Comparison of DRASTIC and DRASTICL groundwater vulnerability assessments of the Burdekin Basin, Queensland, Australia

Spatio-temporal prediction of groundwater vulnerability based on CNN-LSTM model with self-attention mechanism: A case study in Hetao Plain, northern China

Located in northern China, the Hetao Plain is an important agro-economic zone and population centre. The deterioration of local groundwater quality has had a serious impact on human health and economic development. Nowadays, the groundwater vulnerability assessment (GVA) has become an essential task to identify the current status and development trend of groundwater quality. In this study, the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism. The study firstly builds the CNN-LSTM model with self-attention (SA) mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The results indicate that the CNN-LSTM model outperforms these models, demonstrating its significance in groundwater vulnerability assessment. It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years. This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities. Moreover, the overall groundwater vulnerability risk in the entire region has increased, evident from both the notably high value and standard deviation. This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities. The model can be optimized for diverse applications across regional environmental assessment, pollution prediction, and risk statistics. This study holds particular significance for ecological protection and groundwater resource management.

Assessing uncertainties in modeling the risk of geogenic groundwater contamination

Assessing groundwater contamination risk is a critical aspect of preventing and managing groundwater pollution. There was a research gap in the investigation of uncertainties in modeling groundwater contamination risks in aquifers. This study addresses this gap using Bayesian Model Averaging (BMA), with a novel focus on risk exposures from geogenic contaminants, such as lead (Pb). This was achieved through the following methodology: (1) assessing aquifer vulnerability using the SPECTR framework; (2) generating a risk index for geogenic contaminants through unsupervised methods; (3) enhancing geogenic risk through three individual models, including Gene Expression Programming (GEP), M5P, and Support Vector Machines (SVM); (4) combining results from individual models using BMA; and (5) examining inherent uncertainties, accounting for both between-model and within-model variances. The model's efficacy was evaluated using measured Pb concentrations within the aquifer. The findings indicated that the unsupervised risk index had an acceptable correlation, while the individual models were accurate and enhanced the predictability of the data. BMA assigned the higher posterior probabilities (weight) to the SVM model, which indicates a positive correlation between the performance criteria of individual models and the weight values. Also, BMA revealed that the modeling uncertainty is influenced by within-model variance, primarily by the kriging interpolation method.

Integrated assessment of groundwater vulnerability in arid areas combining classical vulnerability index and AHP model

Groundwater is the main source of water for agriculture, industry, and families in arid areas. At present, there is an urgent need to protect groundwater due to human activities. In this study, the Qingshui River Basin was selected as the study area. Based on the DRASTIC model, the DRASTIC-Land use type (DRASTICL) model and the analytic hierarchy process-DRASTICL (AHP-DRASTICL) model were constructed by optimizing the indicators and weights. And the three models were applied to calculate the groundwater vulnerability index (GVI), and the groundwater vulnerability map (GVM) was drawn. The validation results of Spearman correlation coefficient show that the DRASTICL model and the AHP-DRASTICL model have higher correlation, which indicates that the optimized model is more accurate. Among them, the AHP-DRASTICL model has the highest correlation coefficient (ρ = 0.92), which is more in line with the actual situation. The results of this study can provide scientific guidance for the protection and utilization of groundwater in the Qingshui River Basin. And it is of guiding significance for the study of groundwater vulnerability, especially for groundwater management in arid and semi-arid areas.

Assessment of aquifer specific vulnerability to total nitrate contamination using ensemble learning and geochemical evidence

Henan Province's plain area is the granary of China, yet its regional aquifer is being polluted by industrial wastewater, agricultural pesticide, fertilizer and domestic wastewater. In order to safeguard the security of food and drinking water, and in response to the problem of low prediction accuracy caused by the lack of samples and unevenly distributed groundwater monitoring data, we propose a new way to predict the aquifer vulnerability in large areas by rich small-scale data, so as to identify the pollution risks and to address the issue of sample shortage. In small regions with abundant nitrate data, we employed a Random Forest model to screen key impact indicators, using them as features and nitrate-N concentration as the target variable. Consequently, we established six machine learning prediction models, and then selected the best bagging model (R2 = 0.86) to predict the vulnerability of aquifers in larger regions lacking nitrate data. The predicted results showed that highly vulnerable areas accounted for 20 %, which were mainly affected by aquifer thickness (65.91 %). High nitrate-N concentration implies serious aquifer contamination. Therefore, a long series of groundwater nitrate-N concentration monitoring data in a large scale, the trend and slope of nitrate-N concentration showed a significant correlation with the model prediction results (Spearman's correlation coefficients are 0.75 and 0.58). This study can help identify the risk of aquifer contamination, solve the problem of sample shortage in large areas, thus contributing to the security of food and drinking water.

A critical application of different methods for the vulnerability assessment of shallow aquifers in Zhengzhou City

Groundwater vulnerability can partially reflect the possibility of groundwater contamination, which is crucial for ensuring human health and a good ecological environment. The current study seeks to assess the groundwater vulnerability of Zhengzhou City by adopting an amended version of the traditional DRASTIC model, i.e., the DRASTICL model, which incorporates land use type indicators. More specifically, the AHP-DRASTICL, entropy-DRASTICL, and AE-DRASTICL models were established by optimizing weights using the analytic hierarchy process (AHP) and entropy weight method. The evaluation results for these five models were divided into five levels: very low, low, medium, high, and very high. Using Spearman's rank correlation coefficient, the nitrate concentration was used to verify the groundwater vulnerability assessment results. The AE-DRASTICL model was found to perform the best, with a Spearman correlation coefficient of 0.78. However, the AHP and entropy weight method effectively improved the accuracy of vulnerability assessment results, making it more suitable for the study area. This study provides important insights to inform the design of strategies to protect groundwater in Zhengzhou.

Assessing groundwater vulnerability potential using modified DRASTIC in Ajabshir Plain, NW of Iran

The vulnerability of groundwater as the primary source of water for human survival should be assessed for the purpose of pollution management. The Ajabshir plain, one of the major agricultural areas in the northwest of Iran, is always prone to pollution. Therefore, to prevent the increase in pollution, it is necessary to determine the polluting factors and areas prone to groundwater pollution. In this study, by modifying the DRASTIC method using the land-use layer, called DRASTICL, vulnerable areas and pollution index were mapped. To ensure dealing with the uncertainty of the parameters, the DRASTICL model was optimized utilizing the Sugeno-type fuzzy inference system. The models were validated based on nitrate pollution. The correlation of DRASTICL and its optimized model with the nitrate pollution are 0.32 and 0.8, respectively. The results of this study show that integrating the DRASTIC model and fuzzy knowledge is an instrumental way for assessment of vulnerability potential.