Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals

By A. Sergeev, A. Buevich, E. Baglaeva, A. Shichkin. CATENA, 2019

94 citations recorded by the source corpus.

Abstract A hybrid approach was proposed to simulate the spatial distribution of a number of heavy metals in the surface layer of the soil. The idea of the method is to simulate a nonlinear large-scale trend using an artificial neural network (ANN) and the subsequent modelling of the residuals by geostatistical methods. A comparison was made with the basic modelling methods based on ANN: generalised regression neural network (GRNN) and multilayer perceptron (MLP). The raw data for the surface layer modelling of Cuprum (Cu), Manganese (Mn) and Niccolum (Ni) were obtained as a result of the soil screening in the subarctic city Novy Urengoy, Russia. The ANN structures were selected by the computer simulation based on the root mean square error (RMSE) minimization. The predictive accuracy of each selected approach was verified by the correlation coefficient, the coefficient of determination, RMSE, Willmott's index of agreement (d), a ratio of performance to interquartile distance (RPIQ) between the prediction and raw data from the test data set. It was confirmed that the use of the hybrid approach provides an increase in prediction accuracy in comparison with the basic ANN models. The proposed hybrid approach for each element showed the best predictive accuracy in comparison with other models based on ANN.

Subfields: Applied Mathematics, Artificial Intelligence, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/a71e015f1ec320ea496548c54c69aa1d87df838c

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In Applied Mathematics

In Artificial Intelligence

In Environmental Engineering

Citations in the corpus

Machine learning for digital soil mapping: Applications, challenges and suggested solutions (2020, methodology): “When specified, non-probability sampling such as grid-based sampling designs are by far the most used for calibration (e.g. by Pahlavan-Rad and Akbarimoghaddam, 2018; Sergeev et al., 2019; Sharififar et al., 2019).”