بررسی اثر تجزیه سری بارش بر شبیه‌سازی هدایت الکتریکی جریان رودخانه (مطالعه موردی: زیرحوضه اسکندری)

Ahmadi, F., & Maddah, M. A. (2021). Development of Wavelet-Ksar Algorithm Hybrid Model for the Monthly Precipitation Prediction (Case Study: Synoptic Station of Ahvaz). Iranian Journal of Soil and Water Research, 52(2), 409-420. [In Persian].

Andy, L. (2012). Documentation for R package random Fores. (http://cran.r-project.org/web/packages/randomFores)

Bageri, F., Khalili, K., & Nazeri Tahroudi, M. (2023). Evaluation of entropy theory based on random fores in quality monitoring of ground water network. Journal of Water and Irrigation Management, 13(1), 123-139. [In Persian].

Bajirao, T. S., Kumar, P., Kumar, M., Elbeltagi, A., & Kuriqi, A. (2021). Potential of hybrid waveletcoupled data-driven-based algorithms for daily runoff prediction in complex river basins. Theoretical and Applied Climatology, 145(3), 1207-1231.

Breiman, L. (2001). Random Foress. Machine Learning 45(1), 5–32.

Breiman, L., & Cutler, A. (1993). A deterministic algorithm for global optimization. Mathematical Programming, 58(1), 179-199.

Campolo, M., Soldati, A., & Andreussi, P. (2003). Artifcial neural network approach to flood forecasing in the River Arno. Hydrological Sciences Journal, 48(3), 381-398.

Cannas, B., Fanni, A., See, L., & Sias, G. (2006). Data preprocessing for river flow forecasing using neural networks: wavelet transforms and data partitioning. Physics and Chemisry of the Earth, Parts A/B/C, 31(18), 1164-1171.

Darbandi, S., & Pourhosseini, F. A. (2018). River flow simulation using a multilayer perceptron-frefly algorithm model. Applied Water Science, 8(3), 1-9.

Daubechies, I. (1990). The wavelet transform, timefrequency localization and signal analysis. IEEE transactions on information theory, 36(5), 961-1005.

Ekmekcioğlu, Ö., Başakın, E. E., & Özger, M. (2022). Treebased nonlinear ensemble technique to predict energy dissipation in sepped spillways. European Journal of Environmental and Civil Engineering, 26(8), 3547-3565.

Feng, Z. K., & Niu, W. J. (2021). Hybrid artificial neural network and cooperation search algorithm for nonlinear river flow time series forecasing in humid and semi-humid regions. Knowledge-Based Sysems, 211, 106580.

Friedman, J., Hasie, T., & Tibshirani, R. (2001). The elements of satisical learning (Vol. 1, No. 10). New York: Springer series in satisics.

Hassanjabbar, A., Nezaratian, H., & Wu, P. (2022). Climate change impacts on the flow regime and water quality indicators using an artifcial neural network (ANN): a case sudy in Saskatchewan, Canada. Journal of Water and Climate Change, 13(8), 3046-3060.

Hu, T. S., Lam, K. C., & Ng, S. T. (2005). A Modifed Neural Network for Improving River Flow Prediction/Un Réseau de Neurones Modifé pour Améliorer la Prévision de L’Écoulement Fluvial. Hydrological Sciences Journal, 50(2), 299-318.

Jain, S. K., Das, A., & Srivasava, D. K. (1999). Application of ANN for reservoir inflow prediction and operation. Journal of Water Resources Planning and Management, 125(5), 263-271.

Jayawardena, A. W., Xu, P., & Tsang, F. L. L. (2004, July). Rainfall predication by wavelet decomposition. In Asia Pacifc Association of Hydrology and Water Resources Conference, Singapore.

Kalmegh, S. (2015). Analysis of weka data mining algorithm reptree, simple cart and randomtree for classifcation of indian news. International Journal of Innovative Science, Engineering & Technology, 2(2), 438-446.

Landwehr, N., Hall, M., & Frank, E. (2005). Logisic model trees. Machine learning, 59(1), 161-205.

Leo, B. (2001). Random foress. Machine learning, 45(1), 5-32.

Mallat, S. G. (1989). Multiresolution approximations and wavelet orthonormal bases of L² (R). Transactions of the American mathematical society, 315(1), 69-87.

Meresa, H. (2019). Modelling of river flow in ungauged catchment using remote sensing data: application of the empirical (SCS-CN), artifcial neural network (ANN) and hydrological model (HEC-HMS). Modeling Earth Sysems and Environment, 5(1), 257-273.

Mirzaee, N., & Sarraf, A. (2022). Application of data fusion models in river flow simulation using signals of largescale climate, case sudy: Jiroft Dam Basin. Watershed Engineering and Management, 13(4), 672-689.

Nazeri Tahroudi, M., Mirabbasi, R., Ramezani, Y., & Ahmadi, F. (2022b). Probabilisic Assessment of Monthly River Discharge using Copula and OSVR Approaches. Water Resources Management, 36(6), 2027-2043.

Nazeri Tahroudi, M., Mohammadi, M., & Khalili, K. (2022a). The application of the hybrid copulaGARCH approach in the simulation of extreme discharge values. Applied Water Science, 12(12), 1-13.

Nazeri Tahroudi, M., Ramezani, Y., De Michele, C., & Mirabbasi, R. (2022a). Application of Copula Functions for Bivariate Analysis of Rainfall and River Flow Defciencies in the Siminehrood River Basin, Iran. Journal of Hydrologic Engineering, 27(11), 05022015.

Nazeri Tahroudi, M., Ramezani, Y., De Michele, C., & Mirabbasi, R. (2022b). Application of copula‐based approach as a new data‐driven model for downscaling the mean daily temperature. International Journal of Climatology, 43(1), 240-254.

Nilsson, P., Uvo, C. B., & Berndtsson, R. (2006). Monthly runoff simulation: Comparing and combining conceptual and neural network models. Journal of Hydrology, 321(1-4), 344-363.

Nourani, V., Baghanam, A. H., Adamowski, J., & Kisi, O. (2014). Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, 358-377.

Pfahringer, B. (2010). Random model trees: an effective and scalable regression method.

Pronoos Sedighi, M., Ramezani, Y., Nazeri Tahroudi, M., & Taghian, M. (2022). Joint frequency analysis of river flow rate and suspended sediment load using conditional density of copula functions. Acta Geophysica, 1-13.

Seif, A., & Riahi-Madvar, H. (2019). Improving onedimensional pollution dispersion modeling in rivers using ANFIS and ANN-based GA optimized models. Environmental Science and Pollution Research, 26(1), 867-885.

Solgi, A., Pourhaghi, A., Bahmani, R., & Zarei, H. (2017). Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation. Journal of Earth Sysem Science, 126(5), 1-17.

Wei, S., Yang, H., Song, J., Abbaspour, K., & Xu, Z. (2013). A wavelet-neural network hybrid modelling approach for esimating and predicting river monthly flows. Hydrological Sciences Journal, 58(2), 374-389.

Witten, I. H., & Frank, E. (2002). Data mining: practical machine learning tools and techniques with Java
implementations. Acm Sigmod Record, 31(1), 76-77.

Zamani, R., Mirabbasi, R., Nazeri, M., Meshram, S. G., & Ahmadi, F. (2018). Spatio-temporal analysis of daily, seasonal and annual precipitation concentration in Jharkhand sate, India. Stochasic Environmental Research and Risk Assessment, 32(4), 1085-1097.