Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi–Surua Inter-basin of Odisha, India

Mohanty, S.; Jha, Madan K.; Kumar, Ashwani; Panda, D. K.

doi:10.1016/j.jhydrol.2013.04.041

Cited by 128 publications

(58 citation statements)

References 39 publications

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“…Subsequently, their performance has been compared with data-driven models such as artificial neural networks (ANN) [DALIAKOPOULOS 2005;MASKEY et al 2000;MOHAMMADI 2008;MOHANTY et al 2013]. A comparative study suggests that conceptual-based models require many parameters for calibration and have large computation times.…”

Section: Previous Researchmentioning

confidence: 99%

“…As such, it is becoming increasingly important to improve the management of groundwater. MOHANTY et al [2013] suggest that an overall groundwater management strategy depends on various factors, including availability of accurate information, financial support, policy framing and implementation. However, another key component of groundwater management is being able to forecast groundwater levels with a high degree of accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Yadav¹,

Ch²,

Mathur³

et al. 2017

Journal of Water and Land Development

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Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

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Section: Previous Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Yadav¹,

Ch²,

Mathur³

et al. 2017

Journal of Water and Land Development

View full text Add to dashboard Cite

show abstract

“…Material and other parameters used in the simulations are given in Table 1. Validation of the developed numerical models was carried out using correlation coefficient (R), mean absolute error (MAE), root means square error (RMSE), Nash-Scutcliffe efficiency (NSE) [24], [25]. Response surface plots were generated on MATLAB platform.…”

Section: Figure 2 Experimental Setup For Extrude Deflectionsmentioning

confidence: 99%

“…Similarly, results (Table 3) of validation test for the extrude deflection model also gave correlation coefficients (R≈1.0 and NSE≈1.0, and prediction error was estimated to be 5.93%. Under ideal conditions the best-fit between predicted and measured values would yield MAE = 0, RMSE = 0, R = 1 and NSE = 1 [25]. The comparison between experimental, predicted, and industry model equation (3) values are presented on the response surface graphs of extrusion pressure based on parameters d, h. Figure 3 indicates that extrusion pressure decreased as die diameter is increased and good correlation between experimental values and model predictions of extrusion pressure, and that model predictions are higher than values obtained using the industry model equation (3) reported in [21].…”

Section: Model Validationmentioning

confidence: 99%

Numerical and Response Surface Interactions for Optimizing Extrusion Parameters

Terfa¹

2015

IJMSA

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An experimental program was undertaken to extrude a lead alloy on ELECompact-1500 compression machine.Extrusion variables were extrude diameter (d), die bearing length (h), and included die entrant angle Ө = 90 o . Using experimental values, numerical models were obtained to describe the relationship between extrusion variables and extrusion pressure and extrude deflection. The numerical models were then used to obtain the response pressure predictions for aluminum alloy. Results of validation tests indicated good correlation between predicted and experimental values. The predictions also compare favorably with values obtained by a similar second-order modified upper bound model frequently used in industry for estimating extrusion loads with prediction errors below 4%. Surface responses graphs of extrusion pressure and extrude deflection were also used to define the optimized field for interaction of extrusion parameters for minimizing extrusion loads and controlling extrudes deflection or bending. Owing to fewer input variables, the proposed models were considered convenient options for a quick estimate of extrusion loads and product curvature.

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“…15 The FDMs have been widely utilized in the groundwater problems too. Mohanty et al (2013) evaluated the performances of the finite difference groundwater model MODFLOW and the computational model artificial neural network (ANN) in the simulation of groundwater level in an alluvial aquifer system. They compared the results with field observed data and found that the numerical model is suitable for long-term predictions, whereas the ANN model is appropriate for short-term applications.…”

mentioning

confidence: 99%

Analysis of groundwater flow and stream depletion in the L-shaped fluvial aquifer

Lin¹,

Chang²,

Yeh³

2017

Preprint

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Abstract. Understanding the head distribution in aquifers is crucial for the evaluation of groundwater resources. This article develops an analytical model for describing flow induced by pumping in an L-shaped fluvial aquifer bounded by impermeable bedrocks and two nearly fully penetrating streams. A similar scenario for numerical studies was reported in Kihm et al. (2007).The water level of the streams is assumed to be linearly varying with distance. The aquifer is divided into two sub-regions and 10 the continuity conditions of hydraulic head and flux are imposed at the interface of the sub-regions. The steady-state solution describing the head distribution for the model without pumping is first developed by the method of separation of variables. Kihm et al. (2007). The SDR solution is employed to demonstrate its use as a design tool in determining well location for required amounts of SDR from nearby streams under a specific aquifer pumping rate. 20

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Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi–Surua Inter-basin of Odisha, India

Cited by 128 publications

References 39 publications

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Numerical and Response Surface Interactions for Optimizing Extrusion Parameters

Analysis of groundwater flow and stream depletion in the L-shaped fluvial aquifer

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