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All issues Volume 401 (2023) E3S Web of Conf., 401 (2023) 01035 References
Open Access
Issue
E3S Web of Conf.
Volume 401, 2023
V International Scientific Conference “Construction Mechanics, Hydraulics and Water Resources Engineering” (CONMECHYDRO - 2023)
Article Number 01035
Number of page(s) 10
Section Hydraulics of Structures, Hydraulic Engineering and Land Reclamation Construction
DOI https://doi.org/10.1051/e3sconf/202340101035
Published online 11 July 2023
  1. Khalaf Hamid D 2016, Spatial Analysis to Estimate Runoff Using SCS(CN) to Wadi Almur North of Iraq, Tikrit Journal of Pure Sciences, 21(5), 110-121 [Google Scholar]
  2. Zhang B and Govindaraju R S 2003, Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds, Journal of Hydrology, 273, 18–34 [CrossRef] [Google Scholar]
  3. Yazdani M R, Saghafian B, Mahdian M H and Soltani S 2009, Monthly Runoff Estimation Using Artificial Neural Networks, J. Agric. Sci. Technol., 11, 355–362 [Google Scholar]
  4. Srinivasulu S and Jain A 2006, A comparative analysis of training methods for artificial neural network rainfall – runoff models, Applied Soft Computing, 6, 295-306. [CrossRef] [Google Scholar]
  5. Solaimani K 2009, Rainfall-runoff Prediction Based on Artificial Neural Network ( A Case Study : Jarahi Watershed ), American-Eurasian J. Agric. & Environ. Sci., 5, 6, 856–865 [Google Scholar]
  6. Chen S M, Wang Y M and Tsou I 2013, Using artificial neural network approach for modelling rainfall – runoff due to typhoon, J. Earth Syst. Sci., 2, 399–405 [CrossRef] [Google Scholar]
  7. Chakravarti A, Joshi N and Panjiar H 2019, Rainfall Runoff Analysis Using Artificial Neural Network, Indian Journal of Science and Technology, 8(14). [Google Scholar]
  8. C. Mahabir, F. E. Hicks, and A. R. Fayek, Application of fuzzy logic to forecast seasonal runoff, Hydrol. Process., 17(18), 3749–3762, (2003), doi: 10.1002/hyp.1359 [CrossRef] [Google Scholar]
  9. G. Tayfur and V. P. Singh, ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff, J. Hydraul. Eng., 132 (12), 1321–1330 (2006) [CrossRef] [Google Scholar]
  10. Z. Şen and A. Altunkaynak, A comparative fuzzy logic approach to runoff coefficient and runoff estimation, Hydrol. Process., 20(9), 1993–2009, (2006). [CrossRef] [Google Scholar]
  11. A. K. Lohani, N. K. Goel, and K. K. S. Bhatia, Comparative study of neural network, fuzzy logic and linear transfer function techniques in daily rainfall-runoff modelling under different input domains, Hydrol. Process., 25(2), 175–193, (2011). [CrossRef] [Google Scholar]
  12. K. H. Wang and A. Altunkaynak, Comparative Case Study of Rainfall-Runoff Modeling between SWMM and Fuzzy Logic Approach, J. Hydrol. Eng., 17(2), 283– 291, (2012), doi: 10.1061/(asce)he.1943-5584.0000419. [CrossRef] [Google Scholar]
  13. A. Nath F. Mthethwa, G. Saha, Runoff estimation using modified adaptive neuro-fuzzy inference system, Environ Eng Res, Volume 25(4); (2020). [Google Scholar]
  14. A. Montanari and R. Rosso, Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation. WATER RESOURCES RESEARCH, VOL. 33, NO. 5, PAGES 1035-1044, MAY 1997 [CrossRef] [Google Scholar]
  15. M.R. Ghanbarpour, K.C. Abbaspour, G. Jalalvand, and G.A. Moghaddam – Stochastic modeling of surface stream flow at different time scales: Sangsoorakh karst basin, Iran. Journal of Cave and Karst Studies, v. 72, no. 1, p. 1–10. (2010). [CrossRef] [Google Scholar]
  16. M. Valipour, Long-term runoff study using SARIMA and ARIMA models in the United States. Meteorol. Appl. 22: 592–598 (2015), DOI: 10.1002/met.1491 [CrossRef] [Google Scholar]
  17. P. J. Oliveiraa , J. L. S. and P. Cheung, Parameter Estimation of Seasonal Arima Models for Water Demand Forecasting using the Harmony Search Algorithm, Procedia Engineering 186, 177 – 185, (2017) [CrossRef] [Google Scholar]
  18. Kok M. T., Wei K. S., Chien L. W., Huiling J. K. and Suresh B. P., 2019, Evaluation of performance of Active, Beautiful and Clean (ABC) on stormwater runoff management using MIKE URBAN: a case study in a residential estate in Singapore, Urban Water Journal, DOI: 10.1080/1573062X.2019.1634744 [Google Scholar]
  19. Aredo M. R., Hatiye S. D., and Pingale S. M., 2021, Modeling the rainfall‑runof using MIKE 11 NAM model in Shaya catchment, Ethiopia, 2021, Modeling Earth Systems and Environment, https://doi.org/10.1007/s40808-020-01054-8 [Google Scholar]
  20. Ghosh A., Roy M. B., and Roy P.K., 2022, Evaluating the performance of MIKE NAM model on rainfall–runof in lower Gangetic foodplain, West Bengal, India, Modeling Earth Systems and Environment, https://doi.org/10.1007/s40808-021-01347-6 [Google Scholar]
  21. Anghesom A. Ghebrehiwot, Dmitry V. Kozlov, 2020, Assessment of applicability of mike 11 NAM hydrological module for rainfall runoff modelling in a poorly studied river basin, Vestnik MGSU,Vol. 15(7). [Google Scholar]
  22. Ghahreman N and Sameti M 2014, Comparison of M5 Model Tree and Artificial Neural Network for Estimating Potential Evapotranspiration in Semiarid Climates, DESERT, 1, 75–81 [Google Scholar]
  23. Kazeminezhad M H and Mousavi S G 2005, Application of fuzzy inference system in the prediction of wave parameters, Ocean Engineering, 32, 1709–1725, doi: 10.1016/j.oceaneng.2005.02.001. [CrossRef] [Google Scholar]
  24. Al Shalawi S K H 2011, Comparison of Artificial Neural Network and Fuzzy Logic System applications for estimating pan-evaporation for Mosul region, Kufa Magazine for Mathematics and Computers, 1(3), 23-32 [Google Scholar]
  25. Shamsudin S. and Hashim N. 2022 RAINFALL RUNOFF SIMULATION USING MIKE11 NAM. Journal kejuruteraan awam (Journal of civil engineering) 15(2) [Google Scholar]

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