Abstract
A suitable method and appropriate environmental variables are important for accurately predicting heavy metal distribution in soils. However, the classical methods (e.g., ordinary kriging (OK)) have a smoothing effect that results in a tendency to neglect local variability, and the commonly used environmental variables (e.g., terrain factors) are ineffective for improving predictions across plains. Here, variables were derived from the obvious factors affecting soil cadmium (Cd), such as road traffic, and were used as auxiliary variables for a combined method (HASM_RBFNN) that was developed using high accuracy surface modelling (HASM) and radial basis function neural network (RBFNN) model. This combined method was then used to predict soil Cd distribution in a typical area of Chengdu Plain in China, considering the spatial non-stationarity of the relationships between soil Cd and the derived variables based on 339 surface soil samples. The results showed that HASM_RBFNN had lower prediction errors than OK, regression kriging (RK) and HASM_RBFNNs, which didn’t consider the spatial non-stationarity of the soil Cd-derived variables relationships. Furthermore, HASM_RBFNN provided improved detail on local variations. The better performance suggested that the derived environmental variables were effective and HASM_RBFNN was appropriate for improving the prediction of soil Cd distribution across plains.
Highlights
Heavy metals in the soil are crucial factors of environmental and food quality and can threaten human health through the food chain[1, 2]
A combined method is developed based on the premise that the deterministic component of the targeted soil variable caused by correlated environmental factors can be explained by a regression model while the spatially varying but dependent component can be described by the prediction residuals of the linear regression model and captured by the classical methods such as ordinary kriging (OK)[6, 13, 15,16,17,18,19]
Recent studies have found that the radial basis function neural network (RBFNN) approach can perform better than MLR due to its capacity to capture the complex relationships between soils and the environmental factors[12, 15], and a new approach, called high accuracy surface modelling (HASM), developed on the basis of a fundamental theorem of surfaces by Yue et al.[23,24,25,26], can outperform the three classical methods for predicting soil properties[16, 22]
Summary
Heavy metals in the soil are crucial factors of environmental and food quality and can threaten human health through the food chain[1, 2]. Recent studies have found that the radial basis function neural network (RBFNN) approach can perform better than MLR due to its capacity to capture the complex relationships between soils and the environmental factors[12, 15], and a new approach, called high accuracy surface modelling (HASM), developed on the basis of a fundamental theorem of surfaces by Yue et al.[23,24,25,26], can outperform the three classical methods for predicting soil properties[16, 22] Both approaches provide new tools for predicting soil heavy metal distributions across plains based on the methodological framework of the combined methods
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