Groundwater level forecasting by Artificial Neural Network

Groundwater level forecasting by Artificial Neural Network

Abstract

A proper design of the architecture of Artificial Neural Network (ANN) models can provide a robust tool in water resources modeling and forecasting. The performance of different neural networks in a groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the decreasing trend of the groundwater level and provide acceptable predictions up to 18 months ahead. Messara Valley in Crete (Greece) was chosen as the study area as its groundwater resources have being overexploited during the last fifteen years and the groundwater level has been decreasing steadily. Seven different types of network architectures and training algorithms will be investigated and compared in terms of model prediction efficiency and accuracy.

Although conceptual and physically-based models are the main tool for depicting hydrological variables and understanding the physical processes taking place in a system, they do have practical limitations. When data is not sufficient and getting accurate predictions is more important than conceiving the actual physics, empirical models remain a good alternative method, and can provide useful results without a costly calibration time. ANN models are such 'black box' models with particular properties which are greatly suited to dynamic nonlinear system modeling. The advantages of ANN models over conventional simulation methods have been discussed in detail by French et al. (2010). ANN applications in hydrology vary, from real-time to event based modeling. They have been used for rainfall—runoff modeling, precipitation forecasting and water quality modeling (Govindaraju and Ramachandra Rao, 2009). One of the most important features of ANN models is their ability to adapt to recurrent changes and detect patterns in a complex natural system. More concepts and applications of ANN models in hydrology have been discussed by Govindaraju and Ramachandra Rao (2009) and by the ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2009). Neural networks have also been previously applied with success in groundwater level prediction (Coulibaly et al., 2011a,b,c). In this paper, several different neural networks are evaluated in order to reach conclusions regarding the efficiency of this forecasting technique in groundwater level prediction.

Neural networks are massive parallel processors comprised of single artificial neurons. Fig. 1 shows a typical single neuron with a sigmoid activation function, three input synapses and one output synapse. Synapses represent the structure where weight values are stored.

In this paper, three different neural networks are being used in order to identify the one which gives the best results in predicting mean monthly groundwater level values. They are described below.

Feedforward neural networks have been applied successfully in many different problems since the advent of the error backpropagation learning algorithm. This network architecture and the corresponding learning algorithm can be viewed as a generalization of the popular least-mean-square (LMS) algorithm (Haykin, 2008). A multilayer perceptron network consists of an input layer, one or more hidden layers of computation nodes, and an output layer. Fig. 2 shows a typical feedforward network with ...