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Machine learning for digital soil mapping: Applications, challenges and suggested solutions

An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions.

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.

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

High variation topsoil pollution forecasting in the Russian Subarctic: Using artificial neural networks combined with residual kriging

Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration ranges of nitrate and pesticide. This study evaluates the classification performance of the models for two, three, and four class scenarios and compares them with the corresponding regression models. The study also examines the issue of class imbalance and tests the efficacy of three class imbalance mitigation techniques: oversampling, weighting, and oversampling and weighting, for all the scenarios. The models' performance is reported using multiple metrics, both insensitive to class imbalance (accuracy) and sensitive to class imbalance (F1 score and MCC). Finally, the study assesses the importance of features using game-theoretic Shapley values to rank features consistently and offer model interpretability.

Comparative evaluation of machine learning models for groundwater quality assessment.

Case of soil surface chromium anomaly of a northern urban territory - preliminary results.

Forecasting of environmental pollutants spatial distribution is a significant field of research in view of the current concerns regarding environment all over the world. Due to the danger to health and environment associated with an increase in pollution of air, soil, water and biosphere, it is very important to have the models that are capable to describe the modern distribution of contaminants and to forecast the dynamic of their spreading in future at different territories. This article addresses the methods, which applied the most often in this field, with an accent on soil pollution. The possible direction of such methods further development is suggested.

Review and possible development direction of the methods for modeling of soil pollutants spatial distribution

The paper discusses the influence of various methods for splitting raw data into test and training subsets on the accuracy of the prediction of the spatial distribution of the variable for the model based on a multilayer perceptron. A comparison of models was performed taking into account both spatial heterogeneity and the spread of values of the modeled variable with a completely random splitting option. The study was based on the results obtained during soil screening of urbanized territories in the Russian subarctic zone, Novy Urengoy city and Noyabrsk city. The spatial distribution of the chemical element chromium (Cr) in the surface layer of the soil was modeled. For both surveyed areas, the models in which a controlled splitting method was used for training demonstrated more accurate results than models trained using completely random splitting.

A. P. Sergeev

Papers

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

High variation topsoil pollution forecasting in the Russian Subarctic: Using artificial neural networks combined with residual kriging

Case of soil surface chromium anomaly of a northern urban territory - preliminary results.

Review and possible development direction of the methods for modeling of soil pollutants spatial distribution

The Effect of Splitting of Raw Data into Training and Test Subsets on the Accuracy of Predicting Spatial Distribution by a Multilayer Perceptron