Alizadeh, Z., Yazdi, J., Kim, J. H., & Al-Shamiri, A. K. (2018). Assessment of Machine Learning Techniques for Monthly Flow Prediction. Water, 10(11), 1676. https://doi.org/10.3390/w10111676
Al-Rawas, G., Nikoo, M. R., Al-Wardy, M., & Etri, T. (2024). A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques. Water, 16(14), 2069. https://doi.org/10.3390/w16142069
Brunner, M.I., Slater, L., M.Tallaksen, L., & Clark, M. (2021). Challenges in modeling and predicting floods and droughts: A review. WIREs Water, 8(3), 2049-1948. https://doi.org/10.1002/wat2.1520
Chang, L., Bai, Y., Xue, J., Gong, L., Zeng, F., Sun, H., Hu, Y., Huang, H., & Ma, Y. (2023). Dynamic Bayesian networks with application in environmental modeling and management: A review. Environmental Modelling & Software, 170, 105835, ISSN 1364-8152. https://doi.org/10.1016/j.envsoft.2023.105835.
Chen, J., Zhong, P., An, R., Zhu, F., & Xu, B. (2019). Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. Environmental Modelling & Software, 111, 409-420, ISSN 1364-8152. https://doi.org/10.1016/j.envsoft.2018.10.007
Ebrahimi, P., Salamti, A., Mohseni Saravi, M., Malekian, A., & Saedodin, A. (2020). Prediction of Environmental Health by Using Gene Expression and Bayesian Network Techniques in Taleghan Watershed. Pasture and watershed management.73(1), 1-18. (In Persian) https://doi:10.22059/jrwm.2018.250908.1221
Ebrahimi, A., Roozbahani, A., & BaniHabib, M.A. (2018). Groundwater Level Prediction Using Dynamic Bayesian Networks Based on Sensitivity Analysis (Case Study: Birjand Plain). Iranian Water, 12(2), 91-100. (In Persian)
Gan, M., Pan, S., Chen, Y., Cheng, C., Pan, H., & Zhu, X. (2021). Application of the Machine Learning LightGBM Model to the Prediction of the Water Levels of the Lower Columbia River. Journal of Marine Science and Engineering, 9(5), 496. https://doi.org/10.3390/jmse9050496
Gai, Y., Li, L., Li, Z., & Shi, H. (2025). The Optimization of Four Key Parameters in the XBeach Model by GLUE Method: Taking Chudao South Beach as an Example. Journal of Marine Science and Engineering, 13(3), 555. https://doi.org/10.3390/jmse13030555
Goodarzi, L., E.Banihabib, M., Roozbahani, A., & Dietric, J. (2019). Bayesian network model for flood forecasting based on atmospheric ensemble forecasts. Nat. Hazards Earth Syst. Sci, 19, 2513-2524. https://doi.org/10.5194/nhess-19-2513-2019
Ke, G., Meng, Q., Finley, Th., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, TY. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. https://doi.org/10.5555/3294996.3295074
Kuhaneswaran, B., Sorwar, G., Alaei, A. R., & Tong, F. (2025). Evolution of Data-Driven Flood Forecasting: Trends, Technologies, and Gaps—A Systematic Mapping Study. Water, 17(15), 2281. https://doi.org/10.3390/w17152281
Kungurtsev, V., Rysavy, P., Idlahcen, F., Rytir, P., & Wodecki, A. (2024). Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisons. https://10.48550/arXiv.2406.17585
Kisi, O., Choubin, B., Deo, R. C., & Yaseen, Z. M. (2019). Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models. Hydrological Sciences Journal, 64(10), 1240-1252. https://doi.org/10.1080/02626667.2019.1632460
Mahmood, MQ., Wang, X., Aziz, F., & Dogulu, N. (2025). Urban flood modelling: Challenges and opportunities - A stakeholder-informed analysis. Environmental Modelling & Software, 191, 106507, ISSN 1364-8152. https://doi.org/10.1016/j.envsoft.2025.106507
Murphy, K., & Weiss, Y. (2013). The Factored Frontier Algorithm for Approximate Inference in DBNs. Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence. https://doi.org/10.48550/arXiv.1301.2296
Nayak, M., Das, S., & Senapati, M. R. (2022). Improving Flood Prediction with Deep Learning Methods. Journal of The Institution of Engineers (India), Series B. 103. https://doi.org/10.1007/s40031-022-00720-y
Nakhaei, M., Zanjanian, H., Nakhaei, P., Gheibi, M., Moezzi, R., Behzadian, K., & Campos, L. C. (2024). Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin. Water, 16(2), 208. https://doi.org/10.3390/w16020208
Okacha, A., Salhi, A., Bouchouou, M., & Fattasse, M. (2024). Enhancing flood forecasting accuracy in Data-Scarce regions through advanced modeling approaches. Journal of Hydrology, 645, Part B, 132283, ISSN 0022-1694. https://doi.org/10.1016/j.jhydrol.2024.132283
Pala, A., Mutlu, S., & Guven, A. (2025). Evaluation of machine learning models for streamflow projections under different greenhouse gas emission scenarios. Engineering Research, ISSN 2307-1877. https://doi.org/10.1016/j.jer.2025.06.006
Roohi, M., Ghafouri, H. R., Ashrafi, S. M., Motagh, M., & Haghshenas Haghighi, M. (2025). A hybrid approach for enhanced flood prediction and assessment: Leveraging physical models, deep learning and satellite remote sensing. Big Earth Data, 9(3), 439–472. https://doi.org/10.1080/20964471.2025.2530850
Roelvink, D., Reiners, A., Van Dongeren, a., Thiel de Veris, J., McCall, R., & Lescinski, J. (2009). Modelling storm impacts on beaches, dunes and barrier islands. Coastal Engineering, 56, 1133-1152. doi:10.1016/j.coastaleng.2009.08.006
Roelvink, D., Reiners, A.D., Van Dongeren, AP., Van Thiel de Veries, J., Lescinski, J., & McCall, R. (2010). XBeach Model Description and Manual. Unesco-IHE Institute for Water Education, Deltares and Delft University of Technology. Page: 3
Szczepanek, R. (2022). Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost. Hydrology, 9(12), 226. https://doi.org/10.3390/hydrology9120226
Yu, J., Li, Y., Huang, X., & Ye, X. (2025). Data quality and uncertainty issues in flood prediction: a systematic review. International Journal of Digital Earth, 18(1), 1-30. https://doi.org/10.1080/17538947.2025.2495738
Zhao, J., Wang, X., Ke, E., & Zhao, Y. (2025). Enhancing urban flood resilience: a LightGBM-NSGA2 hybrid model for impervious surface optimization. Habitat International, 163, 103475, ISSN: 0197-3975. https://doi.org/10.1016/j.habitatint.2025.103475
