آمار
| تعداد نشریات | 23 |
| تعداد شماره‌ها | 383 |
| تعداد مقالات | 3,036 |
| تعداد مشاهده مقاله | 2,760,824 |
| تعداد دریافت فایل اصل مقاله | 1,950,061 |
Optimization of ANFIS model using wavelet transform for simulating groundwater level variations | ||
| Journal of Applied Research in Water and Wastewater | ||
| مقاله 3، دوره 7، شماره 1 - شماره پیاپی 13، شهریور 2020، صفحه 23-29 اصل مقاله (1.19 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22126/arww.2020.4150.1123 | ||
| نویسندگان | ||
| Fariborz Yosefvand* ؛ Saeid Shabanlou | ||
| Department of Water Engineering, Faculty of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran. | ||
| چکیده | ||
| In this study, for the first time, groundwater level (GWL) variations of the Sarab-e Qanbar well located in the city of Kermanshah, are simulated over a 13-year period by a hybrid model named WANFIS (wavelet-adaptive neuro fuzzy inference system). In order to develop the hybrid model, the wavelet transform and the adaptive neuro fuzzy inference system (ANFIS) model are utilized. Furthermore, the 9 and 4 year data are used for training and testing the artificial intelligence models, respectively. Moreover, the effective lags are detected by the autocorrelation function (ACF) and then eight different models are developed for each of the ANFIS and WANFIS models using them. After that, all mother wavelets are evaluated and Dmey mother wavelet is chosen as the most optimal. For this mother wavelet, the values of scatter index (SI), variance account for (VAF) and Root mean square error (RMSE) are obtained 0.192, 94.951 and 3.117, respectively. Next, the superior model is detected through the analysis of the results obtained by all ANFIS and WANFIS models. The superior model estimates the objective function values with reasonable accuracy. For example, the correlation coefficient (R), Scatter Index (SI) and variance account for (VAF) for this model are obtained 0.974, 0.192 and 94.951, respectively. The modeling results indicate that the wavelet transform noticeably enhances the ANFIS model accuracy. Finally, the lags of the time series data for the Sarab-e Qanbar well including (t-1), (t-2), (t-3) and (t-4) are introduced as the most effective lags. | ||
| کلیدواژه‌ها | ||
| Groundwater level variations؛ Hybrid artificial intelligence technique؛ Wavelet Transform؛ ANFIS؛ Optimization؛ Simulation | ||
| مراجع | ||
|
Adamowski J., and Sun K., Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds, Journal of Hydrology 390 (2010) 85–91. Akhbari A., Zaji A.H., Azimi H., Vafaeifard M., Predicting the discharge coefficient of triangular plan form weirs using radian basis function and M5’methods, Journal of Applied Research in Water and Wastewater 4 (2017) 281-289. Azimi H., Heydari M., Shabanlou S., Numerical simulation of the effects of downstream obstacles on malpasset dam break pattern, Journal of Applied Research in Water and Wastewater 5 (2018) 441-446. Barzegar R., Fijani E., Moghaddam A.A., Tziritis E., Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models, Science of The Total Environment 599 (2017) 20-31. Chitsazan M., Rahmani G., Neyamadpour A., Groundwater level simulation using artificial neural network: a case study from Aghili plain, urban area of Gotvand, south-west Iran, Geopersia 3 (2013) 35-46. Dash N.B., Panda S.N., Remesan R., Sahoo N., Hybrid neural modeling for groundwater level prediction, Neural Computing and Applications 19 (2010) 1251-1263. Ebrahimi H., and Rajaee, T., Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine, Global and Planetary Change 148 (2017) 181-191. Grossmann A., Morlet J., Decomposition of hardy functions into square integrable wavelets of constant shape, SIAM Journal on Mathematical Analysis 15 (1984) 723–736 Jang J.S., ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics: System 23 (1993) 665-685. Jang J.S.R., Sun C.T., Mizutani E., Neuro-Fuzzy and soft computing: a computational approach to learning and machine intelligence, IEEE Transactions on Automatic Control 42 (1997) 1482-1484. Kisi O., and Shiri J., Wavelet and neuro-fuzzy conjunction model for predicting water table depth fluctuations, Hydrology Research 43 (2012) 286-300. Khaki M., Yusoff I., Islami, N., Simulation of groundwater level through artificial intelligence system, Environmental Earth Sciences 73 (2015) 8357-8367. Liu D., Li G., Fu Q., Li M., Liu C., Faiz M.A., Cui S., Application of particle swarm optimization and extreme learning machine forecasting models for regional groundwater depth using nonlinear prediction models as preprocessor, Journal of Hydrologic Engineering 23 (2018) 04018052. Singh R.M., Wavelet-ANN model for flood events, Proceedings of the International Conference on Soft Computing for Problem Solving, India 20-22 (2012) 165–175. | ||
|
آمار تعداد مشاهده مقاله: 435 تعداد دریافت فایل اصل مقاله: 362 |
||