This study presents possible applications of machine learning (ML) methods for estimating water levels without a throughout understanding of hydrological processes and complex databases of irrigation systems. The Bac-Hung-Hai catchment, the biggest irrigation and drainage area in Vietnam, is selected as a case study due to the large database on this case consisting of 3,348 samples drawn over a 21-year monitoring period. The state-of-the-art Gradient tree boosting (GTB)-based model was developed and is compared with eight other common ML methods. The proposed GTB-based model consistently showed the best performance, with the lowest value of mean-squares-error and the greatest values for R² and adjusted R² in all case studies. Moreover, over 91% of the total samples had an error rate of below 10% between the predicted and the observed values. The results suggested that the GTB model can predict water level with high accuracy, thus helping researchers and policy-makers devise proactive strategies for hydraulic regulation and sustainable water management.

本研究介绍了机器学习 (ML) 方法在无需全面了解水文过程和灌溉系统复杂数据库的情况下估算水位的可能应用。Bac-Hung-Hai 流域是越南最大的排灌区，之所以被选为案例研究，是因为该案例的大型数据库包含 21 年监测期间抽取的 3,348 个样本。开发了最先进的基于梯度树提升 (GTB) 的模型，并将其与其他八种常见的机器学习方法进行了比较。提出的基于 GTB 的模型始终表现出最佳性能，均方误差值最低，R ²和调整后的 R ²值最高在所有案例研究中。此外，超过 91% 的总样本在预测值和观察值之间的误差率低于 10%。结果表明，GTB 模型可以高精度预测水位，从而帮助研究人员和政策制定者制定积极的水力调节和可持续水资源管理策略。