###### Abstract  
Deep learning is crucial in marine logistics and container crane error detection, diagnosis, and prediction. A novel deep learning technique using Long Short-Term Memory (LSTM) detected and anticipated errors in a system with imbalanced data. The LSTM model was trained on real operational error data from container cranes. The custom algorithm employs the Synthetic Minority Oversampling TEthnique (SMOTE) to balance the imbalanced data for operational data errors (i.e., too few minority class samples). Python was used to program. Pearson, Spearman, and Kendall correlation matrices and covariance matrices are presented. The model's training and validation loss is shown, and the remaining data are predicted. The test set (30% of actual data) and forecasted data had RMSEs of 0.065. A heatmap of a confusion matrix was created using Matplotlib and Seaborn. Additionally, the error outputs for the time series for the next n seconds were projected, with the n seconds input by the user. Accuracy was 0.996, precision was 1.00, recall was 0.500, and f1 score was 0.667, according to the evaluation criteria that were produced. Experiments demonstrated that the technique is capable of identifying critical elements. Thus, future attempts will improve the model's structure to forecast industrial big data errors. However, the advantage is that it can handle imbalanced data, which is usually what most industries have. With additional data, the model can be further improved. p +
Footnote †: journal: _Algorithms_  
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transportation [4]. The cranes generate enormous amounts of data that can be analysed, and valuable insights can be obtained for efficient, better, and faster decision-making by industries. Recently, growing research has focused on data-driven fault diagnostic methods, particularly those based on neural networks. This is due to advancements in sensor technology and increased data storage capacity, which have enhanced the data processing capabilities of neural network-based methods. Failure to identify process flaws can have detrimental effects on productivity, safety, quality of output, and economy of the process. Efficient and advanced automated diagnostic equipment is crucial in modern enterprises to detect, diagnose, and repair abnormal process behaviours. The primary goal of fault detection and isolation is to promptly alert operators to potential issues, enabling them to take necessary measures to avert system failure following the occurrence of problems [5]. Timely error detection and prediction are crucial in optimising crane performance and ensuring safety within container crane operations. The importance of timely error detection can be understood through several vital perspectives:  
1. Optimisation of Productivity and Efficiency: Timely error detection and prevention optimise crane performance and operational efficiency. By identifying and addressing errors promptly, operators can minimise the impact on productivity and throughput, ensuring that cranes operate at maximum capacity and efficiency [6]. This can be achieved by predicting errors early so that they do not turn into faults and result in failures. This performance optimisation contributes to container terminal operations' overall productivity and profitability. 2. Prevention of Operational Disruptions: Timely error detection allows for the early identification of potential issues or malfunctions in crane operations [7]. By promptly identifying errors, operators can take corrective actions to prevent operational disruptions, such as crane breakdowns or delays in cargo handling. This proactive approach helps to maintain the smooth flow of operations within container terminals, minimising downtime and ensuring the efficient utilisation of resources [8]. 3. Enhancement of Safety Protocols: Early detection of errors enables operators to implement safety protocols and procedures to mitigate risks and hazards associated with crane operations.