Simulation and analysis of temporal changes of groundwater depth using time series modeling

Abstract

In the world's water scarce regions, groundwater as an important and strategic resource needs proper assessment. An accurate forecasting needs to be performed in order to make a better identification of fluctuating nature of groundwater levels. In this study, groundwater level fluctuations of Kabudarahang aquifer was synchronized and verified. Investigation was conducted with usage and application of time series models. Groundwater level data during 2003-2014 are used for calibration and analyses were performed using Box-Jenkins models. Residual error analysis and comparison of observed and calculated groundwater levels were performed. Then a prediction model for groundwater level in Kabudarahang aquifer developed. The model was used for predicting the groundwater level during 2014-2017. Model results showed that the groundwater level in this aquifer will endure a 5 m decline for the next three upcoming years.

Exploiting Earth Observation Data to Impute Groundwater Level Measurements with an Extreme Learning Machine

“…Traditionally, environmental data imputation used classical statistics-based time series modeling approaches-an excellent overview is presented in [4,5]. Examples of these methods include auto-regressive (AR), moving-average (MA), auto-regressive moving-average (ARMA), auto-regressive integrated moving-average (ARIMA), and seasonal auto-regressive integrated moving-average (SARIMA) methods, often combined with multiple linear regression to estimate model parameters [6][7][8]. Recently, other statistical methods have been reported in the literature, including decomposition approaches combined with Bayesian and other statistical models [9][10][11], other machine learning methods [12,13], kriging temporal data [14], and an ARMAX model based on the eigenstructure of aquifer dynamics [15].…”

“…The W 1 matrix and the b vector are saved for use later in the imputation step. Next, we solve for W 2 , beginning by computing A using Equation (6).…”

Temporal dependence of potentiometric levels and groundwater salinity in alluvial aquifer upon rainfall and evapotranspiration

“…In order to examine the effects of drought and precipitation on aquifer storage and to provide interpretation about the physical processes that control transient changes on water table levels, it is necessary to analyse the long-term measurements of groundwater levels (GARDNER; HEILWEIL, 2009), and the main information source about potential of hydrological stress within groundwater system (KHORASANI et al, 2016).…”

“…Aflatooni and Mardaneh (2011) and Khorasani et al (2016) used Box-Jenkings time series method to predict the future groundwater table fluctuations in correlation with rainfall data; and Neves, Costa and Monteiro (2016) used simple spectral analysis, continuous wavelet transform and singular spectral analysis for investigating temporal structure of groundwater, computation of autocorrelation function.…”

Groundwater flow modeling for impact assessment of port dredging works on coastal hydrogeology in the area of Al-Wakrah (Qatar)

“…Groundwater flow modeling has become an invaluable tool for assessing the impact of existing and future activities on groundwater resources (Lachaal et al 2012(Lachaal et al , 2013Jothibasu and Anbazhagan 2016;Seyedmohammadi et al 2016;Sajil Kumar 2016;Ehteshami et al 2016;Gopinath et al 2016;Mokarram 2016;Khorasani et al 2016) especially for the groundwater systems evolution caused by urbanization and growth (Calderhead et al 2012). Previous studies show the significant contribution of groundwater modeling to estimate the mine and tunnel construction impact in groundwater situation and quality (JaramilloNieves and Ge 2012;Hussien 2013;Chen and Jiao 2014;Jahanshahi and Zare 2015;Pujades et al 2016), and to estimate groundwater quality in relation with the port construction (Zammouri et al 2014).…”