Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

Abstract

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.

Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

“…Among the several types of water induced soil erosion, gully erosion is the most destructive form, in terms of its negative impact on humankind throughout the world (Arabameri, Asadi Nalivan, et al 2020). The water induced gully erosion is responsible for several natural causes but recently, human interventions on the environment and associated changes of the equilibrium of ecosystem also influences the formation and development of gullies (Chaplot et al 2005;Band, Janizadeh, Chandra Pal, Saha, Chakrabortty, Shokri, et al 2020).…”

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

“…Therefore, several researchers proposed various kinds of models to address this key challenges and there has been great interest in improving the prediction performance of the hazards related susceptibility mapping through ML models (Oh et al, 2019). It is also be mentioned here that no specific models can give an optimal result and have controversy among the researchers in this regards (Arabameri et al, 2020a). The reliability and accurate result is the most predominate condition for the LS hazard susceptibility mapping and researchers tried to form new novel ensemble models to produced good outcomes (Reichenbach et al, 2018).…”

Influence of Topographic Factors on the Characteristics of Gully Systems in Mountainous Areas of Ningnan Dry-Hot Valley, SW China

“…The understanding of gully erosion damage is reflected in the calculation of the gully erosion amount and erosion rate [ 6 , 7 ]. At present, studies on gully erosion have been expanded from gully development and erosion mechanism [ 8 , 9 ], influencing factors and prevention measures to the studies on gully methods and models [ 10 , 11 , 12 ], and the effects of the ecological environment [ 13 ]. Studies of gully erosion mostly use the gully length, width, depth [ 14 , 15 ], density, and erosion modulus to describe the severity of gully development [ 16 ].…”