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Novel ensemble approach of deep learning neural network (Dlnn) model and particle swarm optimization (pso) algorithm for prediction of gully erosion susceptibility
Sensors (Switzerland) Số 19, năm 2020 (Tập 20, trang 1-28)
ISSN: 14248220
ISSN: 14248220
DOI: 10.3390/s20195609
Tài liệu thuộc danh mục: ISI, Scopus
Article
English
Từ khóa: Deep neural networks; Erosion; Forecasting; Land use; Landforms; Learning algorithms; Learning systems; Lithology; Neural networks; Particle swarm optimization (PSO); Personnel training; Statistical tests; Support vector machines; Swarm intelligence; Area under the curves; Ensemble approaches; Independent variables; Learning neural networks; Particle swarm optimization algorithm; Receiver operating characteristics; Stream power index; Topographic wetness index; Deep learning
Tóm tắt tiếng anh
This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon. � 2020 by the authors. Licensee MDPI, Basel, Switzerland.
