|
1. ASCE Task Committee on Application of artificial neural networks in hydrology, (2000), "Artificial neural networks in hydrology, I: Preliminary concepts", Journal of Hydrology Engineering, 2: 115-123. 2. Azid, A., Juahir, H., Toriman, M. E., Kamarudin, M. K. A., Hasnam, C. N., Abdul Aziz, N. A., Azman, F., Latif, M.T., Zainuddin, S. F. M., Osman, M. R., (2014), "Prediction of the level of air pollution using principal component analysis and Artificial Neural Network Techniques: a case study in Malaysia", Water Air Soil Pollution, 225, 2063: 2077. 3. Ashrafzadeh, A., Malik, A., Jothiprakash, V., Ghorbani, M. A., Biazar, S. M., (2018), "Estimation of daily pan evaporation using neural networks and meta-heuristic approaches", ISH Journal of Hydraulic Engineering, 17: 1-9. 4. Biazar, S., ghorbani, M., Darbandi, S, (2018), "Used from Entropy theory and Gamma test in the determination input variables for daily evaporation estimation", Iranian Journal of Ecohydrology, 5 (2): 535-549. [In Persian]. 5. Camdevyren, H., Demyr, N., Kanik, A., Keskyn, S., (2005), "Use of principal component scores in multiple linear regression models for prediction of Chlorophyll- an in reservoirs", Ecological Modelling, 181: 581-589. 6. Coulibaly, P., Anctil, F., Bobée, B., (2000), "Daily reservoir inflow forecasting using artificial neural networks with stopped training approach", Journal of Hydrology, 230: 244-257. 7. Dawson, C.W., Abrahart, R. J., Shamseldin, A. Y., Wibly, R. L., (2006), "Flood estimation at ungauged sites using artificial neural networks", Journal of Hydrology, 319: 391-409. 8. Dibike, Y., Velickov, S., Solomatine, D., Abbott, M., (2001), "Model induction with of support vector machines. Introduction and applications", Journal of Computing in Civil Engineering, 15: 208- 216. 9. Durrant, P. J., (2001), "Win_gamma TMA non-linear data analysis and modeling tool with applications to flood prediction", Ph.D. Thesis, Department of Computer Science, Cardiff University Wales, UK. 10. Deo, R, C., Ghorbani, M, A., Samadianfard, S., Maraseni, T., Bilgili, M., Biazar, M., (2018), "Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data" Renewable energy, 116: 309-323. 11. Eskandari, A., Nouri, R., Meraji, H., Kiaghadi, A., (2012), "Developing a proper model for online estimation of the 5-day biochemical oxygen demand based on artificial neural network and support vector machine", Journal of Environmental Studies, 38 (1): 71-82. [In Persian]. 12. Ghareman, N., Gharakhani, A., (2013), "Comparison of artificial neural network and multiple regression methods in estimating evaporation from coffees and determining the most important meteorological meteorological elements by main component analysis", Research Watershed research (research and construction), 28 (106): 42-51. [In Persian]. 13. Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J. M., Fernandez, L., (2000), "Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis", Water Research, 34: 807-816. 14. Hooshangi, N., Al Sheikh , A, A., Nadiri, A., (2014), "Optimizing the number of piezometers in predicting groundwater level using PCA and geostatistical methods", Water and Soil Knowledge (Agricultural Knowledge), 25 (4): 53-66. [In Persian]. 15. Isazadeh, M., Biazar, S, M., Ashrafzadeh, A., (2017), "Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters", Environmental Earth Sciences, 76 (17): 610-627. 16. Isazadeh, M., Biazar, S., Ashrafzadeh, A., Khanjani, R., (2018), "Estimation of aquifer qualitative parameters of guilans plain with using gamma test and support vector machine and artificial neural network models", Journal of Environmental Science and Technology, - doi: 10.22034/jest.2018.21197.3034, 78 (1): 50-65. [In Persian]. 17. Kavzoglu, T., Colkesen, I., (2009), "A kernel functions analysis for support vector machines for land cover classification", International Journal of Applied Earth Observation and Geoinformation, 11: 352-359. 18. Kisi, O., Genc, O., Dinc, S., Zounemat-Kermani, M., (2016)," Daily pan evaporation modeling using chi-squared automatic interaction detector, Neural networks, Classification and Regression tree", Computers and Electronics in Agriculture, 122: 112–117. 19. Kisi, O., (2015), "Pan Evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree", Journal of Hydrology, 528: 312-320. 20. Lu, W. Z., Wang, W. J., Wang, X. K., Xu, Z. B., Leung, A.Y.T., (2003), "Using improved neural network to analyze RSP, NOx and NO2 levels in urban air in Mong Kok, Hong Kong", Environmental Monitoring and Assessment, 87: 235-254. 21. McCuen, R. H., (1998), "Hydrologic analysis and design", Prentice Hall, Englewood Cliffs, New Jersey. 22. Mohammadi, B., Biazar, S, M., Asadi, E., (2017), "Performance of hybrid particle swarm algorithm to simulate water level (Case study: Ardabil aquifer)", Journal of Rainwater Catchment Systems, 5 (2): 77-87. ]on line[: http://jircsa.ir/article-1-242-en.html. [In Persian]. 23. Nash, J. E., Sutcliffe, I.V., (1970), "River flow forecasting through conceptual models, Part I, A discussion of principles", Journal of Hydrology, 10: 282-290. 24. Nouri, R., Karachian, R., Khodadai, D, A., Shakibaie Nia, A., (2007), "Evaluation of the importance of river monitoring quality streams using main components analysis and Factor analysis, case study: Karoon River", Water and Wastewater, 18 (3): 60-69. [In Persian]. 25. Noori, R., Karbassi, A., Sabahi, M., (2010), "Evaluation of PCA and gamma test techniques on ANN operation for weekly solid waste prediction", Journal of Environmental Management, 91: 767-771. 26. Singh, V. P., Xu, C.Y., (1997), "Evaluation and generalization of 13 mass transfer equations for determining free water evaporation", Hydrological Process, 11: 311-324. 27. Seifi, A., Mirlatifi, S. M., Reyahi, H., (2013), "Introduction and application of least squat support vector machine in estimating reference evapotranspiration and analysis of uncertainty of results. Case study of Kerman city", Iranian Irrigation and Water Engineering, 13 (4): 67-79. [In Persian] 28. Sharifi, A., Dinipazhu, Y., Fakhriifard, A., Moghaddamnia, A., (2013), "Optimal combination of variables for runoff simulation in Amameh watershed using gamma test", Water and Soil Knowledge, 23 (4): 59-72. [In Persian] 29. Shikho Leslami, N., Mosaedi, A., Davari, K., Mohaghpour, M., (2013), "Propagation of Reference and Evapotranspiration (ETO) using main component analysis (PCA) and multi-linear regression modeling (MLR-PCA) (case study: Mashhad Station)", Water and Soil (Agriculture Sciences and Technology, 28 (2): 420-429. [In Persian] 30. Seyedian, S. M., (2014), "Use of new methods to determine the effective parameters for bridge brushed scour", Iran Irrigation and Water Engineering Society, 5 (3): 1-16. [In Persian] 31. Tian, J., Li, C., Liu, J., Yu, F., Cheng, S., Zhao, N., Jaafar, W., (2016), " Groundwater depth prediction using data-driven models with the assistance of Gamma test", Sustainability, 8: 1076-1085. 32. Tabachnick, B. G., Fidell, L. S., (2001), "Using multivariate statistics", Third Ed, Allyn and Bacon, Boston, London. 33. Tezal, G., Buyukyildiz, M., (2015), "Monthly evaporation forecasting using artificial neural networks and support vector machines", Journal of Theoretical and Applied Climatology, 124: 69-80. 34. Wackernagel, H., (1995), "Multivariate geostatistics: an Introduction with Applications", Second Ed, Springer, New York and London,.
|
(2).jpg)
