Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation

Abstract

A relatively new method of addressing different hydrological problems is the use of artificial neural networks (ANN). In groundwater management ANNs are usually used to predict the hydraulic head at a well location. ANNs can prove to be very useful because, unlike numerical groundwater models, they are very easy to implement in karstic regions without the need of explicit knowledge of the exact flow conduit geometry and they avoid the creation of extremely complex models in the rare cases when all the necessary information is available. With hydrological parameters like rainfall and temperature, as well as with hydrogeological parameters like pumping rates from nearby wells as input, the ANN applies a black box approach and yields the simulated hydraulic head. During the calibration process the network is trained using a set of available field data and its performance is evaluated with a different set. Available measured data from Edward's aquifer in Texas, USA are used in this work to train and evaluate the proposed ANN. The Edwards Aquifer is a unique groundwater system and one of the most prolific artesian aquifers in the world. The present work focuses on simulation of hydraulic head change at an observation well in the area. The adopted ANN is a classic fully connected multilayer perceptron, with two hidden layers. All input parameters are directly or indirectly connected to the aquatic equilibrium and the ANN is treated as a sophisticated analogue to empirical models of the past. A correlation analysis of the measured data 1144 I.C. Trichakis et al. is used to determine the time lag between the current day and the day used for input of the measured rainfall levels. After the calibration process the testing data were used in order to check the ability of the ANN to interpolate or extrapolate in other regions, not used in the training procedure. The results show that there is a need for exact knowledge of pumping from each well in karstic aquifers as it is difficult to simulate the sudden drops and rises, which in this case can be more than 6 ft (approx. 2 m). That aside, the ANN is still a useful way to simulate karstic aquifers that are difficult to be simulated by numerical groundwater models.

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

“…Cancelliere et al (2002) derived operating policy for an irrigation supply reservoir using mixture of dynamic programming and ANN which yielded optimized policy that could properly simulate the real system at all time periods. ANN applications have also been observed in groundwater hydrology problems (Nayak et al 2006;Ghose et al 2010;Sreekanth and Datta 2011;Trichakis and Nikolos 2011;Gaur et al 2012). Nikolos et al (2008) used ANNs combined with Differential Evolution algorithm to optimize pumping strategy to meet the demand considering environmental constraints in Greece.…”

Factor Analysis and Estimation Model of Water Consumption of Government Institutions in Taiwan

“…An advantage of ANNs is that they can correlate large and complex datasets [16,17]. An ANN was previously used to develop and assess a drinking water quality model, and a multilayer perceptron ANN was required in the hydrological modelling [18].…”

Measurement and prediction of karstic spring flow rates

“…Lekkas et al, 2004), groundwater level forecasting (e.g. Daliakopoulos et al, 2005;Trichakis et al, 2011), groundwater pollution prediction (e.g. Sahoo et al, 2005), determination of aquifer parameters (e.g.…”