GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran

Abstract

In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first decision tree (BFTree). To the best of our knowledge, the KLR and CDTree algorithms have been rarely applied to gully erosion modeling. In the first step, from the 242 gully erosion locations that were identified, 70% (170 gullies) were selected as the training dataset, and the other 30% (72 gullies) were considered for the result validation process. In the next step, twelve gully erosion conditioning factors, including topographic, geomorphological, environmental, and hydrologic factors, were selected to estimate gully erosion susceptibility. The area under the ROC curve (AUC) was used to estimate the performance of the models. The results revealed that the RF model had the best performance (AUC = 0.893), followed by the KLR (AUC = 0.825), the CDTree (AUC = 0.808), and the BFTree (AUC = 0.789) models. Overall, the RF model performed significantly better than the others, which may support the application of this method to a transferable susceptibility model in other areas. Therefore, we suggest using the RF, KLR, and CDT models for gully erosion susceptibility mapping in other prone areas to assess their reproducibility.

A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

“…Regarding the validation of the LS model via FR model, the success rate curve was used. It is an estimation of the success of a model that indicates how well the model matches the prior events (Lei et al 2020a(Lei et al , 2020bChen et al 2021aChen et al , 2021bChen et al , 2021c. Success rate curve was created by using training data and LS map (Chen et al 2021a(Chen et al , 2021b(Chen et al , 2021c.…”

“…The RFC model has been used in several LS studies, such as Chen et al (2018aChen et al ( , 2018b, Sevgen et al (2019), Nsengiyumva and Valentino (2020), Kocaman et al (2020) or Zhao et al (2020). Similarly to RFC model, Naïve Bayes classifier (NBC), a supervised probabilistic algorithm built on Bayes theorem, have been applied in several studies in recent years (Tsangaratos and Ilia 2016;He et al 2019;Chen et al 2020aChen et al , 2020bLee et al 2020;Lei et al 2020aLei et al , 2020b. XGBoost is a scalable tree boosting system built on the basis of gradient boosting in order to reduce computational burden.…”

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

“…Best-first decision tree (BFTree) is proven to be a remarkable member inter tree-based algorithms (Chen et al, 2018b). The most important feature of BFTree is that the best node should expand in depth-first order (Nguyen et al, 2019b;Lei et al, 2020a). The best node could be determined by measuring information gain and Gini index, which are used to assess node impurity (Lay et al, 2019;.…”

An Overview of Flood Risk Analysis Methods

“…A current trend in research and hydrological models includes real-time flood economic losses. The estimates are based on geospatial data to delineate the areas and targets affected by floods [18,[20][21][22]. This article aims to present current flood analysis methods, highlighting the methodologies used, the research areas, the progress made in this field, and the accessibility of information.…”