Impact of large diameter recharge wells for reducing groundwater depletion rates in an urban area of Lahore, Pakistan

Ashiq, Muhammad Masood; Rehman, Habib ur; Khan, Noor Muhammad

doi:10.1007/s12665-020-09144-7

Cited by 15 publications

(3 citation statements)

References 23 publications

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“…In addition, local scour could be critical in developing offshore wind and hydrokinetic turbine farms (Mutlu, 2007). Various studies were conducted using numerical modeling, a mathematical model, to solve different problems (Ashiq and Khan, 2020;Ebrahimi and Khorram, 2021;Tunc et al, 2022). Hence, many studies were conducted to study scour around bridge piers.…”

Section: Figure 1 An Illustration Showing Scouring Around a Bridge Piermentioning

confidence: 99%

Machine Learning Model for Estimation of Local Scour Depth around Cylindrical Bridge Piers

Ahmed¹,

Waheed²,

Ashiq³

et al. 2022

IJCE

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Scour around bridge piers is a well-known threat to bridge stability worldwide. It can cause losses in lives and the economy, especially during floods. Therefore, an artificial intelligence approach called artificial neural network (ANN) was used to predict the scour depth around bridge piers. The ANN model was trained with laboratory data, including pier width, flow velocity, particle diameter, sediment critical velocity, flow depth, and scour depth. The data was divided into 70% for training, 15 for validation, and 15% for testing. Besides, the ANN model was trained using various training algrthins and a single hidden layer with 20 neurons in the hidden layer. The results showed that the ANN model with Bayesian regularization backpropagation training algorithm provides a better predicted scour depth with a correlation coefficient (R) equal to 0. 9692 and 0.926 for training and test stages, respectively. Besides, it showed a low mean squared error (MSE), which was 0.0034 for training and 0.0066 for the test. These results were slightly better than the ANN with Levenberg-Marquardt backpropagation with R training equals 0.9552 (MSE training = 0.0047), and R test equals 0.838 (MSE test = 0.007).On the other hand, the ANN model with a scaled conjugate gradient backpropagation training algorithm showed worse predictions (R training = 0.7407 and R test = 0.6409). Besides, the ANN model shows better outcomes than the linear regression model. Finally, the sensitivity analysis has shown that the pier width is the most crucial parameter for estimating scour depth using the ANN model.

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Section: Figure 1 An Illustration Showing Scouring Around a Bridge Piermentioning

confidence: 99%

Machine Learning Model for Estimation of Local Scour Depth around Cylindrical Bridge Piers

Ahmed¹,

Waheed²,

Ashiq³

et al. 2022

IJCE

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“…As the demand for water is increasing by 10% annually, the surface water supply is becoming insufficient and non-viable because the Indus water basin is considered the most depleted river basin globally, exerting pressure on groundwater sources. Currently, 50% of water requirements are fulfilled by groundwater abstraction, which ascertains the need for systematic groundwater budgeting and mapping (Sharma et al 2013;Qureshi et al 2010;Reig et al 2013; Watto and Mugera 2016; Ashiq and Khan 2020). Thus, this study undertakes the area of Karachi, Pakistan as a case study that faces an extreme shortage of groundwater and performs groundwater budgeting and mapping.…”

Section: Introductionmentioning

confidence: 99%

Groundwater budgeting of Nari and Gaj formations and groundwater mapping of Karachi, Pakistan

et al. 2022

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Groundwater depletion is an emerging problem worldwide due to changes in climate and an increase in urbanization. Two significant water-bearing formations, the Oligocene-aged Nari and the Miocene-aged Gaj, were utilized as a case study exposed near Karachi, Pakistan. Groundwater budgeting was performed through a classical equation. The inflow of groundwater in the formations was calculated by thermo-pluviometric data and water loss of Hub Dam. The potential of evapotranspiration (PET) was calculated by the Thornthwaite method. The groundwater inflow from Hub Dam was estimated by using 20 years of annual water loss data by removing PET. The total mean annual inflow of groundwater in the formations was 2414.12 US Gallons per Second (gps). The annual mean outflow was estimated by calculation of groundwater usage for industries and domestic purposes and the mean annual groundwater outflow was 5562.61 US gps and an annual deficit of groundwater was 3148.5 US gps. The research is composed of validating the groundwater budget. Direct Current Electrical Resistivity (DCER) and static water level data from existing industrial wells were used for groundwater maps. The DCER data indicates A-Type and K-Type sub-surface with high resistivity in the three-layer model. The average water table of residential areas in 2019 was 60 m and in industrial areas was 130 m. The oscillation of the groundwater table over the last 20 years and the deficit of the groundwater budget shows an alarming condition for the future. If the same scenario persists, then by 2025, the water table will decline up to 140 m.

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“…Rivers passing through agricultural, industrial, and urban areas are particularly exposed to contamination by various pollutants, which are then carried downstream to their estuaries. As a result, these important surface water resources suffer a high level of threat (Ashiq et al, 2020;Ghoochani et al, 2022;Kerachian & Karamouz, 2007;Meng et al, 2017;Pereira Louzadavalory et al, 2015;Rehana & Mujumdar, 2011). River water quality monitoring and simulation play a significant role in ensuring the sustainable use of water resources and improving human health and the environment.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of hydrological variabilities of water quality models considering event-based scenarios: A case study

Ebrahimi,

Azizi,

Khorram

et al. 2024

Stoch Environ Res Risk Assess

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Given the significant momentum of developing water quality models to simulate water quality variables and support decision-making, the literature recognized the importance of addressing uncertainties embedded in the water quality models, such as inherent, parametric, and hydrological uncertainties. However, hydrological events' magnitude in terms of intensity has not been extensively scrutinized in previous studies. Hence, this paper aims to propose an adjusted Monte Carlo event-based scenarios framework that considers four scenarios (1-35 years of flow rate records, 2-events with ARI ≤ 10 years, 3events with ARI ≤ 5 years, 4events with ARI ≤ 1) to evaluate hydrological variabilities and quantify embedded uncertainties. The study employs a Qual2k model that simulates five water quality variables in the Zaroub river, Iran, as a case study. The model's uncertainty boundaries are quantified using five statistical metrics: Plevel, ARIL, SU, CU, and EU. The results of the study considering extreme hydrological events when examining the flow rate time histories leads to a significant increase in uncertainty in the water quality model.On the other hand, reducing the ARI values of hydrological events not only minimizes the uncertainty boundaries but also improves the accuracy of the model simulations. These findings highlight the crucial role of selecting hydrological scenarios based on the water quality variable under investigation. Furthermore, the proposed framework can be applied to any water quality model and water body. The study's outcomes suggest that the presented methodology reduces uncertainty and provides more reliable simulations for decision-making in water resources conundrums.

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Impact of large diameter recharge wells for reducing groundwater depletion rates in an urban area of Lahore, Pakistan

Cited by 15 publications

References 23 publications

Machine Learning Model for Estimation of Local Scour Depth around Cylindrical Bridge Piers

Machine Learning Model for Estimation of Local Scour Depth around Cylindrical Bridge Piers

Groundwater budgeting of Nari and Gaj formations and groundwater mapping of Karachi, Pakistan

Evaluation of hydrological variabilities of water quality models considering event-based scenarios: A case study

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