## 5 Discussion and Conclusions
### 5.1. Scarity of Datasets  
In hydrodynamic noise studies, the paucity of high-quality data hinders the extensive application of intelligent algorithms. The collection of fluid noise field data is often accompanied by significant expenses, complex operations, and limitations in experimental conditions, impeding the capacity for model training and validation to encompass realistic and diverse flow field environments. As [PERSON] (2013) noted, the paucity of datasets is a pervasive challenge in hydrodynamics research, impeding the generalizability of machine learning models, particularly in scenarios where experimental data are scarce or subject to significant data bias. This hinders the ability of algorithms to accurately predict complex fluid phenomena. In addressing this challenge, researchers are progressively adopting a combination of data augmentation techniques and multimodal sensors to enrich training sets. This involves generating synthetic data or extending existing data sources. In studies related to ocean acoustics, the scarcity of datasets similarly affects the performance of noise \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Application Category** & **Main Function/Objective** & **Representative Algorithms** & **Advantages** & **Challenges and Limitations** & **Typical Application Examples** \\ \hline Optimization Design & Optimize noise source structure, geometric design, and material parameters to reduce noise radiation & Genetic Algorithm (GA), Particle Swarm Optimization & Strong global search ability; suitable for multi-objective and multi-variable optimization & Sensitive to parameters; prone to local optima; potential slow convergence & Turbine or pump blade design optimization for low-noise performance \\ \hline \multirow{4}{*}{Noise Prediction} & Rapidly and accurately predict flow and acoustic field distributions to reveal noise generation mechanisms & Deep Learning (DL), PSO combined with CFD & Deep Learning (DL), PSO and feature extraction capabilities & Highly data-dependent; model & \multirow{2}{*}{NSDL-based noise prediction; rapid reconstruction of noise fields} \\ \hline \multirow{4}{*}{Control Strategy Optimization} & Implement active/ passive noise control by dynamically adjusting control parameters to reduce noise & Reinforcement Learning (RL), Ant Colony Optimization & Explainability & High computational complexity; balancing real-time response with available resources can be challenging & RL-based underwater noise control systems; optimization of response with available resources arrangement and feedback control \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification and summary of intelligent algorithm applications in hydrodynamic noise. prediction models. To expand the range of modeling applications, it is essential to establish diverse hydrodynamic noise datasets. In this context, researchers are gradually adopting a combination of data augmentation techniques and multimodal sensors to alleviate the challenge of insufficient data. By generating synthetic data or extending existing data, researchers can enrich training datasets, thereby enhancing the generalization and prediction capabilities of models. For instance, generative adversarial networks (GAN) are employed to expand fluid noise data, simulating an array of experimental scenarios and intricate flow field conditions, thereby furnishing more comprehensive and diverse data support for the training of intelligent algorithms [104]. Additionally, the integration of multimodal sensors offers a novel approach for data collection, where the fusion of multiple sensors enhances data accuracy and resolution, while covering a broader range of experimental scenarios, thereby providing more dimensional data for noise prediction models. In the domain of ocean acoustics research, the paucity of data has been shown to have a substantial impact on the efficacy of noise prediction models [105].