برآورد رسوبات معلق نواحی ساحلی دریای خزر با استفاده از تکنیک یادگیری ماشین

Cimen,  M. (2008).Estimation of daily suspended sediments using support vector machines. Hydrological Sciences Journal,  53 (3),  656-666. https://doi.org/10.1623/hysj.53.3.656
Dehghani,  R.,  Babaali,  H.(2023). Evaluation of Statistical Models and Modern Hybrid Artificial Intelligence in Simulation of Runoff Precipitation Process. Sustain. Water Resour. Manag,  8,  154-172. https://doi.org/10.1007/s40899-022-00743-9.
Dehghani,  R.,  Torabi,  H.(2021). Dissolved oxygen concentration predictions for running waters using hybrid machine learning techniques. Modeling Earth Systems and Environment,  6(2),  64-78. https://doi.org/10.1007/s40808-021-01253-x.
Doroudi,  S.,  Sharafati,  A.,  Mohajeri,  H. (2021).Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method.Complexity,  9(3),  532-545. https://doi.org/10.1155/2021/5540284
Eberhart R.,  Kennedy J. (1995). A New Optimizer Using Particle Swarm Theory Proc. Sixth International Symposium on Micro Machine and Human Science,  Nagoya,  Japan,  Piscataway,  NJ: IEEE Service Center, 39-43. https://doi.org/10.1109/MHS.1995.494215
Essam,  Y.,  Huang,  Y.,  Birima,  A.,  Ahmed,  A.,  El-Shafie,  A.(2022).Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms. scientific reports ,  12(2),  344-357. https://doi.org/10.1038/s41598-021-04419-w.
Goyal,  M. K. (2014). Modeling of sediment yield prediction using the M5 model tree algorithm and wavelet regression.Water Resources Management,  28(7),  1991-2003. https://doi.org/10.1007/s11269-014-0590-6
Hassanpour,  F.,  Sharifazari,  S.,  Ahmadaali,  K.,  Mohammadi,  S.,  Sheikhalipour,  Z.(2019). Development of the FCM-SVR Hybrid Model for Estimating the Suspended Sediment Load.KSCE Journal of Civil Engineering, 23(6),  2514-2523. https://doi.org/10.1007/s12205-019-1693-7.
Hou,  W.,  Yin,  G.,  Gu,  J.,  Ma,  N. (2023). Estimation of Spring Maize Evapotranspiration in Semi-Arid Regions of Northeast China Using Machine Learning: An Improved SVR Model Based on PSO and RF Algorithms. Water,  15(8),  558-568. https://doi.org/10.3390/w15081503
Kisi,  O.,  Dailr,  A. H.,  Cimen,  M.,  Shiri,  J. (2012). Suspended sediment modeling using genetic programming and soft computing techniques,  Journal of Hydrology,  450(3),  48–58. https://doi.org/10.1016/j.jhydrol.2012.05.031
Malik,  A.,  Tikhamarine,  Y.,  Al-Ansari,  N.,  Shahid,  S.,  Sekhon,  H.S.,  Pal,  R.,  Rai,  R.,  Pandey,  K.,  Singh,  P.,  Elbeltagi,  A.,  Sammen,  S. (2021). Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test. Engineering Applications of Computational Fluid Mechanics,  15(1),  1075-1094. https://doi.org/10.1080/19942060.2021.1942990
Mirjalili,  S.,  and Lewis,  A. (2016). The whale optimization algorithm. Advances in Engineering Software,  .95(6),  51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008.
Nayak,  P.,  Venkatesh,  B.,  Krishna,  B.,  and Jain,  S. K. (2013). Rainfall- runoff modeling using a conceptual,  data-driven,  and wavelet-based computing approach. Journal of Hydrology,  493(6),  57-67. https://doi.org/10.1016/j.jhydrol.2013.04.016
Nourani,  V., Gokcekus,  H., Gelete,  G. (2021).Estimation of Suspended Sediment Load Using Artificial Intelligence-Based Ensemble Model.Complexity,  8(4),  122-136. https://doi.org/10.1155/2021/6633760
Rajaee,  T.,  Nourani,  V.,  Zounemat-Kermani,  M.,  Kisi,  O. (2011). River suspended sediment load prediction: application of ANN and wavelet conjunction model. Journal of Hydrologic Engineering,  16(2),  613–627. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000347
Reddy,  K.,  Saha,  A.K. (2022). A modified Whale Optimization Algorithm for exploitation capability and stability enhancement. Heliyon,  8(10),  425-441. https://doi.org/10.1016/j.heliyon.2022.e11027
Shin,  S.,  Kyung,  D.,  Lee,  S.,  Taik, & Kim,  J.,  and Hyun,  J. (2005). An application of support vector machines in a bankruptcy prediction model,  Expert Systems with Applications, 28(4), 127-135. https://doi.org/10.1016/j.eswa.2004.08.009
Shrivatava,  M.,  Prasad,  V.,  Khare,  R.(2015). Multi-objective optimization of water distribution system using particle swarm optimization. IOSR J. Mech. Civ. Eng,  12(1),  21–28. https://doi.org/10.5004/dwt.2021.26944
Vapnik,  V.N. (1995). The Nature of Statistical Learning Theory. Springer,  New York. https://doi.org/10.1007/978-1-4757-3264-1
Vapnik,  V.N. (1998). Statistical learning theory. Wiley,  New York. https://doi.org/10.1007/978-3-540-28650-9_8
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. https://doi.org/10.1002/aic.690460812
Wu,  C. L.,  Chau,  K. W. (2020). Rainfall–runoff modeling using an artificial neural network coupled with singular spectrum analysis. Journal of Hydrology,  399(3),  394-409. https://doi.org/10.1016/j.jhydrol.2011.01.017.