###### Abstract
When dealing with noisy or large-scale data, BN may face computational complexity or even incompatibility. Compared to the traditional BN method, Fuzzy DFT can better handle the uncertainty dependencies and fuzzy information present in the system [18], which makes it more robust in practical applications, especially in the context of storage tank accidents [19]. Additionally, although Markov Chains (MC) and Monte Carlo simulation perform well in simulating system state transitions, they require significant computational resources and are often unable to meet real-time risk assessment needs, particularly when dealing with high-dimensional data [20; 21]. In contrast, ANNs, with their powerful nonlinear modeling capabilities, can effectively capture complex system behaviors and long-range dependencies, thus demonstrating higher accuracy in risk assessment tasks [22; 23; 24]. Compared to BN, neural networks do not need to assume that the variables are independent of each other when building the model. The neural network model has a data-driven feature that enables it to deal with unknown nonlinear dependencies more easily. In addition, neural networks can learn and optimize the model automatically and perform particularly well when dealing with large-scale or imprecise data, leading to reliable results. Currently, other subject areas have begun to apply neural networks for accident risk research. For example, [PERSON] et al. [25] developed a comprehensive accident analysis model that organically integrated the Human Factors Analysis and Classification System (HFACS) and ANN to accurately quantify the failure probabilities caused by human factors in maritime accidents, providing a new perspective and method for maritime accident risk assessment. [PERSON] et al. [17] innovatively adopted the technical approach of mapping fault tree (FT) to ANN and successfully applied it to the probability analysis of process systems. This method effectively overcame the shortcomings of traditional fault trees in dealing with system complexity and complex calculation processes, significantly improving the accuracy and efficiency of probability analysis for process systems. [PERSON] et al. [26] proposed a probability analysis method for mining systems based on FST and machine learning, skillfully solving the key problem of data uncertainty in probabilistic risk analysis and providing more reliable technical support for risk assessment in the mining industry. However, the current research status of dynamic probability analysis still has certain limitations. Most research methods often only focus on local factors and fail to comprehensively consider and properly address all aspects involved in accident probability analysis from a global perspective. Especially in the important field of probability analysis of storage tank leakage accidents, research on probability analysis using neural networks is relatively scarce. This study proposes a probability analysis method for accident risk based on Fuzzy DFT and neural network technologies. Unlike traditional methods, which often face the issue of data scarcity, the proposed method calculates the failure probabilities of BEs using FST and introduces the Bootstrap algorithm to augment risk data, creating a more comprehensive dataset for probabilistic risk analysis. This not only alleviates the problem of insufficient data in the quantitative analysis of storage tank accidents but also reduces reliance on experts' subjective judgments through a data-driven approach. In terms of accident scenario analysis, traditional FT methods may overlook the dynamic nature of accident development. However, the method proposed in this paper utilizes DFT to comprehensively capture these dynamic features, enabling a more accurate reflection of risk state changes.