Research paperModeling response of runoff and evapotranspiration for predicting water table depth in arid region using dynamic recurrent neural network
Graphical abstract
Normalized actual versus predicted water table depth for scenario 1–5 in testing phase.
Introduction
Aquifers are modeled according to their conceptualization and calibration of model parameters (Anderson et al., 2015). The procedure is based on the physics of the system by calibrating the parameters of the model (Ljung, 1999). The behavior of natural systems is usually characterized by different variables, exaggerated by inputs, which interact to produce observable variations of output (Sun, 2013). The study of the dynamics of evaluation of groundwater level is a relevant topic for groundwater management, especially in arid and semiarid regions, where rainfall amount and distribution vary consistently across the years (Rushton, 2003). The difficulty of modeling is the dynamics of hydro geological systems with their inherent complexity, and nonlinear processes controlling their responses to external inputs (Kresic and Mikszewski, 2012). Water levels are predicted through physically based or data-driven models. Developing a physically based model for assessing water level fluctuations require proper characterization and synthesis of the aquifer parameters, to describe spatial variability of the subsurface hydrogeology and soil characteristics (Taormina et al., 2012, Dadaser-Celik and Cengiz, 2013). Ghose et al. (2010) has predicted water table depth using BPNN and RBFN techniques.
Empirical approaches have been widely used for water table depth modeling (Box and Jenkins, 1976, Tankersley and Graham, 1994, Hipel and McLeod, 1994, Van Geer and Zuur, 1997, Knotters and Van Walsum, 1997). The empirical time series models are described for a longer time series of water table depth. Artificial Neural Networks (ANNs) have been successfully used for water level modeling (Coulibaly et al., 1999, Coulibaly et al., 2001). In hydrological modeling neural networks is found to be a suitable tool (Hsu et al., 1995, Tokar and Marcus, 2000, Coulibaly et al., 2000a). When there is insufficient knowledge of the hydro geologic characteristics of the system, and accuracy in prediction is more important for understanding the physical processes, then black-box-type models are viable options (Nourani and Mano, 2007). Artificial neural networks (ANNs) are among the black-box-type models that can be applied to capture nonlinear behavior of complex systems. ANN models have been used in rainfall–runoff processes (Kumar et al., 2005, RezaeianZadeh et al., 2010). From literature review of previous studies we have focused to predict groundwater levels on monthly basis (Nayak et al., 2006; Jalalkamali et al., 2011; Shirmohammadi et al., 2013).
In fact, aquifer dynamics is related to (1) rainfall intensity (2) infiltration and percolation processes, and (3) aquifer structural characteristics. Thus, estimation along with impact of evapotranspiration and runoff is essential for predicting groundwater levels to rainfall potential in the aquifer. In this context, the choice of a model is important by data availability, mechanism of calibration and validation. The above literature studies point out that ANN model is an acceptable tool for performing the water table prediction, especially in areas where the scarcity of rainfall occurs or where the real-time simulation is needed.
In the present study evapotranspiration and surface runoff is calculated by empirical equation as suggested by Khosla's formulae to Indian conditions on monthly climate data. Then the parameters evapotranspiration and surface runoff are considered for developing models including precipitation, temperature as input and water table depth as output. The purpose of this paper is to identify the complex dynamics of water table depth fluctuation due to the impact of evapotranspiration and surface runoff. The rest of the paper is organized as follows; Section 2 provides a brief description of the study area and the historical data. 3 Artificial neural networks, 4 Results and discussion introduce the architecture and learning algorithm of RNN. Section 5 describes the result and discussion. Section 6 describes the concluding remarks of the study.
Section snippets
Study area and data
Rengali block is located in the western region of Odisha having area of 713sq. km. Its geographical coordinates are 21° 38' 0" North, 84° 3' 0" East. Major soil type of Rengali is red sandy and red loamy soils. Major water bearing formations are granite gneiss, quartzites, sandstone and arenaceous shales. The region is a part of Indian peninsula and is characterized by scanty rainfall. The area is categorized by rippling plains with inaccessible high peaks. The climate is rooted with dry
Artificial neural networks
Simple neuron model receives a vectorial input with components . These input components are then multiplied by the appropriate weights and accumulated as . This term is called weighted sum. Then the nonlinear mapping function defines the scalar output y = . After this transition, a neuron model and the weights are to be adjusted. Data processing of a neuron is presented in Fig. 2. Data input includes precipitation, maximum temperature, minimum temperature, humidity, runoff, and
Evaluation of evapotranspiration loss and Runoff
Monthly surface runoff is calculated using Khosla's formulae in Indian condition. Monthly average evapotranspiration losses are evaluated using Khosla's equation, an empirical one because of non-availability of data. The relationship for monthly runoff is, Rm = P m – L m. Where Lm = 0.48 Tm for Tm > 4.5 °C. Pm = Monthly precipitation in mm. Lm= Monthly evapotranspiration losses in mm. Tm = Mean monthly temperature of station in °C. Then using Khosla's formulae on Indian infirmity the following
Conclusion
In this study, three different transfer functions of dynamic recurrent neural networks are used for monthly average water level prediction in arid region with five scenarios. A correlation analysis is used to find the associations among the inputs and the target water table depth. One of the advantages of dynamic RNN is that it converges first to produce the target as compared to other neural network techniques. This is related to its capacity of reproducing the physical phenomenon of the
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