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Susceptibility mapping of soil water erosion using machine learning models
Water (Switzerland) Số 7, năm 2020 (Tập 12, trang -)
ISSN: 20734441
ISSN: 20734441
DOI: 10.3390/w12071995
Tài liệu thuộc danh mục: ISI, Scopus
Article
English
Từ khóa: Agricultural robots; Agriculture; Barium compounds; Decision trees; Erosion; Lithology; Machine learning; Mapping; Simulated annealing; Soil moisture; Statistical tests; Sustainable development; Textures; Water conservation; Hydrologic soil groups; Machine learning models; Normalized difference vegetation index; Probability of detection; Radial basis function kernels; Susceptibility mapping; Sustainable agriculture; Sustainable management; Soil conservation; erosivity; hazard assessment; machine learning; numerical model; simulated annealing; soil erosion; water erosion
Tóm tắt tiếng anh
Soil erosion is a serious threat to sustainable agriculture, food production, and environmental security. The advancement of accurate models for soil erosion susceptibility and hazard assessment is of utmost importance for enhancing mitigation policies and laws. This paper proposes novel machine learning (ML) models for the susceptibility mapping of the water erosion of soil. The weighted subspace random forest (WSRF), Gaussian process with a radial basis function kernel (Gaussprradial), and naive Bayes (NB) ML methods were used in the prediction of the soil erosion susceptibility. Data included 227 samples of erosion and non-erosion locations through field surveys to advance models of the spatial distribution using predictive factors. In this study, 19 effective factors of soil erosion were considered. The critical factors were selected using simulated annealing feature selection (SAFS). The critical factors included aspect, curvature, slope length, flow accumulation, rainfall erosivity factor, distance from the stream, drainage density, fault density, normalized difference vegetation index (NDVI), hydrologic soil group, soil texture, and lithology. The dataset cells of samples (70% for training and 30% for testing) were randomly prepared to assess the robustness of the different models. The functional relevance between soil erosion and effective factors was computed using the ML models. The ML models were evaluated using different metrics, including accuracy, the kappa coefficient, and the probability of detection (POD). The accuracies of the WSRF, Gaussprradial, and NB methods were 0.91, 0.88, and 0.85, respectively, for the testing data; 0.82, 0.76, and 0.71, respectively, for the kappa coefficient; and 0.94, 0.94, and 0.94, respectively, for POD. However, the ML models, especially the WSRF, had an acceptable performance regarding producing soil erosion susceptibility maps. Maps produced with the most robust models can be a useful tool for sustainable management, watershed conservation, and the reduction of soil and water loss. � 2020 by the authors.
