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Novel machine learning approaches for modelling the gully erosion susceptibility
Remote Sensing Số 17, năm 2020 (Tập 12, trang 1-32)
ISSN: 20724292
ISSN: 20724292
DOI: 10.3390/rs12172833
Tài liệu thuộc danh mục: ISI, Scopus
Article
English
Từ khóa: Agricultural robots; Economics; Erosion; Landforms; Learning algorithms; Neural networks; Support vector machines; Sustainable development; Topography; Water conservation; Conservation measures; Economic activities; Error of the models; General linear modeling; Machine learning approaches; Receiver operating characteristics; Semi-arid environments; Vulnerable regions; Learning systems
Tóm tắt tiếng anh
The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures. � 2020 by the authors. Licensee MDPI, Basel, Switzerland.
