
Evaluating the effect of defining management scenarios in water table prediction accuracy using Wavelet-Support Vector Regression (WSVR) hybrid model | ||
تحقیقات آب و خاک ایران | ||
Volume 54, Issue 9, December 2023, Pages 1415-1429 PDF (1.68 M) | ||
Document Type: Research Paper | ||
DOI: 10.22059/ijswr.2023.359864.669504 | ||
Authors | ||
Yeganeh Hajiloo; Safar Marofi* ; Abdollah Taheri Tizro | ||
Department of Water Science and Engineering, Faculty of Agriculture, Bu Ali Sina University, Hamedan, Iran | ||
Abstract | ||
Due to surface water limitations, groundwater reservoirs are known as the main source of supplying water requirements in most arid countries. Recently, owing to these aquifers’ over-exploitation, in order to apply optimal management of these resources, reliable water table forecasting exceeded in importance. This study is aimed to Determine the best scenario for combining input data in Hamedan-Bahar plain water table forecasting using the wavelet-support vector regression (WSVR) hybrid model. In the first step, Rainfall, temperature, evaporation, and the groundwater level data of seventeen piezometers in 26 years (1991-2017) were collected and completed. Nine scenarios with different lags and combinations were considered to select the model inputs and their lag numbers. Modeling performance in each scenario was evaluated using statistical parameters such as the Pearson correlation coefficient (r), standard error (SE), and root mean square error (RMSE), and using the best scenario, the water table of the area was predicted for ten years ahead. Based on the obtained results, the scenario in which each of the four input parameters was used, with one and two lags, had the highest accuracy. Additionally, the predicted results, using the best scenario, illustrated a noticeable downward trend in the water table of the region in the future. Therefore, concerning the high sensibility of this plain because of supplying the water demands in drinking water, agriculture, and industry of Hamedan and Bahar, as well as the necessity of more water harvesting in the future, deciding more favorable groundwater management is highly imperative in this area. | ||
Keywords | ||
Support vector machines; Water table prediction; Wavelet transport; WSVR hybrid model | ||
References | ||
Ansari, M., & Jabbari, I. (2023). Modeling and forecasting of the underground water level of Izadkhasht plain, Fars province. Journal of Geography and Environmental Hazards, (), -. Behzad, M., Asghari, K., & Coppola Jr, E. A. (2010). Comparative study of SVMs and ANNs in aquifer water level prediction. Journal of Computing in Civil Engineering, 24(5), 408-413. Bierkens, M. F. (1998). Modeling water table fluctuations by means of a stochastic differential equation. Water Resources Research, 34(10), 2485-2499. Cohen, A., & Kovacevic, J. (1996). Wavelets: The mathematical background. Proceedings of the IEEE, 84(4), 514-522. Ebrahimi, H., & Rajaee, T. (2017). Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global and Planetary Change, 148, 181-191. Eskandari, A., Faramarzyan yasuj, F., Zarei, H., & Solgi, A. (2019). Evaluation of combined ANFIS with wavelet transform to modeling and forecasting groundwater level, Journal of Watershed Management Research, 9(18), 56-69. (In Persian) Eskandari, A., Solgi, A., & Zarei, H. (2018). Simulating fluctuations of groundwater level using a combination of support vector machine and wavelet transform. Journal of Irrigation Sciences and Engineering (JISE), 41(1), 165-180. (In Persian) Ghobadian, R., Bahrami, Z., & Dabagh Bagheri, S. (2016). Applying the management scenarios in prediction of groundwater level fluctuations by using the conceptual and mathematical MODFLOW model (Case study: Khezel-Nahavand Plain). Iranian journal of Ecohydrology, 3(3), 303-319. (In Persian) Gong, Y., Zhang, Y., Lan, S., & Wang, H. (2016). A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee, Florida. Water resources management, 30, 375-391. Izadi, A.A., Davari, K., Alizadeh, A., Ghahraman, B., & Haghayeghi Moghadam, S.A. (2007). Water table forecasting using artificial neural networks. Iranian Journal of Irrigation and Drainage, 1(2), 59-71. (In Persian) Khayat, A. (2017). Prediction of grounwater level using the Nuero- Fuzzy method and wavelet analysis under the effects of climate change. Master of Science’s thesis, Faculty of Agriculture, University of Birjand, Iran. (In Persian)
Khedri, A., Kalantari, N., & Vadiati, M. (2020). Comparison study of artificial intelligence method for short term groundwater level prediction in the northeast Gachsaran unconfined aquifer. Water Supply, 20(3), 909-921. Moosavi, V., Vafakhah, M., Shirmohammadi, B., & Behnia, N. (2013). A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water resources management, 27, 1301-1321 Nayak, P. C., Rao, Y. S., & Sudheer, K. P. (2006). Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water resources management, 20, 77-90.
Nourani, V., Hasan Zadeh, Y., Komasi, M., & Sharghi, E. (2008). Precipitation-runoff modeling with wavelet-artificial neural network hybrid model. Fourth National Congress of Civil Engineering. University of Tehran. (In Persian) Rahnama, N., Zarei, H., & Ahmadi, F. (2023). Application of K-star Algorithm for Groundwater Level Forecasting (Case study: Aspas plain). Iranian Journal of Irrigation & Drainage, 17(2), 305-320. Rostaminezhad Dolatabad, H., Shahabi, S., & Madadi, M. R. (2023). Evaluating Decision Tree Efficiency in Combination with Wavelet Transform to Predict Groundwater Level Fluctuation. Iranian Journal of Irrigation & Drainage, 17(3), 413-427. Sadat‑Noori, M., Glamore1, W., Khojasteh, D. (2020). Groundwater level prediction using genetic programming: the importance of precipitation data and weather station location on model accuracy. Environmental Earth Sciences, 79:37. Saeedi Razavi, B., Arab, A. (2019). Groundwater Level Prediction of Ajabshir Plain using Fuzzy Logic, Neural Network Models and Time Series. Journal of Hydrogeology, 3(2), 69-81. (In Persian) Shrivastava, N. A., & Panigrahi, B. K. (2014). A hybrid wavelet-ELM based short term price forecasting for electricity markets. International Journal of Electrical Power & Energy Systems, 55, 41-50. Suryanarayana, C., Sudheer, C., Mahammood, V., & Panigrahi, B. K. (2014). An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing, 145, 324-335. Tapak, L., Rahmani, A. R., & Moghimbeigi, A. (2013). Prediction the groundwater level of Hamadan-Bahar plain, west of Iran using support vector machines. Journal of research in health sciences, 14(1), 82-87. Vapnik, V. (1998). Statistical learning theory. (No Title). Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O., & Lee, K. K. (2011). A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of hydrology, 396(1-2), 128-138. Yu, H., Wen, X., Feng, Q., Deo, R. C., Si, J., & Wu, M. (2018). Comparative study of hybrid-wavelet artificial intelligence models for monthly groundwater depth forecasting in extreme arid regions, Northwest China. Water resources management, 32, 301-323. Zhou, T., Wang, F., & Yang, Z. (2017). Comparative analysis of ANN and SVM models combined with wavelet preprocess for groundwater depth prediction. Water, 9(10), 781. | ||
Statistics Article View: 298 PDF Download: 216 |