Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
复制标题
DOI：
10.1016/j.ecolind.2021.107790
复制
发表时间：
2021-05-17
影响因子：
6.9
通讯作者：
Tien Dat Pham
中科院分区：
文献类型：
--
作者：
Dang An Tran;Tsujimura, Maki;Tien Dat Pham
地下水盐渍化被认为是世界沿海地区的一个主要环境问题，影响着生态系统和人类健康。然而，由于地下水盐渍化过程及其影响因素的复杂性，准确预测地下水含盐量仍然是一个挑战。在本研究中，我们评估了最先进的机器学习（ML）算法用于预测地下水盐度并确定其影响因素。本文利用包含216个地下水样本和14个调节因子的地理数据库，对越南湄公河三角洲沿海多层含水层进行了研究。我们比较了不同机器学习技术的预测性能，即随机森林回归（RFR）、极端梯度增强回归（XGBR）、CatBoost回归（CBR）和轻梯度增强回归（LGBR）模型。采用均方根误差（RMSE）、决定系数（R-2）、赤池信息准则（AIC）和贝叶斯信息准则（BIC）评价模型的性能。结果表明，CBR模型在训练数据集（R-2 = 0.999, RMSE = 29.90）和测试数据集（R-2 = 0.84, RMSE = 205.96, AIC = 720.60, BIC = 751.04）上均具有最高的性能。14个影响因素中的10个是影响地下水盐度预测的最重要因素，包括距盐碱源距离、筛井深度、地下水位、垂直导水率、作业时间、井密度、抽采能力、引水层厚度、距断层距离和水平导水率。研究结果为决策者在沿海低地地区地下水过度开采的背景下提出地下水盐度问题的修复和管理策略提供了见解。由于人为影响因素对地下水盐渍化的影响显著，因此应采取紧急行动，确保湄公河三角洲沿海地区地下水的可持续管理。
Groundwater salinization is considered as a major environmental problem in worldwide coastal areas, influencing ecosystems and human health. However, an accurate prediction of salinity concentration in groundwater remains a challenge due to the complexity of groundwater salinization processes and its influencing factors. In this study, we evaluate state-of-the-art machine learning (ML) algorithms for predicting groundwater salinity and identify its influencing factors. We conducted a study for the coastal multi-layer aquifers of the Mekong River Delta (Vietnam), using a geodatabase of 216 groundwater samples and 14 conditioning factors. We compared the predictive performances of different ML techniques, i.e., the Random Forest Regression (RFR), the Extreme Gradient Boosting Regression (XGBR), the CatBoost Regression (CBR), and the Light Gradient Boosting Regression (LGBR) models. The model performance was assessed by using root-mean-square error (RMSE), coefficient of determination (R-2), the Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results show that the CBR model has the highest performance with both training (R-2 = 0.999, RMSE = 29.90) and testing datasets (R-2 = 0.84, RMSE = 205.96, AIC = 720.60, and BIC = 751.04). Ten of the 14 influencing factors, including the distance to saline sources, the depth of screen well, the groundwater level, the vertical hydraulic conductivity, the operation time, the well density, the extraction capacity, the thickness of the aquitard, the distance to fault, and the horizontal hydraulic conductivity are the most important factors for groundwater salinity prediction. The results provide insights for policymakers in proposing remediation and management strategies for groundwater salinity issues in the context of excessive groundwater exploitation in coastal lowland regions. Since the human-induced influencing factors have significantly influenced groundwater salinization, urgent actions should be taken into consideration to ensure sustainable groundwater management in the coastal areas of the Mekong River Delta.