Abstract
Groundwater drawdown is typically measured using pumping tests and field experiments; however, the traditional methods are time-consuming and costly when applied to extensive areas. In this research, a methodology is introduced based on artificial neural network (ANN)s and field measurements in an alluvial aquifer in the north of Iran. First, the annual drawdown as the output of the ANN models in 250 piezometric wells was measured, and the data were divided into three categories of training data, cross-validation data, and test data. Then, the effective factors in groundwater drawdown including groundwater depth, annual precipitation, annual evaporation, the transmissivity of the aquifer formation, elevation, distance from the sea, distance from water sources (recharge), population density, and groundwater extraction in the influence radius of each well (1000m) were identified and used as the inputs of the ANN models. Several ANN methods were evaluated, and the predictions were compared with the observations. Results show that the modular neural network (MNN) showed the highest performance in modeling groundwater drawdown (Training R-sqr = 0.96, test R-sqr = 0.81). The optimum network was fitted to available input data to map the annual drawdown across the entire aquifer. The accuracy assessment of the final map yielded favorable results (R-sqr = 0.8). The adopted methodology can be applied for the prediction of groundwater drawdown in the study site and similar settings elsewhere.
Highlights
Groundwater is the major water resource in arid and semi-arid areas of the world since these resources are less affected by evaporation and pollutants (Wada et al 2010)
This study aims to evaluate several artificial neural network (ANN) methods to predict the spatial variation of the annual groundwater drawdown using its affecting factors in an alluvial aquifer on the southern coasts of the Caspian Sea
The mean annual drawdown rates of groundwater in the study region according to the statistics of groundwater depth fluctuations in 250 piezometric wells was between 0.15 to 4.37 meters per year
Summary
Groundwater is the major water resource in arid and semi-arid areas of the world since these resources are less affected by evaporation and pollutants (Wada et al 2010). The increase in human population and agricultural and industrial developments have led to an increase in groundwater withdrawal, pollution, and depletion of these resources (Shivasorupy et al 2012). Access to groundwater fluctuations data and aquifer drawdown is essential for water resources management plans. These data are often constrained by spatial and temporal gaps in many parts of the world. The use of artificial intelligence (AI) based models in groundwater studies has been growing in the past decade. AI-based models have been used in groundwater quality studies (Chou 2006; Chou 2007; Han et al 2011; Wang et al 2014; Li et al 2020; Maliqi et al 2020; Pal and Chakrabarty 2020; Mosaffa et al 2021), groundwater depth studies (Dixon 2004; Awasthi et al 2005; Saemi and Ahmadi 2008; Shiri et al 2013; Gong et al 2018; Chen et al 2020), and the other hydrological studies (Smith and Eli 1995; Dawson 1998; Cheng et al 2002; Chau and Cheng 2002; Mehr et al 2003; Wilby et al 2003; Jain et al 2004; Anctil and Rat 2005; Cheng et al 2005; Peters et al 2006; Demirel et al 2009; Nourani et al 2011; Can et al 2012; Demirel et al 2012; Kisi et al 2013; Nourani et al 2013; Kisi 2015; Taormina and Chau 2015; Nourani et al 2017 & 2018; Nourani et al 2019 a&b)
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