Predicting slope safety using an optimized machine learning model.

The hazards and consequences of slope collapse can be reduced by obtaining a reliable and accurate prediction of slope safety, hence, developing effective tools for foreseeing their occurrence is crucial. This research aims to develop a state-of-the-art hybrid machine learning approach to estimate the factor of safety (FOS) of earth slopes as precisely as possible. The current research's contribution to the body of knowledge is multifold. In the first step, a powerful optimization approach based on the artificial electric field algorithm (AEFA), namely the global-best artificial electric field algorithm (GBAEF), is developed and verified using a number of benchmark functions. The aim of the following step is to utilize the machine learning technique of support vector regression (SVR) to develop a predictive model to estimate the slope's safety factor (FOS). Finally, the proposed GBAEF is employed to enhance the performance of the SVR model by appropriately adjusting the hyper-parameters of the SVR model. The model implements 153 data sets, including six input parameters and one output parameter (FOS) collected from the literature. The outcomes show that implementing efficient optimization algorithms to adjust the hyper-parameters of the SVR model can greatly enhance prediction accuracy. A case study of earth slope from Chamoli District, Uttarakhand is used to compare the proposed hybrid model to traditional slope stability techniques. According to experimental findings, the new hybrid AI model has improved FOS prediction accuracy by about 7% when compared to other forecasting models. The outcomes also show that the SVR optimized with GBAEF performs wonderfully in the disciplines of training and testing, with a maximum R(2) of 0.9633 and 0.9242, respectively, which depicts the significant connection between observed and anticipated FOS.