ADVANCED COMPUTATIONAL METHODS FOR DAM FAILURE PREDICTION: A REVIEW OF DEEP LEARNING, MACHINE LEARNING, AND STATISTICAL MODELS

This paper presents a comparative analysis of various data-driven approaches for Sardoba Dam settlement and failure prediction, including statistical, machine learning, deep learning, hybrid, and fuzzy-based models. The selected methods—ARIMA, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), CNN–LSTM hybrid models, Adaptive Neuro-Fuzzy Inference System (ANFIS), Type-2 Fuzzy Logic, and ensemble/regression techniques such as Random Forest (RF) and Support Vector Regression (SVR)—are evaluated in terms of predictive accuracy, robustness, interpretability, and applicability to complex dam deformation scenarios. The study highlights the strengths and limitations of each approach, showing that while traditional statistical models like ARIMA capture linear temporal trends effectively, deep learning and hybrid models (ANN, LSTM, CNN–LSTM, ANN–ARIMA) provide superior performance in modeling nonlinear and time-dependent behaviors. Fuzzy-based systems, including ANFIS and Type-2 fuzzy logic, offer advantages in handling uncertainty and imprecise data. Ensemble methods such as RF and regression-based SVR provide reliable predictions under noisy or limited datasets. Through this analysis, the paper aims to identify the most effective modeling frameworks for accurate and early prediction of Sardoba Dam settlement and potential failure.