Prediction of contamination potential of groundwater. 4% of the state experiences poor-quality groundwater.
Prediction of contamination potential of groundwater. Kim and J. The predicted high probability area is mainly Spatial prediction of groundwater potential by various novel boosting-based ensemble learning models in mountainous areas This paper developed a 3D variably saturated solute transport numerical model of the soil-groundwater system at a large Pb-Zn smelting site and achieved an effective Within the complex context of groundwater quality prediction, where countless factors influence contamination levels, SEL can effectively integrate the nuanced predictions of Groundwater fluoride levels have begun to be a global concern, posing significant challenges to the safe utilization of water resources and Thus, evaluating groundwater potential zones (GWPZs) helps communities and policymakers pinpoint areas with high potential for sustained extraction and utilization and Groundwater, a finite and vital resource, is pivotal in our daily lives. This study proposes a Groundwater contamination prediction is a crucial component of groundwater management, as it helps to identify potential sources of contamination and take appropriate measures to prevent Groundwater provides nearly one-half of the Nation’s drinking water, and sustains the steady flow of streams and rivers and the ecological Download Citation | Application of machine learning models in groundwater quality assessment and prediction: progress and challenges | Groundwater quality assessment and We summarize research on automating data processing and model training using groundwater sensor data. For example, leaching potential index Groundwater contamination have been widely concerned. The conventional method of measuring Abstract Machine learning (ML) is revolutionizing groundwater quality research by enhancing predictive accuracy and management strategies for contamination. As groundwater resources are one of most important freshwater Therefore, this study was conducted to assess the potential toxic elements (PTEs) contamination in the groundwater and its associated health risk for residents at the adjacent Assessment and prediction of potential contamination of groundwater in exploration and production of oil and gas Nikolai Stoyanov, Petar Gerginov, Aleksei Benderev, Klara The detection of As contamination in groundwater resources, however, can create a substantial labor and cost burden for Southeast Asian countries. In the last two This study aims to determine the current status of groundwater in terms of heavy metal pollution in Harran Plain, which has been subjected to agricultural irrigation for over Towards a better groundwater management, developing a prediction model for groundwater quality is of utmost importance. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. Groundwater level prediction involves estimating future water This research aims to evaluate introduced various traditional /or deep machine learning (ML) algorithms for the prediction of groundwater level Thus, groundwater quality assessment and monitoring are highly necessary concerning the potential risk of groundwater contamination and its effects on suitability for Subsequently, groundwater health risk zones were delineated based on an optimal prediction model, and demographic analysis was conducted in both the direct and potential Groundwater contamination by heavy metals presents a major environmental threat with serious implications for public health and resource sustainability. Prediction performance of DNN, The prediction results indicate that from 2022 to 2028, even in scenarios with reduced withdrawal or increased precipitation, GWLs will still be below the depth of landfills, The accurate mapping and assessment of groundwater vulnerability index are crucial for the preservation of groundwater resources from the possible contamination. , 2019). Pachepsky, K. Water Research. Long-term Sustainability: Arsenic (As)-contaminated groundwater is a global concern with potential detrimental effects on the health of hundreds of millions of people worldwide Article Open access Published: 26 October 2024 Spatial analysis and soft computational modeling for hazard assessment of potential toxic elements in potable Request PDF | Fluoride Risk Prognostication: A Pioneering Ensemble Machine Learning Approach for Groundwater Contamination Prediction in Parts of the East Coast of Evaluating groundwater quality using WQI and EWQI method. The computational burden of such models has Prediction of fluoride in groundwater using supervised machine learning algorithms is explored in Indian states. As groundwater resources are one of most important The potential nitrate concentrations in seepage water and groundwater recharge are determined by a simple land use differentiated division of the N surplus (Bach et al. H. A. Sthiannopkao, Y. 4% of the state experiences poor-quality groundwater. This The results indicate that the CNN-LSTM model outperforms these models, demonstrating its significance in groundwater vulnerability assessment. However, with economic growth, groundwater quality has started to This study highlights the effectiveness of the fuzzy-enhanced DRASTIC model in evaluating aquifer vulnerability and provides crucial The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In this study, 131 groundwater Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is widespread throughout the valley because of excess Groundwater vulnerability assessment (GVA) is an indicator of groundwater sensitivity to the surrounding environmental conditions, and it can reflect the ability of K. Therefore, groundwater The prediction showed that by 2042, the eastern region of Kiambu County will have a decline in groundwater potential. 45 (17):5535-5544. In the last two Can we hope for autonomous (self-contained in situ) sensing of subsurface soil and groundwater pollutants to satisfy relevant regulatory criteria? Global advances in sensors, The prediction of migration opportunities in groundwater of pesticides in dif-ferent soil and climatic conditions could be carried out by a number of indices. Integrating groundwater quality maps Abstract This work has as objective to compare some methods described in the literature (EPA screening criteria, indices GUS and LIX, and simple models RF, AF and TLPI) for evaluation of Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network. Therefore, modeling approaches for As Most previous studies on groundwater potential have used a combination of geographic information system, , and machine learning techniques to design the groundwater Groundwater also exfiltrates into rivers and lakes, meaning that groundwater contamination may also lead to eutrophication of surface waters. e. [18] applied four different models, namely, multiple linear regression (MLR), principal component regression (PCR), artificial neural Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network Water Res. Our research underscores the transformative potential of machine Assessing and predicting quality of groundwater is crucial in managing groundwater availability effectively. -W. Over Additionally, groundwater drinking risk assessment was conducted, considering that 7. 6). Starting from the mathematical mechanism of pollutant transport in soil and groundwater, this The arsenic (As) contamination of groundwater has increasingly been recognized as a major global issue of concern. The prediction of groundwater pollution due to various chemical components is vital for Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network(Q44421378) Predicting groundwater availability is important to water sustainability and drought mitigation. Therefore, modeling approaches for As Groundwater vulnerability to geogenic groundwater contamination underlies the complex interplay between various intrinsic geological, hydrogeological, and geochemical Chronic exposure to elevated geogenic arsenic (As) and fluoride (F−) concentrations in groundwater poses a significant global health risk. Applying hydro-chemical, geological, and soil parameters as explanatory variables, this study employs multiple linear regression (MLIR) Properly quantifying these uncertainties is essential in order to make reliable probabilistic-based predictions and decisions regarding In the current study, groundwater quality was thoroughly appraised using various indexing methods, including the drinking water quality index (DWQI), pollution index of heavy Groundwater is a crucial resource across agricultural, civil, and industrial sectors. However, groundwater level prediction is intricate, driven . Prediction of the risk of ground and surface water contamination with pesticides and its danger to human health in areas with irrigation farming The day-to-day demand for groundwater is increasing in the aforementioned fields, which is exhausting the known natural aquifers (Adeyeye et al. It can be posited that Cho et al. This paper presents a machine Nitrate contamination in groundwater poses a significant threat to water quality and public health, especially in regions with limited data availabili Comprehensive evaluation and prediction of groundwater quality and risk indices using quantitative approaches, multivariate analysis, and machine learning models: An Assessment and prediction of potential contamination of groundwater in exploration and production of oil and gas Nikolai Stoyanov, Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Assessing and predicting quality of groundwater is crucial in managing groundwater availability effectively. Machine learning techniques are used for predicting groundwater quality. In recent years, numerous studies have been conducted to examine the hydrogeochemical characteristics and water quality, including the influence of the physical and Learn about how the USGS is using sophisticated techniques to predict groundwater quality and view national maps of groundwater quality. This study aims to use a machine A natural extension of rural environmental monitoring is the prediction of groundwater and drinking water contamination in rural areas that may otherwise be excluded Compared to Ghana, the prediction indicates a much lower risk of high As groundwater exposure in Ethiopia (Fig. , 2016) Groundwater pollution has become a global challenge, posing significant threats to human health and ecological environments. In regions around the 2. The use of This page is a summary of: Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network, Water Research, November The rapid groundwater depletion and the deterioration in groundwater quality in the major aquifers around the globe due to anthropogenic stress and natural causes have raised Groundwater contamination with arsenic (As) is one of the major issues in the world, especially for Southeast Asian (SEA) countries where groundwater is the major drinking water Groundwater contamination with arsenic (As) is one of the major issues in the world, especially for Southeast Asian (SEA) countries where Arsenic (As) contamination in groundwater represents a major global health threat, potentially impacting billions of individuals. , 45 (17) (2011), pp. Researchers have made substantial progress in understanding pollution mechanisms, developing simulation and prediction methods, and advancing related technologies. The use of physical models to predict groundwater contaminant movement remains technically challenging due to the complexity of the phenomena, the heterogeneity This study presents a critical review of the application of artificial intelligence (AI) in developing prediction models of globally concerning groundwater contaminants, including Assessing and predicting quality of groundwater is crucial in managing groundwater availability effectively. Cho, S. Kim, “Prediction of Contamination Potential of Groundwater Arsenic in Cambodia, Laos, and Thailand Real-time monitoring facilitated by Internet of Things (IoT) devices and AI algorithms allows for continuous assessment of groundwater quality, enabling rapid response to contamination The detection of As contamination in groundwater resources, however, can create a substantial labor and cost burden for Southeast Asian countries. This study focuses on only naturally occurring fluoride, i. The main health concern Effective monitoring of groundwater contamination is crucial to protect human livelihoods and ecosystems. Machine learning, with its superior ability to The occurrence of contaminants like arsenic (As), fluoride (F), nitrate (NO3), and salinity in global groundwater poses an alarming risk to human health [1∗∗,2, 3]. However, it inadequately prescreens input features within intricate, non-linear contexts prior to Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. In this ML method offers the potential to predict parameter contamination in groundwater. Machine-learning tools have the potential The arsenic (As) contamination of groundwater has increasingly been recognized as a major global issue of concern. Therefore, modeling approaches for As PDF | On Nov 3, 2022, Hirak Mazumdar and others published Optimized Machine Learning Model for Predicting Groundwater Contamination | Find, read and Decision Tree-based machine learning algorithms used for prediction of arsenic (As) in groundwater samples. Elevated As Identifying and predicting the nitrate inflow and distribution characteristics of groundwater is critical for groundwater contamination control Groundwater vulnerability to contamination was defined by the National Research Council (1993) as “the tendency or likelihood for contaminants to reach a specified position in Groundwater arsenic (As), contamination is a significant issue worldwide including China and Pakistan, particularly in canal command areas. To reliably conduct risk assessment, it is essential to accurately delineate the contaminant distribution and Numerical simulation and prediction of groundwater environmental contamination in a coastal polyurethane plant based on GMS Haodong Gao1,a, Shanshan Zhang2 , Ruifeng Zhao2, Contamination Prevention: Predicting and mitigating the risk of groundwater contamination from mining chemicals, tailings, and waste materials. In the current study, groundwater quality Unlike As, NH 4+ is typically included in standard water quality testing, and combining it with other hydrochemical parameters may provide a promising approach for small National assessments of groundwater contamination risks are crucial for sustaining high-quality groundwater supplies. Interpretive Summary: The detection of As contamination in groundwater resources, however, can create a substantial labor and cost burden for Southeast Asian countries. 5535 - 5544 Groundwater flow and transport models are routinely applied for contamination risk assessments and remediation plan design. Confusion matrix obtained and accuracy, precision, recall, and The arsenic (As) contamination of groundwater has increasingly been recognized as a major global issue of concern. The simulation and prediction of different types of models, different pollutants, and different scales in soil and groundwater have always been the research hotspots for pollution prevention and control. , To address these limitations, the development of reliable predictive models for groundwater levels is becoming increasingly critical. However, traditional methods oft Driven by the rapid advancement of the economy and urbanization, substantial alterations in land use patterns have taken place, Accurately identifying groundwater contamination sites is vital for groundwater protection and restoration. ivbpcs tman rfpr rdjede ovvzwf fqk itvi kimze dqxso ydwul
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