آمار
| تعداد نشریات | 13 |
| تعداد شمارهها | 626 |
| تعداد مقالات | 6,517 |
| تعداد مشاهده مقاله | 8,746,957 |
| تعداد دریافت فایل اصل مقاله | 8,317,539 |
Analysis and evaluation of the drop in the groundwater level of Khorram Abad plain | ||
| Environmental Resources Research | ||
| دوره 12، شماره 2، آذر 2024، صفحه 257-268 اصل مقاله (1.51 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22069/ijerr.2024.22771.1450 | ||
| نویسندگان | ||
| Hamidreza Babaali* 1؛ Ebrahim Nohani2؛ Reza Dehghani3 | ||
| 1Assistant Professor, Department of Civil Engineering, Islamic Azad University, Khorramabad branch, Lorestan, Iran | ||
| 2Assistant Professor, Department of Civil Engineering, Materials and Energy Research Center, Dezful Branch, Islamic Azad University, Dezful, Iran | ||
| 3PhD in Water Sciences and Engineering, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramabad, Iran | ||
| چکیده | ||
| In recent years, the indiscriminate extraction of underground water has caused a sharp drop in the level of underground water; which has resulted in dangers such as land subsidence. Therefore, it is important to reliably predict the level of underground water for the management of these resources. In this research, in order to simulate the underground water level of Khorramabad plain, the performance of hybrid models of support vector regression-wavelet, support vector regression-bat, support vector regression-gray wolf for four piezometric wells using parameters of temperature, precipitation and withdrawal from aquifers during the period The statistics of 1382-1402 were examined. The criteria of correlation coefficient, root mean square error, average absolute value of error and Nash Sutcliffe coefficient were used to evaluate and compare the performance of the models. The results showed that the combined structure provides better performance than other structures in all investigated models. Also, the results showed that the support vector-wavelet regression model has a favorable ability compared to other models. | ||
| کلیدواژهها | ||
| Decline of Water Resources؛ Khorramabad؛ Simulation؛ Hybrid Model | ||
| مراجع | ||
|
Amuda, A., Brest, J., and Mezura-Montes, E. 2013. Structured Population Size Reduction Differential Evolution with Multiple Mutation Strategies on CEC 2013 real parameter optimization. In Proceedings of the IEEE Congress on Evolutionary Computation. Cancun. Mexico. 5, 1925–1931.
Bahmani, R., Taha, B.M., and Ouarda, J. 2021. Groundwater level modeling with hybrid artificial intelligence techniques. Journal of Hydrology. 595(2), 71-84.
Basak, D., Pal, S., and Patranabis, D.C. 2007. Support vector regression. Neural Inf Process. 11(2), 203-225.
Dehghani, R., and Torabi, H. 2021. Application of novel hybrid artificial intelligence algorithms to groundwater simulation. International Journal of Environmental Science and Technology. 4(3), 44-58.
Dehghani, R., and Torabi, H. 2021. Dissolved oxygen concentration predictions for running waters with using hybrid machine learning techniques. Modeling Earth Systems and Environment. 8(2), 145-155.
Dehghani, R., and Torabi, H. 2022. The effect of climate change on groundwater level and its prediction using modern meta- heuristic model. Groundwater for Sustainable Development. 16(4), 224-238.
Dehghani, R., Torabi Poudeh, H., Younesi, H., and Shahinejad, B. 2020. Daily Streamflow Prediction Using Support Vector Machine-Artificial Flora (SVM-AF) Hybrid Model. Acta Geophysica. 68(6), 51-66.
Dehghani, R., Torabi Poudeh, H., Younesi, H., and Shahinejad, B. 2020. Forecasting Daily River Flow Using an Artificial Flora–Support Vector Machine Hybrid Modeling Approach (Case Study: Karkheh Catchment, Iran). Air, Soil, and Water Research. 14(2), 22-35.
Dehghani, R., and Babaali, H. 2022. Evaluation of Statistical Models and Modern Hybrid Artificial Intelligence in Simulation of Runoff Precipitation Process. Sustain Water Resour. Manag. 8(3), 154-165.
Dehghani, R., and Torabi, H. 2021. Applying hybrid artificial algorithms to the estimation of river flow: a case study of Karkheh catchment area. Arabian Journal of Geosciences. 14, 768-780.
Hamel, L. 2009. Knowledge discovery with support vector Machines, Hoboken. N.J. John Wiley.6, 744-765.
Kisi, O., Karahan, M., and Sen, Z. 2006. River suspended sediment modeling using fuzzy logic approach. Hydrology of Process. 20(2), 4351-4362.
Mirzania, E., and Dehghani, R. 2023. A novel hybrid AIG-SVR model for estimating daily reference evapotranspiration. Engineering with computers. Arabian Journal of Geosciences .16(3), 645-662.
Misra, D., Oommen, T., Agarwa, A., Mishra, S.K., and Thompson, A.M. 2009. Application and analysis of support vector machine based simulation for runoff and sediment yield. Biosyst Eng. 103(3), 527–535.
Moravej, M., Amani, P., and Hosseini-Moghari, M. 2020. Groundwater level simulation and forecasting using interior search algorithm-least square support vector regression (ISA-LSSVR). Groundwater for Sustainable Development. 11(1), 100-447.
Nagy, H., Watanabe, K., and Hirano, M. 2002. Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulics Engineering. 128(3), 558-559.
Ostu, N. 1979. A Threshold Selection Method from Gray-Level Histograms [J]. IEEE Transactions on Systems Man and Cybernetics. 9 (1), 62-66.
Shin, S., Kyung, D., Lee, S., Taik Kim, J., and Hyun, J. 2005. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications. 28(4), 127-135.
Vapnik, V., and Chervonenkis, A. 1991. The necessary and sufficient conditions for consistency in the empirical risk minimization method. Pattern Recognition and Image Analysis. 1(3), 283-305.
Vapnik, V.N. 1995. The Nature of Statistical Learning Theory. Springer. New York. 2, 233-265.
Vapnik, V.N. 1998. Statistical learning theory. Wiley. New York. 3, 422-445.
Wang, D., Safavi, A.A., and Romagnoli, J.A. 2000. Wavelet-based adaptive robust M-estimator for non-linear system identification. AIChE Journal. 46(4), 1607-1615.
Zeidalinejad, N., and Dehghani, R. 2023. Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways. Groundwater for Sustainable Development. 20(4), 224-235. | ||
|
آمار تعداد مشاهده مقاله: 7,137 تعداد دریافت فایل اصل مقاله: 101 |
||
سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب