Vietnam Academy for Water Resources, Hanoi 100000, Vietnam.
Institute for Water and Environment, Hanoi 100000, Vietnam.
Int J Environ Res Public Health. 2020 Apr 4;17(7):2473. doi: 10.3390/ijerph17072473.
The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.
本研究的主要目的是利用先进的集成机器学习模型（RABANN）评估越南得农省的地下水潜力，该模型将人工神经网络（ANN）与 RealAdaBoost（RAB）集成技术相结合。在这项研究中，使用了 12 个条件因素和水井产量数据，为开发和验证集成 RABANN 模型创建了训练和测试数据集。使用接收者操作特征曲线（ROC）下的面积（AUC）和几个统计性能指标来验证和比较集成 RABANN 模型与单一 ANN 模型的性能。模型研究的结果表明，两个模型在评估地下水潜力的训练阶段都表现良好（AUC≥0.7），而在验证阶段，集成模型（AUC=0.776）优于单一 ANN 模型（AUC=0.699）。这表明 RAB 集成技术成功地提高了单一 ANN 模型的性能。通过对输入数据进行微小调整，开发的集成模型可以适应其他地区和国家的地下水潜力图绘制，以实现更有效的水资源管理。本研究有助于改善该地区的地下水状况，从而解决与人口有关的水传播疾病相关的健康问题。
