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All issues Volume 457 (2023) E3S Web Conf., 457 (2023) 02040 References
Open Access
Issue
E3S Web Conf.
Volume 457, 2023
International Scientific and Practical Symposium “The Future of the Construction Industry: Challenges and Development Prospects” (FCI-2023)
Article Number 02040
Number of page(s) 8
Section Integrated Safety in Construction
DOI https://doi.org/10.1051/e3sconf/202345702040
Published online 05 December 2023
  1. J. Seibert, and M. J. P. Vis, Teaching hydrological modeling with a user-friendly catchment-runoff-model software package, Hydrol. Earth Syst. Sci., 16, 3315–3325, 2012, DOI: 10.5194/hess-16-3315-2012 [CrossRef] [Google Scholar]
  2. A. Kumar, P. Kumar, V. K. Singh, Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation, Water Resour Manage 33, 1217–1231, 2019. https://doi.org/10.1007/s11269-018-2178-z [CrossRef] [Google Scholar]
  3. A. Bhusal, U. Parajuli, S. Regmi, A. Kalra, Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois. Hydrology 2022, 9, 117. https://doi.org/10.3390/hydrology9070117 [CrossRef] [Google Scholar]
  4. 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]
  5. 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]
  6. 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]
  7. S. M. Chen, Y. M. Wang, I. Tsou, Using artificial neural network approach for modelling rainfall-runoff due to typhoon, J. Earth Syst. Sci. 122, No. 2, pp. 399–405, 2013. [CrossRef] [Google Scholar]
  8. 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]
  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. W. Wang, K. Chau, D. Xu, X. Chen, Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition, Water Resour Manage, 29:2655–2675, 2015, DOI: 10.1007/s11269-015-0962-6. [CrossRef] [Google Scholar]
  11. 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]
  12. N. Ghahreman, M. Sameti, Comparison of M5 Model Tree and Artificial Neural Network for Estimating Potential Evapotranspiration in Semi-arid Climates, DESERT, 1, 75–81, 2014. [Google Scholar]
  13. 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]
  14. M. Kumar; N. S. Raghuwanshi, R. Singh, W. W. Wallender, W. O. Pruitt, Estimating Evapotranspiration using Artificial Neural Network, J. Irrig. Drain Eng., 128, 224–233, 2002. [CrossRef] [Google Scholar]
  15. J. Behmanesh, S. Ayashm, Rainfall-runoff modeling in the Turkey River using numerical and regression methods, Journal of Fundamental and Applied Sciences, Vol. 7 No. 1, 91–102, 2015, DOI: 10.4314/jfas.v7i1.8. [CrossRef] [Google Scholar]
  16. M. H. Kazeminezhad, 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]
  17. 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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