Open Access
| Issue |
E3S Web Conf.
Volume 263, 2021
XXIV International Scientific Conference “Construction the Formation of Living Environment” (FORM-2021)
|
|
|---|---|---|
| Article Number | 05034 | |
| Number of page(s) | 7 | |
| Section | Global Environmental Challenges | |
| DOI | https://doi.org/10.1051/e3sconf/202126305034 | |
| Published online | 28 May 2021 | |
- D. Khalaf Hamid, Spatial Analysis to Estimate Runoff Using SCS(CN) to Wadi Almur North of Iraq, Tikrit Journal of Pure Sciences, 21(5), 110-121, (2016). [Google Scholar]
- B. Zhang and R. S. Govindaraju, Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds, Journal of Hydrology, 273, 18–34, (2003). [CrossRef] [Google Scholar]
- M. R. Yazdani, B. Saghafian, M. H. Mahdian, and S. Soltani, Monthly Runoff Estimation Using Artificial Neural Networks, J. Agric. Sci. Technol., 11, 355–362, (2009). [Google Scholar]
- P. Jimeno-s, J. Senent-aparicio, and D. Pulido-velazquez, A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain, Water 2018, doi: 10.3390/w10020192, (2018). [Google Scholar]
- S. Srinivasulu and A. Jain, A comparative analysis of training methods for artificial neural network rainfall – runoff models, Applied Soft Computing, 6, 295–306, doi: 10.1016/j.asoc.2005.02.002, (2006). [CrossRef] [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), doi: 10.17485/ijst/2015/v8i14/54370, (2019). [Google Scholar]
- M. Issa, The relationship between river flow and precipitation in the Orontes Basin, Damascus University Journal, 31(2), (2015). [Google Scholar]
- Y. Hamdan, E. Layos and I. Mohammed, Identify indicators of climate change through the analysis of the amount of rain on upper basin for Orontes River, Al-Baath University Journal, 39(43), (2017). [Google Scholar]
- N. Ghahreman and 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]
- M. Kumar, N. S. Raghuwanshi, R. Singh, W. W. Wallender, and W. O. Pruitt, Estimating Evapotranspiration using Artificial Neural Network, J. Irrig. Drain Eng., 128, 224–233, (2002). [CrossRef] [Google Scholar]
- T. Razavi and K. Province, Estimating of Reference Evapotranspiration by Using Artificial Neural Networks, International Conference on Transport, Environment and Civil Engineering (ICTECE'2012) August, Kuala Lumpur (Malaysia), 80–84, (2012). [Google Scholar]
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