A novel soil liquefaction prediction model with intellectual feature extraction and classification. (November 2022) - Reddy, Nerusupalli Dinesh Kumar; Gupta, Ashok Kumar; Sahu, Anil Kumar
Highlights: The highlights of the article are given below for your kind perusal. Kindly, consider and forward my article for further processes. Establishes new soil liquefaction prediction model, where improved correlation features, chi square, relief features and technical indicators are derived. Deploys ensemble classifiers (DBN, LSTM and SVM) with optimized Bi-GRU for precise prediction results. Proposes a novel AC-SSO model for choosing the optimal weights in Bi-GRU. Abstract: This paper introduced a novel soil liquefaction prediction model with proposed features. Initially, pre-processing was carried out and then diverse improved features (chi-square features, relief features, technical indicators, and improved correlation-based features) were derived that were then predicted via SVM, LSTM, and DBN. The attained outputs from these classifiers were then predicted via optimized Bi-GRU that offered the final output. In particular, the Bi-GRU weights were tuned optimally via a novel AC-SSO model. Eventually, the primacy of the presented method was confirmed over existing schemes about varied measures. On analyzing the results, the presented scheme has achieved the slightest cost value (approximately 1.11), which was 1.35%, 0.36%, 0.63%, and 0.63% better than SSA, CSO, and GWO models. Thus, the excellence of the established approach was proven.