Using statistical and artificial neural network models to forecast potentiometric levels at a deep well in South Texas

Uddameri, Venkatesh

doi:10.1007/s00254-006-0452-5

Cited by 60 publications

(29 citation statements)

References 15 publications

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“…Thus, in recent years, artificial neural network (ANN) models have been increasingly utilized for groundwater level forecasting (e.g. Daliakopoulos et al 2005;Nayak et al 2006;Uddameri 2007;Krishna et al 2008;Tsanis et al 2008;Banerjee et al 2009;Sreekanth et al 2009;Sethi et al 2010;Adamowski and Chan 2011;Taormina et al 2012). ANN models are often described as "black box" models which can provide relatively accurate predictions of groundwater levels even with limited data sets.…”

Section: Introductionmentioning

confidence: 98%

Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models

et al. 2014

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Several groundwater-level forecasting studies have shown that data-driven models are simpler, faster to develop, and provide more accurate and precise results than physical or numerical-based models. Five data-driven models were examined for the forecasting of groundwater levels as a result of recharge via tailings from an abandoned mine in Quebec, Canada, for lead times of 1 day, 1 week and 1 month. The five models are: a multiple linear regression (MLR); an artificial neural network (ANN); two models that are based on de-noising the model predictors using the wavelet-transform (W-MLR, W-ANN); and a W-ensemble ANN (W-ENN) model. The tailing recharge, total precipitation, and mean air temperature were used as predictors. The ANN models performed better than the MLR models, and both MLR and ANN models performed significantly better after denoising the predictors using wavelet-transforms. Overall, the W-ENN model performed best for each of the three lead times. These results highlight the ability of wavelettransforms to decompose non-stationary data into discrete wavelet-components, highlighting cyclic patterns and trends in the time-series at varying temporal scales, rendering the data readily usable in forecasting. The good performance of the W-ENN model highlights the usefulness of ensemble modeling, which ensures model robustness along with improved reliability by reducing variance. a Original time series b Approximation component at the seventh decomposition level c Approximation component at the ninth decomposition level d Based on the approximation component at the sixth decomposition level 132

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Section: Introductionmentioning

confidence: 98%

Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models

et al. 2014

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“…Compared to surface water hydrology, relatively less number of studies on ANN application in groundwater hydrology has been reported in the literature. In groundwater hydrology, the neural network technique has been used for aquifer parameter estimation (Aziz and Wong 1992;Morshed and Kaluarachchi 1998;Balkhair 2002;Shigdi and Garcia 2003;Garcia and Shigdi 2006;Samani et al 2007;Karahan and Ayvaz 2008), groundwater quality prediction (Hong and Rosen 2001;Milot et al 2002;Kuo et al 2004), and groundwater level prediction (Coulibaly et al 2001;Coppola et al 2003Coppola et al , 2005Daliakopoulos et al 2005;Nayak et al 2006;Uddameri 2007;Krishna et al 2008;Banerjee et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Mohanty

Jha

Kumar

et al. 2009

Water Resour Manage

179

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Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg-Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to 1846 S. Mohanty et al. that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.

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“…Particularly, in groundwater hydrology, the neural network technique has been used for aquifer parameter estimation (Aziz and Wong 1992;Morshed and Kaluarachchi 1998;Balkhair 2002;Shigdi and Garcia 2003;Garcia and Shigdi 2006;Samani et al 2007;Karahan and Ayvaz 2008;Viveros and Parra 2014), groundwater quality prediction (Hong and Rosen 2001;Milot et al 2002;Kuo et al 2004;Banerjee et al 2011;Chang et al 2013), and groundwater level prediction (Coulibaly et al 2001;Coppola et al 2003Coppola et al , 2005Daliakopoulos et al 2005;Nayak et al 2006;Uddameri 2007;Krishna et al 2008;Ghose et al 2010;Mohanty et al 2010;Yoon et al 2011;Taormina et al 2012;Sahoo and Jha 2013;He et al 2014;Emamgholizadeh et al 2014). In most of the past studies on ANN modeling of groundwater level, ANN models were developed for simulating groundwater level in a single well or in a few wells only.…”

Section: Introductionmentioning

confidence: 98%

Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites

Mohanty

Jha

Raul³

et al. 2015

Water Resour Manage

111

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Reliable forecast of groundwater level is necessary for its sustainable use and for planning land and water management strategies. This paper deals with an application of artificial neural network (ANN) approach to the weekly forecasting of groundwater levels in multiple wells located over a river basin. Gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm was employed to predict groundwater levels 1 week ahead at 18 sites over the study area. Based on the domain knowledge and pertinent statistical analysis, appropriate set of inputs for the ANN model was selected. This consisted of weekly rainfall, pan evaporation, river stage, water level in the surface drain, pumping rates of 18 sites and groundwater levels of 18 sites in the previous week, which led to 40 input nodes and 18 output nodes. During training of the ANN model, the optimum number of hidden neurons was found to be 40 and the model performance was found satisfactory (RMSE= 0.2397 m, r=0.9861, and NSE=0.9722). During testing of the model, the values of statistical indicators RMSE, r and NSE were 0.4118 m, 0.9715 and 0.9288, respectively. Using the same inputs, the developed ANN model was further used for forecasting groundwater levels 2, 3 and 4 weeks ahead in 18 tubewells. The model performance was better while forecasting groundwater levels at shorter lead times (up to 2 weeks) than that for larger lead times.

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Using statistical and artificial neural network models to forecast potentiometric levels at a deep well in South Texas

Cited by 60 publications

References 15 publications

Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models

Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models

Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites

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