Simulating Fluctuations of Groundwater Level Using a Combination of Support Vector Machine and Wavelet Transform

Document Type : Research Paper


1 Instructor, Department of Civil Engineering, Islamic Azad University of Bushehr, Iran.

2 Ph.D. Student, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Iran

3 Associate Professor, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Iran.


Groundwater resources are known as one of the most important water resources. Proper knowledge and exploitation of groundwater, particularly in arid and semi-arid regions, plays an important role in sustainable development in different fields such as agricultural, social, and economic activities. Many models have been used in order to predict groundwater level including empirical time series models and physical models (Bierkens, 1998). It is true that these models have been widely used, but when the dynamic behavior of a hydrological system is changed during the time, the mentioned models may not be able to predict water resource parameters, therefore making them unsuitable (Nayak et al. 2006). On the other hand, physical models need a great deal data to simulate groundwater fluctuations and, since the relations among effective parameters may be nonlinear, the mentioned models are not practical enough to show the existing relations.


Main Subjects

1-    Adamowski, J. and Chan, H.F., 2011. A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1–4), pp. 28-40.
2-    Bierkens, M., 1998. Modeling water table fluctuations by means of a stochastic differential equation. Water Resources Reserch, 34(10), pp. 2485-2499.
3-    Cortes, C. and Vapnik, V., 1995. Support-Vector Networks. Machine Learning, 20, pp. 273-295.
4-    Fallah-Mehdipour, E., Bozorg Haddad, O. and Mariño, M.A., 2013. Prediction and simulation of monthly groundwater levels by genetic programming. Journal of Hydro-environment Research, 7(4), pp. 253-260.
5-    Izadi, A.A., Davari, K., Alizadeh, A., Ghahreman, B. and Haghayeghi moghadam, S.A., 2007.  Using Artificial Neural Network to Predict the Water Ttable (case study: Neyshabur plain). Iranian journal of Irrigation and Drainage, 1(2), pp. 59-71. (In Persian).
6-    Kumar, P.E. and Fofola-Georgiou, G., 1994. Wavelet in geophysiscs. New York, Academic Press.
7-    Mallat, S.G., 1998. A wavelet tour of signal processing, San Diego, pp. 1-557.
8-    Moosavi, V., Vafakhah, M., Shirmohammadi, B. and Ranjbar, M., 2014. Optimization of Wavelet-ANFIS and Wavelet-ANN Hybrid Models by Taguchi Method for Groundwater Level Forecasting. Arabian Journal for Science and Engineering, 39, pp. 1785–1796.
9-    Nakhaei, M. Saberi nasr, R. and Faraj zadeh, R., 2011. Benefit of Wavelet-Artificial Neural Network in prediction of water table fluctuations. 4th Iran Water Resources Management Conference. Amirkabir University of Technology, Tehran. (In Persian).
10- Nayak, P., Satyaji Rao, Y.R. and Sudheer, K.P., 2006. Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach. Water Resources Management, 20, pp.77-90.
11- Nourani, V., Asghari Mogaddam, A. and Naderi, A.O., 2008. An ANN-based model for spatiotemporal groundwater level forecasting. Hydrological Processes, 22, pp. 5054–5066.
12- Nourani, V., Komasi, M. and Mano, A., 2009. A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling. Water Resources Management, 23, pp. 2877–2894.
13- Rajaee, T. and Zeynivand, A., 2015. Modeling of Groundwater Level using ANN–Wavelet Hybrid Model (Case Study: Sharif Abad Plain). Journal of Civil and Environmental Engineering, 44(4), pp.51-63. (In Persian).
14- Riad, S., Mania, J., Bouchaou, L. and Najjar, Y., 2004. Rainfall-runoff model usingan artificial neural network approach. Mathematical and Computer Modelling, 40(7–8), pp.839-846.
15- Schölkopf, B. and Smola, A.J., 2002. Learning with Kernels. MIT Press, Cambridge, MA.
16- Schölkopf, B., Smola, A. and Müller, K.R., 2005. Kernel principal component analysis. Lecture Notes in Computer Science, 1327, pp. 583-588.
17- Shiri, J., Kisi, O., Yoon, H., Lee, K.K. and Hossein Nazemi, A., 2013. Predicting groundwater level fluctuations with meteorological effect implications-A comparative study among soft computing techniques. Computers & Geosciences, 56, pp. 32-44.
18- Smola, A.J. and Schölkopf, B., 2004. Atutorial on support vector regression. Statistics and Computing, 14(199), pp.199-222.
19- Solgi, A., 2014. Stream flow forecasting using combined Neural Network Wavelet model and comparsion with Adaptive Neuro Fuzzy Inference System and Artificial Neural Network methods (Case Study Gamasyab River,Nahavand). MSc. Shahid Chamran University of Ahvaz, Iran, pp.1-164. (In Persian).
20- Sujay Raghavendra, N. and Paresh Chandra, D., 2014. Support vector machine applications in the field of hydrology: A review. Applied Soft Computing, 19, pp.372-386.
21- Suryanarayana, C., Sudheer, C., Mahammood, V. and Panigrahi, B.K., 2014. An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing, 145, pp.324-335.
22- Wang, W. and Ding, J., 2003. Wavelet Network Model and Its Application to the Prediction of Hydrology. Natureand Science, 1(1), pp. 67-71.
Volume 41, Issue 1
May 2018
Pages 165-180
  • Receive Date: 02 January 2016
  • Revise Date: 10 November 2016
  • Accept Date: 10 December 2016
  • Publish Date: 21 April 2018