risk and risk index), based on the application of the local background value and the European average value, displayed a range of pollution levels due to differences in the threshold limits from differing geochemical background levels. The principal components analysis and positive matrix factorization, respectively, identified the sources of pollution and the distribution of PTE sources. Mapping the health index and total carcinogenic risk highlighted hotspots of areas within the study area that require immediate remediation. The self-organizing map (SeOM) revealed a diversified colour pattern for the factor scores. A single neuron exhibited a high hotspot in all factor loadings on different blocks of neurons. Children's CDItotal (Chronic Daily Intake total) values for non-carcinogenic risk and carcinogenic risk were found to be greater than adults', as were their HQ (hazard quotients) and CR (carcinogenic risk) values. According to the health index of non-carcinogenic risk, 6.1% of the study area sampled posed a potential risk to children rather than adults. Corresponding to the sampled pointwise health risk assessment, 13.05% of the sampled locations are carcinogenic to children. The estimated health risk in the agricultural soil was high, with both carcinogenic and non-carcinogenic risks that could threaten persons living in the study area, particularly children. Conclusion: In general, the continuous application of agriculturally related inputs such as phosphate fertilizers and other anthropogenic activities (e.g., steel industry) can increase the level of PTEs in soils. The use of mean, maximum, and minimum values in health risk estimation does not provide a comprehensive picture of a research area's health state. This study recommends using a sampled pointwise or location health risks assessment approach, which allows researchers to identify high-risk environments that exceeds the recommended threshold as well as areas on the verge of becoming high risk, allowing for rapid remedial action. [ABSTRACT FROM AUTHOR]