Government Engineering College, Sheohar

This study proposed a hybrid machine learning framework integrated with reliability analysis to predict the factor of safety against liquefaction and quantify the associated uncertainties. Three optimized Extreme Gradient Boosting models, XGB-AOA, XGB-ESMA, and XGB-MPA, were developed and evaluated using comprehensive statistical, graphical, and probabilistic analyses. Model performance was assessed through actual and predicted comparisons, multi-criteria score evaluation, Regression Error Characteristic curves, Anderson-Darling testing, internal k-fold cross-validation, and external validation. Reliability-based methods, including the first-order reliability method, second-order reliability method, and higher-order approximations, were applied to estimate the probability of failure and reliability index. Model interpretability was investigated using SHapley Additive explanations (SHAP) to identify the most influential variables governing liquefaction resistance. Results indicated that the XGB-ESMA model achieved superior prediction accuracy, robustness, and generalisation capability compared with XGB-MPA and XGB-AOA. Reliability analysis revealed a high probability of failure (approximately 60–64

This study developed an integrated machine learning framework for predicting the factor of safety (FOS) of soil slopes using Extreme Gradient Boosting (XGB) optimized with three metaheuristic algorithms: Particle Swarm Optimization (PSO), Tiki Taka Optimization Algorithm (TTOA), and Whale Optimization Algorithm (WOA). A dataset comprising six influential parameters, namely cohesion, unit weight, friction angle, slope angle, slope height, and pore pressure ratio, was employed for model development. The dataset was normalized and divided into training and independent testing subsets, while five-fold cross validation was adopted during model optimization to improve robustness and reduce overfitting. Model performance was assessed using multiple statistical indicators, scatter plots, Regression Error Characteristic curves, and Taylor diagrams. The results demonstrate that all optimized XGB models effectively captured the nonlinear relationship between the input parameters and FOS, while the proposed framework provides an accurate, interpretable, and computationally efficient tool. To improve model transparency, SHAP, LIME, and permutation feature importance analyses were incorporated. The explainability results identified slope angle, cohesion, and slope height as the dominant predictors, consistent with their governing roles in slope geometry and shear strength. The proposed framework provides an accurate, interpretable, and computationally efficient tool for data driven slope stability assessment.

Wireless Sensor Networks (WSNs) consist of many geographically distributed sensor nodes that sense, record, and communicate environmental data to a central base station. However, the limited energy resources of sensor nodes lead to frequent energy depletion, thereby reducing network stability and lifetime. This energy constraint remains a critical challenge in the efficient deployment of WSNs. To overcome these issues, this study introduces a hybrid optimisation technique that integrates the Improved Butterfly Optimisation Algorithm (I-BOA) with Ant Colony Optimisation (ACO) for energy-efficient clustering and intelligent routing. The I-BOA algorithm selects the optimal cluster heads (CHs) based on residual energy, node degree, centrality, and distance metrics, ensuring balanced energy consumption across the network. Meanwhile, the ACO algorithm determines the most efficient data-forwarding paths from CHs to relay nodes and, ultimately, to the base station, by considering distance, residual energy, and node degree. The proposed I-BOA + ACO model is evaluated against conventional algorithms, including MOPSO, HSA-PSO, M-LEACH, and DEEC, under identical network conditions. Simulation results reveal that the proposed method achieves a 23.8