Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, 247667, India.
Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, 247667, India.
J Environ Manage. 2024 Nov;370:122903. doi: 10.1016/j.jenvman.2024.122903. Epub 2024 Oct 15.
Assessing groundwater quality typically involves labor-intensive, time-consuming, and costly laboratory tests, making real-time monitoring impractical, especially at the local level. Groundwater quality projections at the local scale using broad spatial datasets have been inaccurate due to variations in hydrogeology, human activities, industrial operations, groundwater extraction, and waste disposal. This study aims to identify the most dependable and resilient machine learning algorithms for forecasting groundwater quality at nearby monitoring locations by utilizing simple water quality metrics that can be quickly assessed without extensive sampling and laboratory testing. The Entropy-weighted Water Quality Index (EWQI) was calculated using a large spatial and temporal dataset (2014-2021) of 977 wells with parameters including pH, total hardness (TH), calcium (Ca⁺), magnesium (Mg⁺), sodium (Na⁺), potassium (K⁺), sulfate (SO₄⁻), chloride (Cl⁻), nitrate (NO₃⁻), total dissolved solids (TDS), and fluoride (F⁻). Further, similar parameters were also observed in 33 open wells at the three local monitoring sites from December 2022 to March 2023. The EWQI was predicted using a Random Forest (RF), eXtreme Gradient Boosting (XGB), and Deep Neural Network (DNN). The pH, TH, and TDS were used as input variables for EWQI predictions, as they can be easily measured using handheld probes or multi-parameters. The model performance was evaluated using R, MAE, and RMSE. During the training stage, all three models predicted the EWQI with an R greater than 90%, with minimal errors when pH, TH, and TDS were input variables. To validate the models at a local scale, the EWQI was predicted at the village level (e.g., Antoli, Balapura, and Lapodiaya) using pH, TH, and TDS as input variables. The machine learning models were able to predict the EWQI very closely to the actual EWQI, with an R greater than 90%. It is also evident that the models could predict the EWQI using basic parameters that are easily measured, providing an overall idea of the water quality for a small area. Hence, these machine learning models could be useful for accurately representing groundwater quality, thereby avoiding the use of time-consuming and costly laboratory techniques.
评估地下水质量通常需要耗费大量的人力、时间和成本进行实验室测试，因此实时监测在实践中并不可行，尤其是在地方层面。由于水文地质、人类活动、工业运营、地下水开采和废物处理等方面的差异，利用广泛的空间数据集在地方尺度上对地下水质量进行预测一直不够准确。本研究旨在通过利用简单的水质指标来识别最可靠和最有弹性的机器学习算法，这些指标可以在无需广泛采样和实验室测试的情况下快速评估，从而预测附近监测点的地下水质量。利用 2014 年至 2021 年间 977 口井的大时空数据集（包括 pH 值、总硬度（TH）、钙（Ca⁺）、镁（Mg⁺）、钠（Na⁺）、钾（K⁺）、硫酸盐（SO₄⁻）、氯化物（Cl⁻）、硝酸盐（NO₃⁻）、总溶解固体（TDS）和氟化物（F⁻）等参数）计算了熵权水质指数（EWQI）。此外，还在 2022 年 12 月至 2023 年 3 月期间在三个地方监测点的 33 口露天井中观察到了类似的参数。使用随机森林（RF）、极端梯度增强（XGB）和深度神经网络（DNN）对 EWQI 进行了预测。pH 值、TH 和 TDS 被用作 EWQI 预测的输入变量，因为它们可以使用手持探头或多参数很容易地测量。使用 R、MAE 和 RMSE 评估了模型性能。在训练阶段，所有三种模型对 EWQI 的预测 R 值均大于 90%，当 pH 值、TH 和 TDS 作为输入变量时，误差最小。为了在地方尺度上验证模型，使用 pH 值、TH 和 TDS 作为输入变量，在村庄层面（例如，Antoli、Balapura 和 Lapodiaya）预测了 EWQI。机器学习模型能够非常准确地预测 EWQI，R 值大于 90%。很明显，这些模型可以使用容易测量的基本参数来预测 EWQI，从而提供小区域水质的总体概念。因此，这些机器学习模型可用于准确表示地下水质量，从而避免使用耗时且昂贵的实验室技术。
