Edge-Centric Intelligent Early Warning System for Residual Soil Stability Prediction in Slope

Abstract

Different geospatial and geotechnical parameters change over time and can affect the residual soil stability on a slope. Thus, it is essential to analyze the stability of slopes continuously to identify the potential landslide sections. The stability of slopes is defined by the factor of safety (FOS). To track the immediate changes in soil stability, it is essential to monitor multiple environmental parameters using Internet of Things (IoT) devices for real-Time decision making. Further, the relation between the environmental parameters and FOS is nonlinear which makes it a multivariate and complex problem. To motivate the above-mentioned challenges, in this article, we propose a new fusion-based bag-of-neural network (FuBoNN) model for predicting FOS using a set of IoT devices in edge networks. Besides that, for increasing the prediction accuracy of FOS, multiple laboratory data related to FOS are fused over the monitoring parameters and prepare a rich data set. The newly created fused data set is fed into the population-based neural network (NN) and the best NN is selected in each iteration that transfers its knowledge to the population. The fused data set is categorized into four class labels to simulate the stability issue of residual soil and fed to the input of the proposed FuBoNN model, which provides a 0.0003% of error in predicting the multiple categories of the FOS. The proposed work is compared to the standard machine learning models that demonstrate the efficiency of the proposed model and produce 2.5% improved prediction accuracy over the existing ones. © 2014 IEEE.