Ten-year prediction of groundwater level in Karaj plain (Iran) using MODFLOW2005-NWT in MATLAB

Yousefi, Hossein; Zahedi, Sina; Niksokhan, Mohammad Hossein; Momeni, Marzieh

doi:10.1007/s12665-019-8340-y

Cited by 26 publications

(9 citation statements)

References 27 publications

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“…The advanced numerical methods are capable in modelling complex groundwater flow processes within a given domain. Groundwater level fluctuation has been modelled using physical-based numerical models in several studies (Kim et al, 2008;Wang et al, 2008;Borsi et al, 2013;Yousefi et al, 2019), however, large number of parameter to represent all the physical processes makes groundwater simulations with physical based modelling complex.…”

Section: Introductionmentioning

confidence: 99%

“…In most studies, application of simple to complex groundwater models have been demonstrated in large agricultural catchments and there are very few studies predicting groundwater levels in urban areas (Coulibaly et al, 2001;Daliakopoulos et al, 2005;Wang et al, 2014;Shaoo et al, 2017;Wunsch et al, 2018;Yousefi et al, 2019). A study by Lerner (2002) describes how urbanization affects groundwater cycle by way of changes to both the total water budget, and to pathways recharge.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble modelling framework for groundwater level prediction in urban areas of India

Yadav

Gupta

Patidar

et al. 2020

Science of The Total Environment

100

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India is facing the worst water crisis in its history, and major Indian cities which accommodates about 50% of its population will be among highly groundwater stressed cities by 2020. In past few decades, the urban groundwater resources declined significantly due to over exploitation, urbanization, population growth and climate change. To understand the role of these variables on groundwater level fluctuation, we developed a machine learning based modelling approach considering singular spectrum analysis (SSA), mutual information (MI), genetic algorithm (GA), artificial neural network (ANN), and support vector machine (SVM). The developed approach was used to predict the groundwater levels in Bengaluru, a densely populated city with declining groundwater water resources. The input data consist of groundwater levels, rainfall, temperature, NOI, SOI, NIÑO3 and monthly population growth rate, and were pre-processed using mutual information, genetic algorithm and lag analysis. Later, the optimized input sets were used in ANN and SVM to predict monthly groundwater level fluctuations. The results suggest that the machine learning based approach with data pre

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble modelling framework for groundwater level prediction in urban areas of India

Yadav

Gupta

Patidar

et al. 2020

Science of The Total Environment

100

View full text Add to dashboard Cite

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“…(2019), Yousefi et al . (2019), and Fang et al . (2019) have taken advantage of these techniques to study water resources.…”

Section: Introductionmentioning

confidence: 96%

Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks

Poursaeid

Mastouri

Shabanlou

et al. 2020

Water & Environment J

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In recent years, as a result of climate change as well as rainfall reduction in arid and semi‐arid regions, modelling qualitative and quantitative parameters belonging to aquifers has become crucially important. In Iran, as aquifers are treated as the most commonly used drinking water resources, modelling their qualitative and quantitative parameters is enormously important. In this paper, for the first time, values of salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, are simulated by means of four modern artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (WOSELM) as well as the MODFLOW software for a 15‐year period monthly. To develop the hybrid artificial intelligence models, the wavelet is employed. First, the effective lags in estimating the qualitative and quantitative parameters of the groundwater are identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models are developed by the selected input combinations and also the ACF and the PACF in the form of different lags for each of ELM, WAELM, OSELM and WOSELM methods. Then, the superior models in simulating the groundwater qualitative and qualitative parameters are detected by conducting a sensitivity analysis. To forecast the electrical conductivity (EC) by the best WOSELM model, the values of the Nash–Sutcliffe efficiency coefficient (NSC), Mean Absolute Error (MAE) and the scatter index (SI) are obtained to be 0.991, 18.005 and 4.28E‐03, respectively. In addition, the most effective lags in estimating these parameters are introduced. Subsequently, the results found by the MODFLOW model are compared with those of the artificial intelligence models and it is concluded that the latter are more accurate. For instance, the scatter index and Nash–Sutcliffe efficiency coefficient values calculated by WOSELM for TDS, respectively, are 5.34E‐03 and 0.991. Finally, an uncertainty analysis is conducted to evaluate the performance of different numerical models. For example, MODFLOW has an underestimated performance in simulating the salinity parameter.

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“…Countries in arid and semi-arid areas are more dependent on groundwater resources than other water resources (Zahedi, 2017;Yousefi et al 2019). However, this water resource has mainly been affected by climate change with altered precipitation regimes (Yousefi et al 2018).…”

Section: Introductionmentioning

confidence: 99%

A Scenario-Based Management of Water Resources and Supply Systems Using a Combined System Dynamics and Compromise Programming Approach

Momeni

Behzadian

Yousefi

et al. 2021

Water Resour Manage

Self Cite

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Long-term sustainability in water supply systems is a major challenge due to water resources depletion, climate change and population growth. This paper presents a scenario-based approach for performance assessment of intervention strategies in water resources and supply systems (WRSS). A system dynamics (SD) approach is used for modelling the key WRSS components and their complex interactions with natural and human systems and is combined with a multi-criteria decision analysis for sustainability performance assessment of strategies in each scenario. The scenarios combine population growth rates with groundwater extraction limits and three intervention strategies. The methodology was demonstrated on a real-world case study in Iran.Results show scenario-based analysis can provide suitable strategies leading to long-term sustainability of water resources for each scenario imposed on the water systems. For scenarios with either no threshold or one threshold of groundwater extraction limit, the only effective strategies for sustainable groundwater preservation are those involving agricultural water demand decrease with an average recovery rate of 130% for groundwater resources while other strategies

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Ten-year prediction of groundwater level in Karaj plain (Iran) using MODFLOW2005-NWT in MATLAB

Cited by 26 publications

References 27 publications

Ensemble modelling framework for groundwater level prediction in urban areas of India

Ensemble modelling framework for groundwater level prediction in urban areas of India

Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks

A Scenario-Based Management of Water Resources and Supply Systems Using a Combined System Dynamics and Compromise Programming Approach

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