Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management

Kouziokas, Georgios N.; Chatzigeorgiou, Alexander; Perakis, Κonstantinos

doi:10.1007/s11269-018-2126-y

Cited by 48 publications

(18 citation statements)

References 27 publications

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“…The results of the NARX modeling approach obtained in this study generally agree with the previously conducted research in this field (e.g., [11][12][13][14][41][42][43][44]). In particular, the results confirm the suitability of NARX in efficiently modeling groundwater level fluctuations.…”

Section: Comparisons With Previous Studiessupporting

confidence: 90%

A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

Alsumaiei

2020

Water

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The application of a nonlinear autoregressive modeling approach with exogenous input (NARX) neural networks for modeling groundwater level fluctuation has been examined by several researchers. However, the suitability of NARX in modeling groundwater level dynamics in urbanized and arid aquifer systems has not been comprehensively investigated. In this study, a NARX-based modeling approach is presented to establish a robust water management tool to aid urban water managers in controlling the development of shallow water tables induced by artificial recharge activity. Temperature data series are used as exogenous inputs for the NARX network, as they better reflect the intensity of artificial recharge activities, such as excessive lawns irrigation. Input delays and feedback delays for the NARX networks are determined based on the autocorrelation and cross-correlation analyses of detrended groundwater levels and monthly temperature averages. The validation of the proposed approach is assessed through a rolling validation procedure. Four observation wells in Kuwait City are selected to test the applicability of the proposed approach. The results showed the superiority of the NARX-based approach in modeling groundwater levels in such an urbanized and arid aquifer system, with coefficient of determination (R2) values ranging between 0.762 and 0.994 in the validation period. Comparison with other statistical models applied to the same study area shows that NARX models presented here reduced the mean absolute error (MAE) of groundwater levels forecasts by 50%. The findings of this paper are promising and provide a valuable tool for the urban city planner to assist in controlling the problem of shallow water tables for similar climatic and aquifer systems.

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Section: Comparisons With Previous Studiessupporting

confidence: 90%

A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

Alsumaiei

2020

Water

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“…Among different ensemble learning methods, the gradient boosting model XGBoost is the most potent model (Osman et al, 2021). Kouziokas et al (2018) conducted a study in Pennsylvania to predict GWLs using a feedforward ANN paired with different types of optimization algorithms, which were resilient backpropagation (RB), LM, Scaled Conjugate Gradient (SCG), and BFGS Quasi-Newton (BFGS-QN). This study finds that all models produced sufficient GWL forecasting accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

Afan

Osman

Essam

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells. The results from S1 prove that the ensemble EDL generally performs superior to the DL in the estimation of GWL of each station using data of remaining four wells except the Paya Indah Wetland in which the DL method provide better estimates compared to EDL. Regarding S2, the EDL also exhibits superior performance in predicting daily GWL in all five stations compared to the DL model. Implementing EDL decreased the RMSE, NAE and RRMSE by 11.6%, 27.3% and 22.3% and increased the R, Spearman rho and Kendall tau by 0.4%, 1.1% and 3.5%, respectively. Moreover, EDL for S2 shows a high level of precision within less time lag, ranging between 2 and 4 compared to DL. Therefore, the EDL model has the potential in managing the sustainability of groundwater in Malaysia.

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“…The stochastic ARIMA models are widely used in water resources management applications, especially for modeling hydrological stream flows, groundwater level fluctuations, and drought patterns (Myronidis et al 2018;Takafuji et al 2018;Sakizadeh et al 2019;. Moreover, the ability of AI in hydrology and water resources management and for groundwater level modeling has been examined by many studies (Rakhshandehroo et al 2012;Ghose et al 2018;Kouziokas et al 2018;Guzman et al 2019;Lee et al 2019;Tang et al 2019). In particular, this study aims to simulate the fluctuations in the groundwater level of the Gaza coastal aquifer in light of the climate change consequences.…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling of spatial and temporal vulnerability of groundwater level in the Gaza Strip (Palestine)

et al. 2021

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The water supply in the Gaza Strip substantially depends on the groundwater resource of the Gaza coastal aquifer. The climate changes and the over-exploiting processes negatively impact the recovery of the groundwater balance. The climate variability is characterized by the decline in the precipitation of −5.2% and an increase in temperature of +1 °C in the timeframe of 2020–2040. The potential evaporation and the sunshine period are expected to increase by about 111 mm and 5 hours, respectively, during the next 20 years. However, the atmosphere is predicted to be drier where the relative humidity will fall by a trend of −8% in 20 years. The groundwater abstraction is predicted to increase by 55% by 2040. The response of the groundwater level to climate change and groundwater pumping was evaluated using a model of a 20-neuron ANN with a performance of the correlation coefficient (r)=0.95–0.99 and the root mean square error (RMSE)=0.09–0.21. Nowadays, the model reveals that the groundwater level ranges between −0.38 and −18.5 m and by 2040 it is expected to reach −1.13 and −28 m below MSL at the northern and southern governorates of the Gaza Strip, respectively.

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Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management

Cited by 48 publications

References 27 publications

A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

Statistical modeling of spatial and temporal vulnerability of groundwater level in the Gaza Strip (Palestine)

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