Groundwater for Sustainable Development最新文献_第2页

Hydrogeophysical mapping of paleochannels for water security in Bhawanigarh Block, District Sangrur, Punjab, India 印度旁遮普省桑格鲁尔地区巴瓦尼加尔区古河道水文地质物理测绘，促进水安全

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2024-10-09 DOI: 10.1016/j.gsd.2024.101354

Paleochannels, ancient buried riverbeds, offer significant potential for groundwater management and contribute to achieving the Sustainable Development Goals (SDGs), particularly those focused on clean water (SDG 6) and sustainable ecosystems (SDG 15). These channels, formed when rivers change course due to natural processes or human activities, become filled with loose, permeable sediments like sand and gravel, making them natural reservoirs capable of storing large volumes of groundwater. This characteristic makes paleochannels invaluable for enhancing water security in arid and semi-arid regions.

The present study, conducted in the Bhawanigarh block of Sangrur District, Punjab, focuses on mapping paleochannels for Managed Aquifer Recharge (MAR) using Electrical Resistivity Survey techniques. A total of 37 Vertical Electrical Soundings (VES) were performed with a Computerized Resistivity Meter, and the findings were validated using well-logging, exploration data, historical aerial photographs, and satellite imagery. Identifying and mapping these paleochannels enable targeted groundwater recharge efforts, enhancing the sustainable management of water resources.

By strategically utilizing paleochannels for artificial recharge, excess surface water can be directed into these hidden reservoirs, effectively replenishing groundwater supplies. This approach supports agricultural and drinking water needs and strengthens resilience against climate change impacts, aligning with SDG 13 (Climate Action). Moreover, the careful management of these ancient channels promotes the sustainable use of natural resources, contributing to the overall goals of environmental sustainability and water security outlined in the SDGs.

古河道是古代埋藏的河床，为地下水管理提供了巨大潜力，有助于实现可持续发展目标（SDGs），尤其是以清洁水（SDGs 6）和可持续生态系统（SDGs 15）为重点的目标。这些河道是由于自然过程或人类活动导致河流改道而形成的，河道中充满了沙子和砾石等疏松、渗透性强的沉积物，使其成为能够储存大量地下水的天然水库。本研究在旁遮普省桑格鲁尔地区的巴瓦尼加尔区进行，重点是利用电阻率测量技术绘制古河道图，用于管理含水层补给（MAR）。使用计算机化电阻率测量仪共进行了 37 次垂直电测深 (VES)，并使用测井记录、勘探数据、历史航拍照片和卫星图像对结果进行了验证。通过战略性地利用古河道进行人工补给，可以将多余的地表水引入这些隐蔽的水库，从而有效补充地下水供应。这种方法既能满足农业和饮用水需求，又能增强抵御气候变化影响的能力，符合可持续发展目标 13（气候行动）。此外，对这些古老渠道的精心管理促进了自然资源的可持续利用，有助于实现可持续发展目标中提出的环境可持续性和水安全的总体目标。

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引用次数: 0

Groundwater fluoride contamination, sources, hotspots, health hazards, and sustainable containment measures: A systematic review of the Ghanaian context 地下水氟污染、来源、热点、健康危害和可持续遏制措施：对加纳情况的系统回顾

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2024-10-09 DOI: 10.1016/j.gsd.2024.101352

Groundwater quality is globally threatened by geogenic and human activities. These activities release high levels of potentially toxic elements, such as fluoride (F⁻), which pose significant threats to human health. This has become a global issue, especially in developing countries such as Ghana. Despite efforts to address this issue, knowledge gaps still need to be addressed to ensure safe and healthy drinking water for all Ghanaians. Moreover, Ghana has been reported to be a fluorosis-endemic country but the sources and exact hotspots of F⁻ enrichment in the aquifers on a countrywide scale are lacking in the available literature. Understanding the quality of water used for diverse purposes in Ghana is necessary to achieve the United Nations Sustainable Development Goals like good health and well-being (SDG 3) and clean water and sanitation (SDG 6), among others. Therefore, this study synthesized all previous studies on groundwater F⁻ contamination in Ghana, to identify the sources of F⁻ enrichment in groundwater, delineate the hotspots for fluorosis, assess the associated human health risks, identify the best sustainable defluoridation methods, and recommend policy intervention for high groundwater F⁻ threat to aquifers in Ghana. In the Ghanaian context, F⁻ contamination in groundwater is largely from geogenic sources like the weathering of fluoride-bearing rocks (granitoids and carbonate sedimentary lithologies) from the Birimian and Voltaian Supergroups and the dissolution of fluoride-rich minerals (fluorapatite, amphiboles, fluorite, biotite, and muscovite). Hotspots for high groundwater F⁻ in Ghana are mainly restricted to the Upper East Region (0.10–5.00 mg/L), North East Region (0.01–13.29 mg/L), Northern Region (0.1–11.6 mg/L), and the White Volta River Basin (0.04–3.79 mg/L). The mean and maximum values of F⁻ in these hotspots exceed the maximum permissible level (1.5 mg/L) set by the World Health Organization and Ghana Standards Authority. Most people in these areas suffer from dental fluorosis. Therefore, affordable and sustainable defluoridation technologies as well as community-based initiatives are recommended to deal with this menace.

地下水质量在全球范围内受到地质活动和人类活动的威胁。这些活动释放出高浓度的潜在有毒元素，如氟化物（F-），对人类健康构成严重威胁。这已成为一个全球性问题，尤其是在加纳等发展中国家。尽管为解决这一问题做出了努力，但仍需填补知识空白，以确保所有加纳人都能获得安全健康的饮用水。此外，据报道加纳是一个氟中毒流行的国家，但现有文献中缺乏全国范围内含水层中氟富集的来源和确切热点。要实现联合国可持续发展目标，如良好的健康和福祉（可持续发展目标 3）以及清洁水和卫生设施（可持续发展目标 6）等，就必须了解加纳各种用途的水质。因此，本研究综合了以往关于加纳地下水氟污染的所有研究，以确定地下水中氟富集的来源，划定氟中毒的热点地区，评估相关的人类健康风险，确定最佳的可持续除氟方法，并针对加纳地下水含氟过高对含水层造成的威胁提出政策干预建议。在加纳，地下水中的氟污染主要来自地质来源，如来自比里米亚超群和伏尔泰超群的含氟岩石（花岗岩和碳酸盐沉积岩质）的风化以及富氟矿物（氟磷灰石、闪石、萤石、黑云母和麝香石）的溶解。加纳地下水含氟量较高的热点地区主要限于上东部地区（0.10-5.00 毫克/升）、东北部地区（0.01-13.29 毫克/升）、北部地区（0.1-11.6 毫克/升）和白沃尔塔河流域（0.04-3.79 毫克/升）。这些热点地区的 F- 平均值和最大值都超过了世界卫生组织和加纳标准局规定的最大允许值（1.5 毫克/升）。这些地区的大多数人都患有氟斑牙。因此，建议采用负担得起、可持续的除氟技术以及基于社区的举措来应对这一威胁。

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引用次数: 0

Groundwater quality assessment for drinking and irrigation purposes in the Ayad river basin, Udaipur (India) 印度乌代布尔 Ayad 河流域用于饮用和灌溉的地下水质量评估

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2024-10-05 DOI: 10.1016/j.gsd.2024.101351

Globally, about 5.25 billion people depend on groundwater for their water needs. However, groundwater quality significantly impacts human health and agriculture, influenced by factors such as land use, waste seepage, soil properties, and geological settings. In Rajasthan, the primary groundwater quality issues involve fluoride, nitrate, chloride, and calcium. This study addresses the gap in the understanding of the spatial and temporal variations of these contaminants and how the variations are linked to geology and land use. The basis for the analysis is data spanning 2000 to 2021from the Ground Water Department (GWD), the Central Ground Water Board (CGWB), and citizen science data from 2022 to 2023, focusing on the Ayad River Basin. The research aims to evaluate groundwater quality for drinking and irrigation by assessing physico-chemical parameters and using the Weighted Arithmetic Water Quality Index (WAWQI) method to calculate the Groundwater Quality Index (GWQI) from 2000 to 2023. The findings suggest a decreasing GWQI trend from west to east in the basin, with good groundwater quality (GWQI below 50) in the southern regions near the cities Umarda, Ramgiri, Undri, and Hariyab. The highest index values were near Bhoyana, Khemli, and Sisarma. The results of the salinity hazard test showed that salinity is a major issue in the eastern part of the basin. Though the groundwater is notably hard, a comprehensive analysis of various parameters nevertheless suggested its suitability for irrigation purposes. These results provide new insights in the quality of the groundwater resources in the Ayad River basin and valuable insights for policymakers and for decision-makers to develop strategies to preserve the groundwater quality.

全球约有 52.5 亿人的用水需求依赖地下水。然而，受土地利用、废物渗漏、土壤特性和地质环境等因素的影响，地下水质量对人类健康和农业产生了重大影响。在拉贾斯坦邦，主要的地下水水质问题涉及氟化物、硝酸盐、氯化物和钙。这项研究弥补了人们对这些污染物的时空变化以及这些变化如何与地质和土地利用相关联的认识上的空白。分析的基础是地下水部 (GWD) 和中央地下水委员会 (CGWB) 2000 年至 2021 年的数据，以及 2022 年至 2023 年的公民科学数据，重点是阿亚德河流域。研究旨在通过评估物理化学参数和使用加权算术水质指数（WAWQI）方法计算 2000 年至 2023 年的地下水质量指数（GWQI），从而评估饮用水和灌溉用地下水的质量。研究结果表明，流域内的地下水质量指数自西向东呈下降趋势，南部地区靠近 Umarda、Ramgiri、Undri 和 Hariyab 等城市的地下水质量较好（地下水质量指数低于 50）。指数值最高的是博亚纳、克姆利和西萨玛附近。盐度危害测试结果表明，盐度是盆地东部的一个主要问题。虽然地下水明显偏硬，但对各种参数的综合分析表明，地下水适合灌溉。这些结果为了解阿亚德河流域地下水资源的质量提供了新的视角，也为政策制定者和决策者制定保护地下水质量的战略提供了宝贵的见解。

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引用次数: 0

Utilizing a multi-tracer method to investigate sulphate contamination: Novel insights on hydrogeochemical characteristics of groundwater in intricate karst systems 利用多示踪剂方法调查硫酸盐污染：对复杂岩溶系统地下水水文地球化学特征的新见解

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2024-10-02 DOI: 10.1016/j.gsd.2024.101350

Karst environments, especially in Mediterranean area, are highly vulnerable to natural and anthropogenic contamination. This study presents a comprehensive hydrogeochemical assesment of surface water and groundwater across a 2300 km² catchment area spanning Southern Dalmatia (Croatia) and Western Herzegovina (Bosnia and Herzegovina).

For the first time in the study area, data were collected over six years integrating ion analysis, sulphur isotope (δ³⁴S) composition, and physical-chemical analysis of water from 30 locations. The research identified four hydrogeochemical facies (carbonate, sulphate, mixed carbonate/sulphate and chloride), influenced by seawater intrusion, carbonate dissolution, evaporite presence, and human activities.

Elevated sulphate levels, often exceeding 250 mg/L, were a main focus of the study due to their potential risks to drinking water quality. The study developed a conceptual model to explain the distribution of sulphates, underscoring the importance of evaporite diapirism and δ³⁴S analysis in tracing sulphate origins. These findings contribute to an improved understanding of karst systems and offer essential data for groundwater protection and legislative measures in the Mediterranean region.

岩溶环境，尤其是地中海地区的岩溶环境，极易受到自然和人为污染的影响。该研究首次在研究地区收集了六年的数据，综合了离子分析、硫同位素（δ34S）组成以及 30 个地点的水的物理化学分析。研究确定了受海水入侵、碳酸盐溶解、蒸发岩存在和人类活动影响的四种水文地质化学面貌（碳酸盐、硫酸盐、混合碳酸盐/硫酸盐和氯化物）。该研究建立了一个概念模型来解释硫酸盐的分布，强调了蒸发岩萃取和 δ34S 分析在追踪硫酸盐来源方面的重要性。这些发现有助于加深对岩溶系统的了解，并为地中海地区的地下水保护和立法措施提供重要数据。

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引用次数: 0

Three-dimensional solute transport in finite and curved porous media with surface input sources 带有表面输入源的有限和弯曲多孔介质中的三维溶质输运

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2024-09-25 DOI: 10.1016/j.gsd.2024.101349

In this paper, an analytical solution for three-dimensional solute transport in porous media between two curved surfaces is investigated. It is assumed that the groundwater velocity and dispersion coefficient vary with time and position. Groundwater velocity is not considered to be horizontal. The components of dispersion coefficient along the axes are considered to be proportional to the square of corresponding the position variable. The dispersion coefficient components along axes are proportional to the corresponding component of groundwater velocity in temporal aspects while former is squarely proportional to letter one in position components. It is assumed that the sources originate from two curved surfaces. The nature of the source on the two surfaces is the same, but there may be a variation in potential. Initially, the aquifer's domain is supposed to be uniformly polluted. The Laplace Integral Transformation Technique (LITT) is used to obtain analytical solutions. Numerical examples are given to demonstrate the effects of various factors on the solute concentration profile in a system where advection and dispersion play important roles.

In addition, the sub-case of horizontal flow is also discussed. The model is extremely useful in analyzing and dealing with widespread surface sources of groundwater pollution in simulated agricultural fields or urban dumping areas.

本文研究了多孔介质在两个曲面之间的三维溶质输运的解析解。假设地下水速度和扩散系数随时间和位置变化。地下水速度不被认为是水平的。沿轴向的分散系数分量被认为与相应位置变量的平方成正比。在时间方面，沿轴的弥散系数分量与地下水流速的相应分量成正比，而在位置分量方面，前者与字母一成正比。假设水源来自两个曲面。两个曲面上的水源性质相同，但水势可能不同。初始情况下，含水层域假定受到均匀污染。利用拉普拉斯积分变换技术（LITT）获得解析解。此外，还讨论了水平流动的子情况。该模型在分析和处理模拟农田或城市垃圾堆放区广泛存在的地下水面源污染时非常有用。

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引用次数: 0

Robust estimation of hydrogeological parameters from wireline logs usingsemi-supervised deep neural networks assisted with global optimization-based regression methods 利用基于全局优化的回归方法辅助半监督深度神经网络，从有线测井记录中稳健地估算水文地质参数

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2024-09-21 DOI: 10.1016/j.gsd.2024.101348

Understanding the distribution of hydrogeological properties of the aquifers is crucial for sustainable groundwater resource development. This research explores the application of deep autoencoder neural networks (AE-NN), assisted with global optimization methods for estimating hydrogeological parameters in the Quaternary aquifer system in the Debrecen area, Hungary. Traditional methods for estimating aquifer parameters typically depend on field experiments and laboratory analyses, which are both costly and time-consuming, and often fail to account for the heterogeneity of groundwater formations. In this study, deep AE-NN models are trained to extract latent space (LS) representations that capture key features from the available well logs, including spontaneous potential (SP), natural gamma ray (NGR), shallow resistivity (RS), and deep resistivity (RD). The LS log is then correlated with shale volume and hydraulic conductivity, as determined by the Larionov and Csókás methods, respectively. Regression analysis revealed a Gaussian relationship between the LS log and shale volume and a negative nonlinear relationship with hydraulic conductivity. Global optimization methods, including simulated annealing (SA) and particle swarm optimization (PSO), were used to refine the regression parameters, enhancing the predictive capabilities of the models. The results demonstrated that AE-NN assisted with global optimization methods can be effectively used to estimate shale volume and hydraulic conductivity, proposing a novel and independent approach for estimating hydrogeological parameters critical to groundwater flow and contaminant transport modeling.

了解含水层水文地质特性的分布对于地下水资源的可持续开发至关重要。本研究探索了深度自动编码器神经网络（AE-NN）的应用，并辅以全局优化方法来估算匈牙利德布勒森地区第四纪含水层系统的水文地质参数。估算含水层参数的传统方法通常依赖于现场实验和实验室分析，既费钱又费时，而且往往无法考虑地下水层的异质性。在这项研究中，对深层 AE-NN 模型进行了训练，以提取潜空间（LS）表示法，捕捉现有测井记录的关键特征，包括自发电位（SP）、天然伽马射线（NGR）、浅层电阻率（RS）和深层电阻率（RD）。然后，LS 测井与页岩体积和导水率相关联，分别由 Larionov 和 Csókás 方法确定。回归分析表明，LS 测井与页岩体积之间存在高斯关系，而与导水率之间存在负的非线性关系。全局优化方法包括模拟退火（SA）和粒子群优化（PSO），用于完善回归参数，提高模型的预测能力。结果表明，AE-NN 在全局优化方法的辅助下可有效用于估算页岩体积和导水率，为估算对地下水流和污染物迁移建模至关重要的水文地质参数提出了一种新颖而独立的方法。

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引用次数: 0

A review of the current scenario and best possible solution for fecal sludge management (FSM) in India 印度粪便污泥管理（FSM）现状及最佳解决方案回顾

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2024-09-19 DOI: 10.1016/j.gsd.2024.101346

Fecal Sludge (FS) is partially digested slurry which is collected from onsite sanitation system (OSSs) such as septic tanks and pit latrines and dumped into nallas, open drains, open lands and water bodies. The current research is motivated by the awful situation and difficulties associated with managing FS in India. This study aims to provide a comprehensive analysis of FS production, gaps, challenges, impact, and the most cost-effective FS treatment solution for cities of India. The potential for commercialization as well as the reuse of treated FS in Indian cities are covered in this research. The current status of FS management in Indian cities is also reported through fecal waste flow diagram. Many septic tanks are poorly constructed, outdated, and do not meet required specifications in Indian cities. Groundwater is one of India's most valuable resources, and it is also impacted by seepage or infiltration of contaminants from septic tanks. UNICEF claims that if FS is not properly treated, it can pollute the surrounding environment, and drinking water supplies can cause severe diseases such as diarrhoea, dysentery and cholera. A survey revealed that a significant portion of urban India is unsewered and lacks access to adequate sanitation. Hence, there is an urgent need to conduct research in this area to better understand the impact of FS on water resources and land quality. Many individuals and groups from the public, commercial, and civil society sectors are required for the safe handling of FS at every point of the sanitation chain, from the household user to the final disposal of treated FS. To achieve Sustainable Development Goal 6 "clean water and sanitation" by 2030, there is an urgent need for cost-effective FSM solutions for developing countries.

粪便污泥（FS）是从化粪池和坑厕等现场卫生系统（OSS）中收集的部分消化泥浆，然后倾倒到河道、明渠、空地和水体中。当前研究的动机是印度在管理粪便污水方面的严峻形势和困难。这项研究旨在全面分析印度城市的粪便污水产量、差距、挑战、影响以及最具成本效益的粪便污水处理解决方案。本研究涵盖了商业化的潜力以及印度城市对处理过的垃圾填埋场的再利用。印度城市的粪便污水管理现状也通过粪便污水流程图进行了报告。在印度城市中，许多化粪池建造简陋、陈旧，不符合规定的规格。地下水是印度最宝贵的资源之一，化粪池渗出或渗入的污染物也会对地下水造成影响。联合国儿童基金会称，如果化粪池处理不当，会污染周围环境，饮用水供应也会引发腹泻、痢疾和霍乱等严重疾病。一项调查显示，印度城市中有很大一部分地区没有污水管道，无法获得足够的卫生设施。因此，迫切需要在这一领域开展研究，以更好地了解 FS 对水资源和土地质量的影响。从家庭用户到经过处理的粪便的最终处置，卫生设施链的每一个环节都需要来自公共、商业和民间社会的许多个人和团体来安全处理粪便。为了到 2030 年实现可持续发展目标 6 "清洁水和卫生设施"，发展中国家迫切需要成本效益高的无害环境管理解决方案。

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引用次数: 0

Multiscale groundwater level forecasts with multi-model ensemble approaches: Combining machine learning models using decision theories and bayesian model averaging 利用多模型集合方法进行多尺度地下水位预测：利用决策理论和贝叶斯模型平均法组合机器学习模型

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2024-09-18 DOI: 10.1016/j.gsd.2024.101347

Creating precise groundwater level (GWL) prediction models is of crucial significance for the productive use, extended planning, and controlling of limited sub-surface water supplies. In this research, the accuracy of GWL forecasts in Bangladesh was enhanced for three weeks by utilizing ensembles of Machine Learning (ML) models. Six advanced ML-based models were developed and assessed using eight performance indices, and an Overall Ranking (OR) was provided by combining the rankings produced by Grey Relational Analysis (GRA), Variation Coefficient (COV), and Shannon's Entropy (SE). The standalone forecasting models demonstrated excellent performance across the three forecasting horizons, with accuracy values ranging from 0.986 to 0.997 for one-step, 0.971 to 0.999 for two-step, and 0.960 to 0.997 for three-step forecasts at GT3330001. Results also revealed that three ranking techniques (SE, COV, and GRA), as well as their combined ranking (OR), produced different best-performing models at different prediction horizons for different observation wells. Weighted average ensembles of the prediction models were developed by calculating individual model weights using four ensemble modelling techniques: SE, COV, GRA, and Bayesian Model Averaging (BMA). The BMA-based ensemble technique outperformed three benchmark ensemble approaches, achieving R = 0.947, KGE = 0.925, IOA = 0.972, MAE = 0.062 m, and RMSE = 0.123 m for one-step-ahead forecasts at GT3330001. The findings exhibit a consistent trend across other forecasting horizons and observation wells. Finally, the Dempster-Shafer evidence theory was employed to rank the single and composite models. The ranking results demonstrated that the BMA-based ensemble consistently secured the top position (with the weight values of 0.997, 0.991, and 0.987 for one-week, two-weeks, and three-weeks forward forecasts at GT3330001) for all forecasting horizons and observation wells. This study shows that the BMA-based composite model can produce more accurate GWL projections at Bangladesh study location, with potential for application in other regions worldwide.

创建精确的地下水位（GWL）预测模型对于有限的地下水供应的生产利用、扩展规划和控制具有至关重要的意义。在这项研究中，利用机器学习（ML）模型集提高了孟加拉国三周内地下水位预测的准确性。研究人员开发了六种先进的基于 ML 的模型，并使用八项性能指标对其进行了评估，综合灰色关系分析 (GRA)、变异系数 (COV) 和香农熵 (SE) 得出了综合排名 (OR)。独立预测模型在三个预测范围内均表现出色，在 GT3330001 上，一步预测的准确度值为 0.986 至 0.997，两步预测的准确度值为 0.971 至 0.999，三步预测的准确度值为 0.960 至 0.997。结果还显示，三种排序技术（SE、COV 和 GRA）以及它们的组合排序（OR）在不同观测井的不同预测范围产生了不同的最佳模型。通过使用四种集合建模技术计算单个模型的权重，建立了加权平均集合预测模型：SE、COV、GRA 和贝叶斯模型平均（BMA）。基于 BMA 的集合技术优于三种基准集合方法，在 GT3330001 的一步前预报中，R = 0.947，KGE = 0.925，IOA = 0.972，MAE = 0.062 m，RMSE = 0.123 m。这些结果在其他预测范围和观测井中呈现出一致的趋势。最后，采用 Dempster-Shafer 证据理论对单一模式和复合模式进行排序。排序结果表明，基于 BMA 的集合模型在所有预测范围和观测井中一直稳居榜首（在 GT3330001 的一周、两周和三周前瞻预测中的权重值分别为 0.997、0.991 和 0.987）。这项研究表明，基于 BMA 的复合模型可以对孟加拉国研究地点的 GWL 进行更准确的预测，并有可能应用于全球其他地区。

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引用次数: 0

Application of the DRASTIC-LU/LC method combined with machine learning models to assess and predict the vulnerability of the Rmel aquifer (Northwest, Morocco) 应用 DRASTIC-LU/LC 方法与机器学习模型相结合，评估和预测 Rmel 含水层（摩洛哥西北部）的脆弱性

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2024-09-18 DOI: 10.1016/j.gsd.2024.101345

The Rmel aquifer, located in the Tangier-Tetouan-Al Hoceima region of northwest Morocco, covers approximately 240 km² and faces increasing pollution threats due to population growth and economic development. This study assesses aquifer vulnerability to pollution, and compares the performance of various machine learning models integrated with the DRASTIC-LU/LC method. The research used a dataset of 52 water samples analyzed for nitrate concentrations, considering eight factors influencing vulnerability: aquifer depth, net recharge, aquifer lithology, soil texture, topography, vadose zone impact, hydraulic conductivity, and land use. An information gain test was applied to evaluate the importance of these factors. Four machine learning algorithms were used with the DRASTIC-LU/LC method: multilayer perceptron (MLP), the bagging algorithm (BA), K-nearest neighbors (KNN), and extremely randomized trees (ERT). Model performance was assessed via the area under the ROC curve (ROC-AUC) to measure accuracy. The ERT model combined with DRASTIC-LU/LC achieved the highest accuracy (AUC = 0.929), followed by BA (AUC = 0.925), MLP (AUC = 0.852), and KNN (AUC = 0.787). In comparison, the original DRASTIC-LU/LC model had an AUC of 0.530. The results highlight significant vulnerability variation across the Rmel aquifer, with high to very high levels in the southern and northwestern regions, and moderate to low levels in the northeast and central areas. Vulnerability maps were validated by comparing the observed nitrate concentrations in the water samples, confirming model accuracy. Groundwater depth, net recharge, and hydraulic conductivity were identified as the most significant factors influencing vulnerability. This study demonstrates the effectiveness of integrating machine learning models with the DRASTIC-LU/LC method for accurate aquifer vulnerability assessment, offering valuable tools for public policy and groundwater management.

Rmel 含水层位于摩洛哥西北部的丹吉尔-泰图安-胡塞马地区，面积约 240 平方公里，因人口增长和经济发展而面临日益严重的污染威胁。本研究评估了含水层易受污染的程度，并比较了与 DRASTIC-LU/LC 方法相结合的各种机器学习模型的性能。研究使用了 52 个分析硝酸盐浓度的水样数据集，考虑了影响脆弱性的八个因素：含水层深度、净补给量、含水层岩性、土壤质地、地形、浸润带影响、水力传导性和土地利用。采用信息增益测试来评估这些因素的重要性。DRASTIC-LU/LC 方法使用了四种机器学习算法：多层感知器 (MLP)、装袋算法 (BA)、K-近邻 (KNN) 和极随机树 (ERT)。模型性能通过 ROC 曲线下面积（ROC-AUC）进行评估，以衡量准确性。与 DRASTIC-LU/LC 相结合的 ERT 模型达到了最高的准确率（AUC = 0.929），其次是 BA（AUC = 0.925）、MLP（AUC = 0.852）和 KNN（AUC = 0.787）。相比之下，原始 DRASTIC-LU/LC 模型的 AUC 为 0.530。结果表明，整个 Rmel 含水层的易损性差异很大，南部和西北部地区的易损性水平较高到非常高，而东北部和中部地区的易损性水平中等到较低。通过比较水样中观测到的硝酸盐浓度，验证了易损性地图，确认了模型的准确性。地下水深度、净补给量和水力传导性被确定为影响脆弱性的最重要因素。这项研究证明了将机器学习模型与 DRASTIC-LU/LC 方法相结合进行精确含水层脆弱性评估的有效性，为公共政策和地下水管理提供了宝贵的工具。

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引用次数: 0

Identifying potential artificial recharge zone in an arid craton 确定干旱克拉通的潜在人工补给区

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2024-09-17 DOI: 10.1016/j.gsd.2024.101338

Identifying sustainable artificial recharge zones in arid cratons is challenging due to complex geology and limited natural recharge conditions, making accurate site selection and management difficult. This study integrates Vertical Electrical Sounding (VES), the Analytic Hierarchy Process (AHP), and Boolean analysis to identify sustainable artificial recharge zones in the arid Bundelkhand craton of India. Aquifer thickness and fractures emerged as critical determinants of groundwater recharge conditions, revealing varying degrees of suitability for recharge across the study area. Approximately 2.31% (13.36 km²) of the area along streams exhibited "very high" suitability, while 8.09% (45.82 km²) had "high" suitability. “Moderate" suitability covered 17.86% (101.66 km²), "low" suitability accounted for 38.85% (218.39 km²), and "very low" suitability represented 17.35% (98.75 km²) of the area. Recharge potential was highest in the northeast and central parts, with the middle of the watershed exhibiting the lowest potential. The study demonstrated that this integrated approach significantly improved precision from 71.40% to 85.70% and enhanced the F1 score from 0.833 to 0.923, surpassing the performance of the AHP method alone. The findings highlighted the importance of strategic selection and targeting of specific locations for artificial recharge, as only ∼18% of the study area was suitable for such efforts, despite ∼43% showing potential for groundwater. AHP with VES proves more precise and reliable than Fuzzy-AHP with VES, with AHP's conservative approach classifying 55.70% of the area as very low to low suitability compared to Fuzzy-AHP's 41.92%, ensuring only the most suitable sites are selected. VES offers cost-effectiveness, noninvasiveness, and rapid generation of a 1D subsurface model, balancing its lower detail compared to Electrical Resistivity Tomography. When combined with the AHP, VES enhances adaptability to changing conditions, emphasizing ecological preservation and climate change resilience. This approach effectively addresses water challenges in arid regions, contributing to sustainable water resource management.

由于地质复杂、自然补给条件有限，在干旱的环形山中确定可持续的人工补给区具有挑战性，因此很难进行准确的选址和管理。本研究整合了垂直电探测（VES）、层次分析法（AHP）和布尔分析法，以确定印度干旱的邦德尔康德喀斯特地区的可持续人工补给区。含水层厚度和裂缝是决定地下水补给条件的关键因素，显示了整个研究区域不同程度的补给适宜性。溪流沿岸约有 2.31% 的区域（13.36 平方公里）具有 "非常高 "的适宜性，8.09% 的区域（45.82 平方公里）具有 "高 "的适宜性。"中度 "适宜性占 17.86%（101.66 平方公里），"低 "适宜性占 38.85%（218.39 平方公里），"极低 "适宜性占 17.35%（98.75 平方公里）。东北部和中部的补给潜力最大，流域中部的补给潜力最小。研究表明，这种综合方法大大提高了精确度，精确度从 71.40% 提高到 85.70%，F1 分数从 0.833 提高到 0.923，超过了单独使用 AHP 方法的性能。研究结果凸显了战略性选择和锁定特定地点进行人工补给的重要性，因为尽管有 43% 的区域显示出地下水的潜力，但只有 18% 的研究区域适合进行人工补给。事实证明，采用 VES 的 AHP 比采用 VES 的模糊-AHP 更精确、更可靠，AHP 的保守方法将 55.70% 的区域划分为非常低至低适宜性区域，而 Fuzzy-AHP 为 41.92%，从而确保只选择最适宜的地点。与电阻率断层扫描法相比，VES 具有成本效益高、无损伤、可快速生成一维地下模型等优点，但其细节较少。当与 AHP 结合使用时，VES 可增强对不断变化的条件的适应性，强调生态保护和气候变化适应能力。这种方法可有效解决干旱地区的水资源挑战，促进可持续水资源管理。

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引用次数: 0