Transport modeling and multivariate adaptive regression splines for evaluating performance of ASR systems in freshwater aquifers

Forghani, Ali; Peralta, Richard C.

doi:10.1016/j.jhydrol.2017.08.012

Cited by 16 publications

(5 citation statements)

References 16 publications

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“…Brown et al (2016) formulated a new index for brackish water aquifers using the three best dimensionless parameters developed earlier. The term recovery effectiveness is developed by Forghani and Peralta (2017) for freshwater aquifers and is defined as the amount of recharged water recovered by the ASR well during the subsequent recovery period. However, the EE of these systems has not been considered in many studies.…”

Section: Methodsmentioning

confidence: 99%

Optimizing Multiwell Aquifer Storage and Recovery Systems for Energy Use and Recovery Efficiency

Majumdar

Miller

Sheng³

2021

Groundwater

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As more aquifer storage and recovery (ASR) systems are employed for management of water resources, the skillful operation of multiwell ASR systems has become very important to improve their performance. In this study, we developed MODFLOW and MT3DMS models to simulate a multiwell ASR system in a synthetic aquifer to assess effects of hydrogeological and operational factors on the performance of the multiwell ASR system. We evaluated a simplified (dual well) ASR system in comparison with complex system (three‐, four‐, five‐, and seven‐well systems). Recovery and energy efficiencies were calculated using the model simulations. Factors such as higher hydraulic conductivity and longitudinal dispersivity significantly reduced the recovery and energy efficiencies of the system. In contrast, increasing the volume of recharged water increased the recovery efficiency; however, the energy efficiency was reduced. Recovery and energy efficiencies also plummet when there is an increase in the underlying regional gradient and the designed storage duration. Operating the system multiple times can yield higher volume of potable water, but the energy efficiency may not vary significantly after the second operating cycle. Single‐well systems and multiwell systems exhibit similar responses to changes in physical factors, although operational factors have a more pronounced effect on the multiwell systems. One of the major findings was that fewer wells in a multiwell ASR system can yield higher volume of potable water and better output with respect to the electrical power being consumed. The results provide design engineers with guidelines for optimizing performance of the multiwell ASR systems.

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Section: Methodsmentioning

confidence: 99%

Optimizing Multiwell Aquifer Storage and Recovery Systems for Energy Use and Recovery Efficiency

Majumdar

Miller

Sheng³

2021

Groundwater

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“…Multivariate Adaptive Regression Splines (MARS) is a multi-variable non-parametric regression analysis for fitting the relationship between a set of input variables and dependent variables introduced by Friedman (1991). Recently the MARS as a powerful regression technique has been used for modeling of different types of data (Rezaie-balf, 2019) (Heddam and Kisi, 2018) (Safari, 2019) (Emamgolizadeh et al, 2015) (Forghani and Peralta, 2017). In this method the training data sets are partitioned into separate regions, and each one gets its own regression line called basis functions.…”

Section: Multivariate Adaptive Regression Spline (Mars)mentioning

confidence: 99%

Implementing and evaluating various machine learning models for pipe burst prediction

Ravanbakhsh

Momeni

Robati

2021

Preprint

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Abstract. By accurate predicting of pipe bursts, it is possible to schedule pipe maintenance, rehabilitation and improve the level of services in water distribution networks (WDNs). In this study, we aimed to implement five artificial intelligence and machine learning regression models such as multivariate adaptive regression splines (MARS), M5' regression tree (M5'), Least square support vector regression (LS-SVR), fuzzy regression based on c-means clustering (FCMR) and regressive convolution neural network with support vector regression (RCNN-SVR) for predicting pipe burst rate and evaluating the performance of these models. The most effective parameters for regression models are pipes age, diameter, depth of installation, length, average and maximum hydraulic pressure. In the present study, collected data include 158 cases for polyethylene (PE) and 124 cases for asbestos cement (AC) pipes during 2012-2019. The results indicate that the RCNN-SVR model has a great performance of pipe burst rate (PBR) prediction.

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“…Multivariate Adaptive Regression Splines (MARS) is widely applied in different water resources engineering problems as a powerful regression technique (Forghani and Peralta, 2017;Heddam and Kisi, 2018;Fathian et al, 2019;Safari, 2019). The superior performance of MARS on variety of machine learning problems has been reported by Kumar and Wahid (2017).…”

Section: Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

Safari

Arashloo

Mehr

2020

Journal of Hydrology

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Transport modeling and multivariate adaptive regression splines for evaluating performance of ASR systems in freshwater aquifers

Cited by 16 publications

References 16 publications

Optimizing Multiwell Aquifer Storage and Recovery Systems for Energy Use and Recovery Efficiency

Optimizing Multiwell Aquifer Storage and Recovery Systems for Energy Use and Recovery Efficiency

Implementing and evaluating various machine learning models for pipe burst prediction

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

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