Abdi, P. (2016). Investigating the flood potential of Zanjanroud basin with SCS method and geographic information system, National Committee for Irrigation and Drainage, Technical workshop for coexistence with floods. (In Persian)
Ahmadi, H., & Rahimi, H. (2022). Using a deep learning approach to estimate floods based on area precipitation pattern. Water and Irrigation Management, 11(4), 753-767. https://doi.org/10.22059/jwim.2022.328451.909 (In Persian)
Bagherian Kalat, A., Lashkaripour, Gh.R., & Gafoori, Mohammad. (2021). Evaluating the Impacts of Implemented Watershed Management Project on Vegetal Cover and Sediment Yield in Kakhk Watershed
Project. Journal of Environmental Science and Technology, 23(7), 51-63.
10.30495/jest.2022.27360.3642 (In Persian)
Bak, G., & Bae, Y. (2023). Deep learning algorithm development for river flow prediction: PNP algorithm. Soft Computing, 27(18), 13487-13515. https://doi.org/10.1007/s00500-023-08254-1
Bigdeli, Z., Majnooni Heris, A., Delirhasannia, R., & Karimi, S. (2023). Rainfall-Runoff Modeling of Aji Chai Basin Using Random Forest and Artificial Neural Network Models. New Research in Sustainable Water Engineering, 1(2), 27-42. https://doi.org/ 10.22103/nrswe.2023.20278.1013 (In Persian)
Botsis, D., Latinopulos, P., & Diamantaras, K. (2011, September). Rainfall-runoff modeling using support vector regression and artificial neural networks. In 12th International Conference on Environmental Science and Technology (CEST2011) (pp. 8-10).
Convertino, M., Annis, A., & Nardi, F. (2019). Information-theoretic portfolio decision model for optimal flood management.
Environmental Modelling & Software,
119, 258-274.
https://doi.org/10.1016/j.envsoft.2019.06.013
Costa, M. H., Botta, A., & Cardille, J. A. (2003). Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia. Journal of hydrology, 283(1-4), 206-217. https://doi.org/10.1016/S0022-1694(03)00267-1
Dawson, C. W., Abrahart, R. J., Shamseldin, A. Y., & Wilby, R. L. (2006). Flood estimation at ungauged sites using artificial neural networks. Journal of hydrology, 319(1-4), 391-409. https://doi.org/10.1016/j.jhydrol.2005.07.032
de Oliveira Serrão E A, Silva M T Ferreira T, R, de Ataide LC P, dos Santos C A, de Lima AMM, Gomes DJ C. (2022). Impacts of land use and land cover changes on hydrological processes and sediment yield determined using the SWAT model. International J.of Sediment Research. 37(1), 54-69. https://doi.org/10.1016/j.ijsrc.2021.04.002
Dos Santos, V., Laurent, F., Abe, C., & Messner, F. (2018). Hydrologic response to land use change in a large basin in eastern Amazon. Water, 10(4), 429. https://doi.org/10.3390/w10040429
Eslahi, M., Pourasghar, F., Mansouri Derakhshan, N., & Akbarzadeh, U. (2022). The estimation of Probable Maximum Precipitation (PMP) with Flood Forecast Approach in Urmia Lake Basin. Journal of Climate Research, 1401(49), 103-114. https://doi.org/10.1016/j.envsoft.2022.105436 (In Persian)
Essam, Y., Huang, Y. F., Ng, J. L., Birima, A. H., Ahmed, A. N., & El-Shafie, A. (2022). Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms. Scientific reports, 12(1), 3883. https://doi.org/10.1038/s41598-022-07693-4
Fallah, M., Bahrami, H., Asadi, H. (2022). Investigating Capabilities of Machine Learning Techniques in Forecasting Daily Streamflow Using Some Meteorological Data and Normalized Difference Snow Index (Case Study: Latian and Navroud Basins). Iranian Journal of Soil and Water Research, 53(5), 1127. https://doi.org/10.22059/ijswr.2022.338986.669207 (In Persian)
Gharakhanlou, N. M., & Perez, L. (2023). Flood susceptible prediction through the use of geospatial variables and machine learning methods. Journal of Hydrology, 617, 129121. https://doi.org/10.1016/j.jhydrol.2023.129121
Golzari S, Zareabyaneh H, Delavar M, Mobargaei Dinan N. (2020). Performance of SWAT Model in Quantitative and Qualitative Simulation of Runoff and Watershed Protective Measures in Zarrinehrood Basin. JWMR, 11(22) :111-120. https://doi.org/10.52547/jwmr.11.22.111 (In Persian)
Hasani, A., Modaresi, F., & Ebrahimi, K. (2021). Evaluation of intelligent prediction models towards precision of flood peak flows. Iranian Journal of Irrigation & Drainage, 15(4), 794-804. https://dorl.net/dor/20.1001.1.20087942.2021.15.4.5.5 (In Persian)
Jiang, Z., Yang, S., Liu, Z., Xu, Y., Xiong, Y., Qi, S., ... & Xu, T. (2022). Coupling machine learning and weather forecast to predict farmland flood disaster: A case study in Yangtze River basin. Environmental Modelling & Software, 155, 105436. https://doi.org/10.1016/j.envsoft.2022.105436
Jonkman, S. N. (2005). Global perspectives on loss of human life caused by floods. Natural hazards, 34(2), 151-175. https://doi.org/10.1007/s11069-004-8891-3
Koohdarzi Moghaddam, M., Taghipour, S. M., & Erfani Pourghasemi, V. (2022). Effectiveness of watershed management measures on soil erosion and sediment yield reduction (Case study: Doholkooh Watershed, South Khorasan Province). Water and Soil Management and Modelling, 2(4), 1-17. https://doi.org/10.22098/mmws.2022.10282.1080 (In Persian)
Lawal, Z. K., Yassin, H., & Zakari, R. Y. (2021, December). Flood prediction using machine learning models: a case study of Kebbi state Nigeria. In 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-6). IEEE.
Legates, D. R., & McCabe Jr, G. J. (1999). Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation. Water resources research, 35(1), 233-241. https://doi.org/10.1029/1998WR900018
Lin, J. Y., Cheng, C. T., & Chau, K. W. (2006). Using support vector machines for long-term discharge prediction. Hydrological sciences journal, 51(4), 599-612. https://doi.org/10.1623/hysj.51.4.599
Mehri S, Moradi H.R, Mostafazadeh R. (2023). Simulation and determination of hydrological balance components in the upstream of Gheshlagh dam using SWAT model. Environment and Water Engineering. doi:
https://doi.org/10.22034/ewe.2023.360340.1805 (In Persian)
Mostafaei, S., Moosavi, V., & Berndtsson, R. (2023). Comparing the Performance of Deep Learning, Polynomial Neural Network and HEC-HMS Models in Predicting Daily Runoff. Iran-Water Resources Research, 19(4), 16-33. https://doi.org/10.22034/iwrr.2023.172260 (In Persian)
Panahi, A.,
Janbaz Ghobadi, Gh.,
khaledi, Sh.,
Motavalli, S. (2023). Forecasting and zoning flood potential according to climate change algorithms (Case Study: Garganrood Watershed).
Geography, 21(78): 109-134. https://doi.org/20.1001.1.27833739.1402.21.78.7.2 (In Persian)
Puttinaovarat, S., & Horkaew, P. (2020). Flood forecasting system based on integrated big and crowdsource data by using machine learning techniques. IEEE Access, 8, 5885-5905. https://doi.org/10.1109/ACCESS.2019.2963819
Saedi, A., Saghafian, B., & Moazami, S. (2020). Uncertainty of Flood Forecasts via ensemble precipitation forecasts of seven NWP Models for Spring 2019 Golestan Flood.
Iran-Water Resources Research,
16(1), 347-359. https://doi.org/
20.1001.1.17352347.1399.16.1.23.7 (In Persian)
Sattari, M., Abdollah Pourazad, M., & Mirabbasi Najafabadi, R. (2016). Technical Note: Hourly river flow forecast of Aharchay River using machine learning methods. Watershed Engineering and Management, 8(1), 115-127. https://doi.org/10.22092/ijwmse.2016.105979. (In Persian)
Shabanlou, S., Sedghi, H., Saghafian, B., & Mousavi, S. H. (2008). Flood zoning in Golestan’s rivers network using GIS. Iranian Water Researches Journal, 2(2), 11-22. https://doi.org/10.1029/1998WR900018 (In Persian)
Silveira, L., Charbonnier, F., & Genta, J. L. (2000). The antecedent soil moisture condition of the curve number procedure. Hydrological sciences journal, 45(1), 3-12. https://doi.org/10.1080/02626660009492302
Sönmez, O., & Bizimana, H. (2020). Flood hazard risk evaluation using fuzzy logic and weightage-based combination methods in geographic information system. Scientia Iranica. Transaction A, Civil Engineering, 27(2), 517-528. https://doi.org/10.24200/sci.2018.21037
Tongal, H., & Booij, M. J. (2018). Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. Journal of hydrology, 564, 266-282. https://doi.org/10.1016/j.jhydrol.2018.07.004
Zanial, W. N. C. W., Malek, M. B. A., Reba, M. N. M., Zaini, N., Ahmed, A. N., Sherif, M., & Elshafie, A. (2023). River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network. Applied Water Science, 13(1), 28. https://doi.org/10.1007/s13201-022-01830-0
Zehra, N. (2020). Prediction analysis of floods using machine learning algorithms (NARX & SVM). International Journal of Sciences: Basic and Applied Research (IJSBAR), 49(2), 24-34. https://doi.org/10.1109/ACCESS.2019.2963819