Open Access
Issue |
E3S Web of Conf.
Volume 410, 2023
XXVI International Scientific Conference “Construction the Formation of Living Environment” (FORM-2023)
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Article Number | 05014 | |
Number of page(s) | 8 | |
Section | Hydrotechnical Construction and Melioration | |
DOI | https://doi.org/10.1051/e3sconf/202341005014 | |
Published online | 09 August 2023 |
- M. Fereydooni, M. Rahnemaei, H. Babazadeh, H. Sedghi, M.R. Elhami, Comparison of artificial neural networks and stochastic models in river discharge forecasting, (Case study:Ghara- Aghaj River, Fars Province, Iran), African Journal of Agricultural Research, Vol. 7(40), pp. 5446-5458, 23 October, (2012). DOI: 10.5897/AJAR11.1091 [Google Scholar]
- K. Solaimani, Rainfall-runoff Prediction Based on Artificial Neural Network (A Case Study: Jarahi Watershed), American-Eurasian J. Agric. & Environ. Sci., 5, 6, 856–865, (2009) [Google Scholar]
- S. M. Chen, Y. M. Wang and I. Tsou, Using artificial neural network approach for modelling rainfall – runoff due to typhoon, J. Earth Syst. Sci., 2, 399–405, (2013) [CrossRef] [Google Scholar]
- A. Chakravarti, N. Joshi and H. Panjiar, Rainfall Runoff Analysis Using Artificial Neural Network, Indian Journal of Science and Technology, 8(14), (2019), doi: 10.17485/ijst/2015/v8i14/54370 [Google Scholar]
- 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]
- 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]
- Z. Şen and A. Altunkaynak, A comparative fuzzy logic approach to runoff coefficient and runoff estimation, Hydrol. Process., 20(9), 1993–2009, (2006), doi: 10.1002/hyp.5992. [CrossRef] [Google Scholar]
- 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) doi:10.1002/hyp.7831. [CrossRef] [Google Scholar]
- 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]
- A. Nath F. Mthethwa, G. Saha, Runoff estimation using modified adaptive neuro-fuzzy inference system, Environ Eng Res, Volume 25(4); (2020), https://doi.org/10.4491/eer.2019.166 [Google Scholar]
- 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]
- 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) DOI: 10.4311/jcks2007ES0017 [CrossRef] [Google Scholar]
- 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]
- 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]
- M. H. Kazeminezhad and S. G. Mousavi, Application of fuzzy inference system in the prediction of wave parameters, Ocean Engineering, 32, 1709–1725, (2005) doi: 10.1016/j.oceaneng.2005.02.001. [CrossRef] [Google Scholar]
- S. K. H. Al Shalawi, 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 (2011) [Google Scholar]
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