The early stages of fish during their life cycle, including larvae and juveniles, are sensitive to the environment. Determining the occurrences of fish larvae and juvenile relative to their associated environments is essential for conservation and fisheries management. Computer-based modeling has rarely been applied for forecasting the distribution patterns of the early fish stages in dynamic systems such as estuaries. In the present study, we applied novel modeling techniques to fish larval and juvenile samples collected in May, September, November, and December during 2019 along the Ba Lat estuary of the Red River, northern Vietnam. The results showed that the occurrences of freshwater and marine fish larvae and juveniles were inversely related to environmental factors (electrical conductivity, temperature, pH, depth, shore distance and turbidity) with a high square of multiple correlation coefficients. The occurrences of the two fish groups were strongly related to temporal and spatial changes in the estuary, and these correlations could be utilized for machine learning processing. Linear regression, Gaussian process models, ensemble regression, and artificial neural network (ANN) models were applied to elucidate the distributions of fish larvae and juveniles. It shows that ANN models obtained the highest R² (> 0.63). In addition, the spatial distribution prediction of fish larvae and juveniles using ANN models was similar to the field measurement. Thus, we suggest utilizing ANN models to predict the occurrences of early fish stages in estuaries in tropical regions such as Vietnam. Recommendations for further applications of ANN models are also given in this study.

鱼类在其生命周期的早期阶段，包括幼体和幼体，对环境很敏感。确定鱼类幼鱼和幼鱼相对于其相关环境的发生情况对于保护和渔业管理至关重要。基于计算机的建模很少用于预测动态系统（如河口）中早期鱼类的分布模式。在本研究中，我们将新的建模技术应用于 2019 年 5 月、9 月、11 月和 12 月在越南北部红河 Ba Lat 河口采集的鱼类幼体和幼鱼样本。结果表明，淡水和海水鱼类幼体和幼体的出现与环境因素（电导率、温度、pH、深度、岸距离和浊度）具有高平方的多重相关系数。两个鱼群的出现与河口的时空变化密切相关，这些相关性可用于机器学习处理。应用线性回归、高斯过程模型、集成回归和人工神经网络 (ANN) 模型来阐明鱼类幼鱼和幼鱼的分布。这表明 ANN 模型获得了最高的 并应用人工神经网络（ANN）模型来阐明鱼类幼鱼和幼鱼的分布。这表明 ANN 模型获得了最高的 并应用人工神经网络（ANN）模型来阐明鱼类幼鱼和幼鱼的分布。这表明 ANN 模型获得了最高的R ² (> 0.63)。此外，使用人工神经网络模型对鱼类幼体和幼体的空间分布预测与现场测量相似。因此，我们建议利用人工神经网络模型来预测越南等热带地区河口早期鱼类阶段的发生。本研究还给出了进一步应用 ANN 模型的建议。