منابع
اسداللهی، معصومه (1401). مدلهای رگرسیونی استوار براساس بردار پشتیبان در محیط فازی (پایاننامه دکتری). دانشگاه بیرجند، بیرجند، ایران.
تقوایی، فهیمه، حسینی، خسرو و هاشمی، علیاصغر. (1403). پیشبینی تأثیر تغییر اقلیم بر تراز آب زیرزمینی با استفاده از الگوریتمهای فرا تکاملی (مطالعه موردی: دشت ریز-بوشهر). پژوهش آب ایران، 53، 48-39. doi: 10.22034/IWRJ.2023.14484.2548
زراعتی نیشابوری، سپیده، پوررضا بیلندی، محسن، خاشعی سیوکی، عباس و شهیدی، علی. (1399). مقایسه مدل رگرسیون فازی امکانی و رگرسیون کمترین مربعات فازی در پیشبینی تراز سطح ایستابی آبخوان دشت نیشابور. علوم و مهندسی آبیاری، 43(1)، 143-131. doi: 10.22055/jise.2018.23275.1652
زراعتی نیشابوری، سپیده، پوررضا بیلندی، محسن، خاشعی سیوکی، عباس و شهیدی، علی. (1401). پیش بینی تراز آب زیرزمینی دشت نیشابور با معرفی مدل رگرسیون فازی امکانی. آبخوان و قنات، 3(1)، 64-53. doi: 10.22077/jaaq.2018.1727.1008
صدقآمیز، عباس و فروغی، فرید. (1402). پیش بینی نوسانات سطح آب زیرزمینی با استفاده از مدلهای استنتاج فازی، استنتاج فازی عصبی و شبکه عصبی. سامانههای سطوح آبگیر باران، 11(4)، 50-31. doi: 20.1001.1.24235970.1402.11.4.3.5
عباس نوین پور، اسفندیار، کریمی، فاطمه و رضایی، حسین. (1401). پیش بینی سطح آب زیرزمینی دشت قروه با استفاده از مدل MODFLOW در سناریوهای مختلف تغییر اقلیم LARS-WG. دانش آب و خاک، 32(4)، 73-61. doi: 10.22034/ws.2021.30465.2197
Refrences
Abbassnouvinpour, E., Karimi, F., and Rezaie, H. (2022). The Prediction of Groundwater Level in Ghorve Plain Using the MODFLOW Model in Different Scenarios of LARS-WG Climate Change. Water and Soil Science, 32(4), 61-73. doi: 10.22034/ws.2021.30465.2197. [In Persian]
Aghlmand, R., & Abbasi, A. (2019). Application of MODFLOW with boundary conditions analyses based on limited available observations: A case study of Birjand plain in East Iran. Water, 11(9), 1904.
Ahmadi, A., Olyaei, M., Heydari, Z., Emami, M., Zeynolabedin, A., Ghomlaghi, A., Daccache, Graham E. F., & Sadegh, M. (2022). Groundwater level modeling with machine learning: a systematic review and meta-analysis. Water, 14(6), 949. doi: 10.3390/w14060949
Asadollahi, M. (2023). Robust regression models based on support vectors in a fuzzy environment (PhD dissertation). University of Birjand, Birjand, Iran.[In Persian]
Badetiya, Y., & Barale, M. (2024). Modeling groundwater level using geographically weighted regression. Arabian Journal of Geosciences, 17(9), 251.
Band, S. S., Heggy, E., Bateni, S. M., Karami, H., Rabiee, M., Samadianfard, S., Samadianfard, Chau, K-W., & Mosavi, A. (2021). Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Engineering Applications of Computational Fluid Mechanics, 15(1), 11471158. doi: 10.1080/19942060.2021.1944913
Bardossy, A., & Duckstein, L. (2022). Fuzzy rule-based modeling with applications to geophysical, biological, and engineering systems. CRC Press. doi: 10.1201/9780138755133
Bardossy, A., Bogardi, I., & Duckstein, L. (1990). Fuzzy regression in hydrology. Water Resources Research, 26(7), 1497-1508. doi: 10.1029/WR026i007p01497
Chen, B. S., Lee, M. Y., Lin, T. H., & Zhang, W. (2021). Robust state/fault estimation and fault-tolerant control in discrete-time T–S fuzzy systems: An embedded smoothing signal model approach. IEEE Transactions on Cybernetics, 52(7), 6886-6900. doi: 10.1109/TCYB.2020.3042984
Chukhrova, N., & Johannssen, A. (2019). Fuzzy regression analysis: systematic review and bibliography. Applied Soft Computing, 84, 105708.
Chutia, R., Saikia, S., & Gogoi, M. K. (2022). A theoretical approach to ranking of parametric fuzzy numbers using value and left–right ambiguity. Mathematical Sciences, 16(3), 299-315. doi: 10.1007/s40096-021-00422-4
Dehghani, R., & Torabi Poudeh, H. (2022). Application of novel hybrid artificial intelligence algorithms to groundwater simulation. International Journal of Environmental Science and Technology, 19(5), 43514368. doi: 10.1007/s13762-021-03596-5
Ehteram, M., & Banadkooki, F. B. (2023). A developed multiple linear regression (MLR) model for monthly groundwater level prediction. Water, 15(22), 3940. doi: 10.3390/w15223940
Elbeltagi, A., Salam, R., Pal, S. C., Zerouali, B., Shahid, S., Mallick, J., Islam, M.S., & Islam, A. R. M. T. (2022). Groundwater level estimation in northern region of Bangladesh using hybrid locally weighted linear regression and Gaussian process regression modeling. Theoretical and Applied Climatology, 149(1), 131151. doi: 10.1007 / s00704-022-04037-0
Feng, F., Ghorbani, H., & Radwan, A. E. (2024). Predicting groundwater level using traditional and deep machine learning algorithms. Frontiers in Environmental Science, 12, 1291327. doi: 10.3389/fenvs.2024.1291327
Guo, F. F., & Shen, J. (2019). A smoothing approach for minimizing a linear function subject to fuzzy relation inequalities with addition–min composition. International Journal of Fuzzy Systems, 21, 281-290. doi: 10.1007/s40815-018-0530-3
Hesamian, G., & Akbari, M. G. (2020). A fuzzy additive regression model with exact predictors and fuzzy responses. Applied Soft Computing, 95, 106507. doi: 10.1016/j.asoc.2020.106507
Jithendra, T., & Basha, S. S. (2023). Analyzing groundwater level with hybrid ANN and ANFIS using metaheuristic optimization. Earth science informatics, 16(4), 3323-3353.
Kung, C. F., & Hao, P. Y. (2023). Fuzzy Least Squares Support Vector Machine with Fuzzy Hyperplane. Neural Processing Letters, 55(6), 7415-7446. doi: 10.1007/s11063-023-11267-4
Li, W., Wei, Z., Chen, Y., Tang, C., & Song, Y. (2020). Fuzzy granular hyperplane classifiers. IEEE Access, 8, 112066-112077. doi: 10.1109/ACCESS.2020.3002904
Mohapatra, J. B., Jha, P., Jha, M. K., & Biswal, S. (2021). Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India. Science of the Total Environment, 785, 147319. doi: 10.1016/j.scitotenv.2021.147319
Mohtashami, A., Monfared, S. A. H., Azizyan, G., & Akbarpour, A. (2022). Numerical simulation of groundwater in an unconfined aquifer with a novel hybrid model (case study: Birjand Aquifer, Iran). Journal of Hydroinformatics, 24(1), 160178. doi: 10.2166/hydro.2021.113
Norouzi Khatiri, K., Nematollahi, B., Hafeziyeh, S., Niksokhan, M. H., Nikoo, M. R., & AlRawas, G. (2023). Groundwater management and allocation models: a review. Water, 15(2), 253. doi: 10.3390/w15020253
Rezaei, A., Sayadi, M. H., Zadeh, R. J., & Mousazadeh, H. (2021). Assessing the hydrogeochemical processes through classical integration of groundwater parameters in the Birjand plain in eastern Iran. Groundwater for Sustainable Development, 15, 100684. doi: 10.1016/j.gsd.2021.100684
Sahoo, S., Russo, T. A., Elliott, J., & Foster, I. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US. Water Resources Research, 53(5), 3878-3895. doi: 10.1002/2016WR019933
Sedghamiz, A., & Foroughi, F. (2023). Prediction of groundwater level fluctuations using fuzzy inference system, Adaptive Neuro–Fuzzy Inference System and neural network. Iranian Journal of Rainwater Catchment Systems, 11(4), 31-50. doi: 20.1001.1.24235970.1402.11.4.3.5 [In Persian].
Taghvaei, F., Hosseini, K., & Hashemi, A. A. (2024). Predicting the impact of climate change on groundwater level using evolutionary algorithms: A case study of Riz Plain-Bushehr. Iranian Journal of Water Research, 53, 39–48. doi: 10.22034/IWRJ.2023.14484.2548. [In Persian]
Taheri, S. M., & Kelkinnama, M. (2012). Fuzzy linear regression based on least absolute deviations. Iranian Journal of Fuzzy Systems, 9(1), 121-140.
Tanaka, H. (1982). A linear regression model with fuzzy function. Journal of the Operations Research Society of Japan, 25, 162-173.
Tao, H., Hameed, M. M., Marhoon, H. A., Zounemat-Kermani, M., Heddam, S., Kim, S., Sulaiman, S.O., Tan, M.L., Sa’adi, Z., Mehr, A.D. and Allawi, M.F (2022). Groundwater level prediction using machine learning models: A comprehensive review. Neurocomputing, 489, 271-308.doi: 10.1016/j.neucom.2022.03.014.
Zeraati Neyshabouri, S., Pourreza Bilondi, M., Kashei-Siuki, A. and Shahidi, A. (2022). Estimating The Groundwater Table Of Neyshabour Plain With Introducing Fuzzy Possibilistic Regression Model. Journal of Aquifer and Qanat, 3(1), 53-64. doi: 10.22077/jaaq.2018.1727.1008. [In Persian]
Zeraati Neyshabouri, S., Pourreza Bilondi, M., Khashei Siuki, A., and Shahidi, A. (2020). Comparison of Fuzzy Possibilistic Regression and Fuzzy Least Squares Regression Models to Estimate Groundwater Level of Neyshabour Aquifer. Irrigation Sciences and Engineering, 43(1), 131-143. doi: 10.22055/jise.2018.23275.1652 [In Persian]
Zowam, F. J., & Milewski, A. M. (2024). Groundwater level prediction using machine learning and Geostatistical interpolation models. Water, 16(19), 2771. doi: 10.3390/w16192771
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