Application of Artificial Neural Networks to Complex Groundwater Management Problems

Coppola, Emery A.; Poulton, Mary M.; Charles, Emmanuel G.; Dustman, John; Szidarovszky, Ferenc

doi:10.1023/b:narr.0000007808.11860.7e

Cited by 59 publications

(41 citation statements)

References 11 publications

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“…Ref. [18] applied the ANN model on three types of groundwater and management problems and affirmed the efficiency after using ANN. Ref.…”

Section: Introductionmentioning

confidence: 93%

Identification of Unknown Groundwater Pollution Sources and Determination of Optimal Well Locations Using ANN-GA Based Simulation-Optimization Model

Leichombam¹,

Bhattacharjya²

2016

JWARP

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The linked simulation-optimization model can be used for solving a complex groundwater pollution source identification problem. Advanced simulators have been developed and successfully linked with numerous optimization algorithms for identification of groundwater pollution sources. However, the identification of pollution sources in a groundwater aquifer using linked simulation-optimization model has proven to be computationally expensive. To overcome this computational burden, an approximate simulator, the artificial neural network (ANN) model can be used as a surrogate model to replace the complex time-consuming numerical simulation model. However, for large-scale aquifer system, the performance of the ANN-based surrogate model is not satisfactory when a single ANN model is used to predict the concentration at different observation locations. In such a situation, the model efficiency can be enhanced by developing separate ANN model for each of the observation locations. The number of ANN models is equal to the number of observation wells in the aquifer. As a result, the complexity of the ANN-based simulation-optimization model will be related to the number of observation wells. Thus, this study used a modified formulation to find out the optimal numbers of observation wells which will eventually reduce the computational time of the model. The performance of the ANN-based simulation-optimization model is evaluated by identifying the groundwater pollutant sources of a hypothetical study area. The limited evaluation shows that the model has the potential for field application.

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“…Ref. [18] applied the ANN model on three types of groundwater and management problems and affirmed the efficiency after using ANN. Ref.…”

Section: Introductionmentioning

confidence: 93%

Identification of Unknown Groundwater Pollution Sources and Determination of Optimal Well Locations Using ANN-GA Based Simulation-Optimization Model

Leichombam¹,

Bhattacharjya²

2016

JWARP

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“…Their study incorporated the subsurface water system into the management model using the response matrix approach. However, many studies applied the artificial neural networks (ANNs) to model hydrology field complexity [17][18][19][20][21][22] including rainfall-runoff modeling [22,23] and groundwater flow and transport [24]. The current work trained an ANN to predict the time-varying subsurface water level in response to management alternatives [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…The current work trained an ANN to predict the time-varying subsurface water level in response to management alternatives [18][19][20][21]. Coppola et al [18] trained an ANN with MODFLOW simulation data to predict subsurface water levels at locations under various pumping conditions. The ANN forecasted subsurface water levels at the next time based on management alternatives including control and state variables at the current time.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Inference System for the Conjunctive Use of Surface and Subsurface Water

Chang

Chu

Chen

2013

Advances in Fuzzy Systems

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This study develops the water resources management model for conjunctive use of surface and subsurface water using a fuzzy inference system (FIS). The study applies the FIS to allocate the demands of surface and subsurface water. Subsequently, water allocations in the surface water system are simulated by using linear programming techniques, and the responses of subsurface water system with respect to pumping are forecasted by using artificial neural networks. The operating rule for the water systems is that the more abundant water system supplies more water. By using the fuzzy rule, the FIS conjunctive use model easily incorporates expert knowledge and operational polices into water resources management. The result indicates that the FIS model is more effective and efficient when compared with the decoupled conjunctive use and simulation-optimization models. Furthermore, the FIS model is an alternative way to obtain the conjunctive use policies between surface and subsurface water.

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“…Time series modeling using ANN has been a particular focus of interest and better performing models have been reported in a diverse set of fields that include rainfall-runoff modeling [8][9][10][11][12] and groundwater level prediction [13][14][15][16]. It is also reported that ANN models are not very satisfied in precision for forecasting because it considered only few aspects of time series property [17].…”

Section: Introductionmentioning

confidence: 99%

Time Series Modeling of River Flow Using Wavelet Neural Networks

Krishna¹,

Rao²,

Nayak³

2011

JWARP

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A new hybrid model which combines wavelets and Artificial Neural Network (ANN) called wavelet neural network (WNN) model was proposed in the current study and applied for time series modeling of river flow. The time series of daily river flow of the Malaprabha River basin (Karnataka state, India) were analyzed by the WNN model. The observed time series are decomposed into sub-series using discrete wavelet transform and then appropriate sub-series is used as inputs to the neural network for forecasting hydrological variables. The hybrid model (WNN) was compared with the standard ANN and AR models. The WNN model was able to provide a good fit with the observed data, especially the peak values during the testing period. The benchmark results from WNN model applications showed that the hybrid model produced better results in estimating the hydrograph properties than the latter models (ANN and AR).

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Application of Artificial Neural Networks to Complex Groundwater Management Problems

Cited by 59 publications

References 11 publications

Identification of Unknown Groundwater Pollution Sources and Determination of Optimal Well Locations Using ANN-GA Based Simulation-Optimization Model

Identification of Unknown Groundwater Pollution Sources and Determination of Optimal Well Locations Using ANN-GA Based Simulation-Optimization Model

A Fuzzy Inference System for the Conjunctive Use of Surface and Subsurface Water

Time Series Modeling of River Flow Using Wavelet Neural Networks

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