## 1 Introduction
Some examples of the application of database or data-driven algorithms in pipeline assessment are as follows. [PERSON] and [PERSON] (2016) developed a criteria-dependent Poisson model as a quantitative integration method for various pipeline databases to improve pipeline reliability [14]. [PERSON] et al. (2020) developed a hybrid AI model for the prediction of stress intensity factors [15]. The continuation of work from [PERSON] et al. (2021) compared various AI models (artificial neural network, M5 tree, multivariate adaptive regression splines, locally weighted polynomials, kriging, and extreme learning machines) for predicting the maximum pitting corrosion depth in oil and gas pipelines [16]. [PERSON] et al. (2021) fused a spatiotemporal modeling approach with text mining to analyze the emergency severity in the natural gas distribution pipeline [17]. [PERSON] and [PERSON] (2021) analyzed pipeline incidents from a statistical perspective by determining a hierarchy for the causes of the incident, assigning weights for the effect posed by the incidents and establishing correlations between various parameters [18]. [PERSON] and [PERSON] (2018) performed data mining on the last 21 years of pipeline accident data from the United States and found that pipeline accidents due to natural force damage increase during the winter season [19]. Natural force damage is often referred to as outside force damage, which includes incidents caused by acts of nature such as earth movement, heavy rains, high winds, and extreme hot or cold temperatures [20]. Fluctuations in temperature can cause physical changes in materials, such as expansion and contraction, which can impact the integrity of pipelines. Although only a small percentage, approximately 3% of the overall pipeline incidents, were caused by natural forces in 2022 [6], their effect is often debilitating and may cause catastrophic failure across the energy industry [21]. The study presented in this paper aims to address the critical need to accurately forecast the natural force failure causes that impact natural gas pipelines. By incorporating comprehensive data on climate change into the analysis of pipeline incidents, this research represents a significant advancement in our ability to predict and mitigate the effects of natural force failures on pipeline infrastructure. This study leverages cutting-edge data-driven analysis and sophisticated artificial intelligence (AI) or machine learning algorithms to provide actionable insights into the potential impact of natural force failure on pipeline infrastructure. AI algorithms can be used to predict pipeline damage caused by natural forces by analyzing various features such as pipeline characteristics, environmental conditions, and geographical location [22]. By analyzing these factors, AI algorithms can identify patterns and correlations that humans may not be able to detect, allowing for more accurate predictions of pipeline damage. Additionally, AI can be used to optimize pipeline maintenance schedules by predicting when and where pipeline damage is most likely to occur, allowing for proactive maintenance and potentially reducing the likelihood of damage occurring in the first place [23]. Finally, AI approaches can also be used to monitor pipelines in real time, identifying potential damage as soon as it occurs and alerting operators to take appropriate action. Although not all of the above-mentioned factors will be addressed in this paper, the main contributions of this paper include: (1) the proposed approach of incorporating climate change data into pipeline incident data, using artificial intelligence algorithms to predict pipeline failures caused by natural forces, (2) the high prediction accuracy achieved by the proposed model for natural gas pipeline damage caused by natural forces, and (3) the emphasis on the importance of enhancing the understanding of the complex interactions between climate change and pipeline infrastructure monitoring and maintenance.