A multi-step approach to evaluate the sustainable use of groundwater resources for human consumption and agriculture

A novel approach in comparing the performance of bivariate statistical methods, boosting, and stacking models in flood susceptibility assessment

Evaluating the performance of flood susceptibility assessment methodologies is critical for optimizing flood management strategies. This study presents a novel methodology for comparing bivariate statistical methods, boosting, and stacking models to determine the most effective technique for flood susceptibility assessment in Hoa Vang District, Da Nang City, Vietnam. Twelve key factors from an initial set of seventeen factors determine their impact on flooding based on information gain ratio (IGR) and multicollinearity analysis. The study extracted 2,172 samples from Sentinel 1 imagery and field survey data, dividing them into training (70 %) and testing (30 %) sets using a random method. The two primary indices utilized for the bivariate statistical approach were the weight of evidence (WoE) and frequency ratio (FR). In bivariate statistics, the study utilizes two methods for classifying factors influencing flooding: the traditional Jenks natural breaks (JNB) and an improved version of JNB that accounts for correlation with flood data. Boosting models (AdaBoost (AB), XGBoost (XGB), CatBoost (CB), Light Gradient Boosting Machine (LGB), and Gradient Boosting (GB)) were employed both independently and in combination as base learners within the stacking model framework. Performance evaluation utilized the receiver operating characteristic and area under the curve (ROC-AUC), Kappa statistics, and other indices. The results show that the stacking models delivered the highest evaluation performance, with an average score of 0.882, outperforming the boosting models (0.76) and significantly surpassing the flood susceptibility maps generated by the bivariate statistical methods WoE (0.282) and FR (0.136). The study identified high and very high-risk flood zones, encompassing 14 % of the district, focusing on the southern communes. These findings provide valuable insights for enhancing flood susceptibility management and mitigation strategies, offering a robust tool for decision-making in flood-prone areas.

Advancing groundwater quality predictions: Machine learning challenges and solutions

Machine learning (ML) is revolutionizing groundwater quality research by enhancing predictive accuracy and management strategies for contamination. This comprehensive review explores the evolution of ML technologies and their integration into environmental science, assessing 230 papers to understand the advancements and challenges in groundwater quality research. It reveals that a substantial portion of the research neglects critical preprocessing steps, crucial for model accuracy, with 83 % of the studies overlooking this phase. Furthermore, while model optimization is more commonly addressed, being implemented in 65 % of the papers, there is a noticeable gap in model interpretability, with only 15 % of the research providing explanations for model outcomes. Comparative evaluation of ML algorithms and careful selection of evaluation metrics are deemed essential for determining model fitness and reliability. The review underscores the need for interdisciplinary collaboration, methodological rigor, and continuous innovation to advance ML in groundwater management. By addressing these challenges and implementing solutions, the full potential of ML can be harnessed to tackle complex environmental issues and ensure sustainable groundwater management. This comprehensive and critical review paper can serve as a guiding framework to establish minimum standards for developing ML in groundwater quality studies.

Evaluate the groundwater quality and human health risks for sustainable drinking and irrigation purposes in mountainous region of Chongqing, Southwest China

Groundwater is crucial for agriculture and domestic consumption. This research investigated the hydrogeochemical properties and contaminant sources of groundwater within the mountainous terrain of northern Chongqing, with the objective of evaluating its appropriateness for irrigation and potable use. The hydrochemical type of the groundwater was HCO3 - Ca, dominated by silicate and calcite dissolutions. High NO3- (29.03% exceeds 10 mg/L) were attributed to the overuse of agricultural fertilizers. A comprehensive evaluation was conducted to determine the groundwater suitability for agricultural and potable uses. The results showed that groundwater in the southwestern region, particularly within the Yangtze River mainstem watershed, exhibited less suitability for irrigation owing to its lower mineralization, in contrast to the northeastern region near the Daning River watershed. But this trend is reversed for drinking purposes. Overall, the groundwater was appropriate for both drinking (93.55% were classified as excellent) and irrigation (70.98% were classified as low restriction) purposes in the study area. Deterministic and probabilistic noncarcinogenic health risk analyses centered on nitrate exposure revealed that infants (with 13.79% of samples >1) were at greater risk than children (8.58%), adult males (6.98%), and adult females (5.24%). This underscores the urgency to reduce nitrogen fertilizer usage and improve water management in the region. This research will provide guidance for the sustainable groundwater management in mountainous regions.

Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms

Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI.