Modeling of groundwater level changes in an urban area

Akter, Aysha; Ahmed, Shoukat

doi:10.1007/s40899-020-00480-x

Cited by 12 publications

(9 citation statements)

References 31 publications

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“…[65] used a combined hydrological-hydrogeological model, i.e., employed MODFLOW, under climate change scenarios, defining the spatiotemporal dynamics of water balance and groundwater-surface water (GW-SW) interactions for the upper stream basin of Del Azul. Groundwater numerical models with a traditional rectilinear grid geometry, such as MODFLOW [66], have rarely been used to simulate aquifer test results at a pumping well, in contrast to analytical models, because they are not designed or expected to accurately reproduce the head gradient near the well. MOD-FLOW is a groundwater modeling approach based on mathematical models.…”

Section: Groundwater Modelling With Modflowmentioning

confidence: 99%

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

et al. 2022

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With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes.

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Section: Groundwater Modelling With Modflowmentioning

confidence: 99%

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

et al. 2022

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“…The use of groundwater to supply Dhaka already causes concerns over regional impacts of contaminant migration and potential contamination of deep aquifers 25 . Groundwater depletion may also be a problem in other large cities such as Chittagong 80 . Urban water demand is projected to increase to approximately double the current level.…”

Section: Discussionmentioning

confidence: 99%

The impact of climate change, population growth and development on sustainable water security in Bangladesh to 2100

Kirby

Mainuddin

2022

Sci Rep

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There are concerns that groundwater use for irrigation and for urban water supply is unsustainable in some parts of Bangladesh, particularly in the agriculturally productive northwest region. We use an integrated population – GDP – food – water model to examine water demand to 2100 in Bangladesh in development scenarios relevant to food and water security. The results indicate that irrigation water demand is projected to increase in coming decades, but later in the century it may decrease due to increasing crop yields and a falling population. The increased demand is greatest in the northwest region and, if unchecked, would increase concerns there about the unsustainable use of groundwater. The growth in demand is determined particularly by growth in crop yields, population growth and the fraction of food demand satisfied by imports. An extreme hot-dry climate change scenario has a lesser impact. This suggests that, in principle, Bangladesh can offset the impacts of climate change on irrigation water demand through its domestic policies. Urban water use currently also leads to concerns over unsustainable groundwater use. Our results suggest that urban water demand is likely to grow proportionately significantly more than irrigation water demand. Alternative sources for urban water are therefore urgently required.

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“…Ansarifar et al [16] employed MODFLOW mathematical approach to simulate the groundwater level in a coastal aquifer. Akter and Ahmed [17] used MODFLOW 2005 mathematical model to study water level drawdown and groundwater modeling. They showed that mathematical models could show pertinent details and save money and time.…”

Section: Introductionmentioning

confidence: 99%

Modeling of daily groundwater level using deep learning neural networks

Othman

2023

Turkish Journal of Engineering

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Groundwater is an essential water source, becoming more vital due to shortages in available surface water resources. Hence, monitoring groundwater levels can show the amount of water available to extract and use for various purposes. However, the groundwater system is naturally complex, and we need models to simulate it. Therefore, we employed a deep learning model called CNN-biLSTM neural networks for modeling grounding, and the data was obtained from USGS. The data included daily groundwater levels from 2002 to 2021, and the data was divided into 95% for training and 5% for teasing. Besides, three deep CNN-biLSTM models were employed using three different algorithms (SGDM, ADAM, and RMSprop. Also, Bayesian optimization was used to optimize parameters such as the number of biLSTM layers and the number of biLSTM units. The model's performance was based on Spearman's Rank-Order Correlation (r), and the model with SGDM showed the best results compared to other models in this study. Finally, the CNN model with LSTM can simulate time series data effectively.

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Modeling of groundwater level changes in an urban area

Cited by 12 publications

References 31 publications

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

The impact of climate change, population growth and development on sustainable water security in Bangladesh to 2100

Modeling of daily groundwater level using deep learning neural networks

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