Azad, A., Manoochehri, M., Kashi, H., Farzin, S., Karami, H., Nourani, V. and Shiri, J. 2019. Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling. Journal of hydrology. 571: 214-224
Breiman, L. 2001. Random forests. Machine learning. 45: 5-32
Breiman, L. 2017. Classification and regression trees. Routledge.
Bui, D. T., Ngo, P. T. T., Pham, T. D., Jaafari, A., Minh, N. Q., Hoa, P. V. and Samui, P. 2019. A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. Catena. 179: 184-196
Byeon, H. 2015. Development of prediction model for endocrine disorders in the Korean elderly using CART algorithm. Development. 6(9)
Chen, T. and Guestrin, C. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785-794
Chen, Y., Zhang, W., Yang, J., Xu, Y., Cheng, W. and Peng, L. 2023. Extraction Methods for Small-scale Features on a Large Scale: Investigating Object-oriented Cart Decision Tree for Gravel Information Extraction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
Ehteram, M., Mousavi, S. F., Karami, H., Farzin, S., Singh, V. P., Chau, K. W. and El-Shafie, A. 2018. Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty. Journal of Hydroinformatics. 20(2): 332-355
El-Haddad, B. A., Youssef, A. M., Pourghasemi, H. R., Pradhan, B., El-Shater, A. H. and El-Khashab, M. H. 2021. Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt. Natural Hazards, 105: 83-114.
Elsafi, S. H. 2014. Artificial neural networks (ANNs) for flood forecasting at Dongola Station in the River Nile, Sudan. Alexandria Engineering Journal. 53 (3), 655-662
Gayathri, B. M. and Sumathi, C. P. 2016. Comparative study of relevance vector machine with various machine learning techniques used for detecting breast cancer. In 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). 1-5
Gelbart, M. A., Snoek, J. and Adams, R. P. 2014. Bayesian optimization with unknown constraints. 1403.5607
Gholizadeh, M., Jamei, M., Ahmadianfar, I. and Pourrajab, R. 2020. Prediction of nanofluids viscosity using random forest (RF) approach. Chemometrics and Intelligent Laboratory Systems. 201: 104010
Han, Y., Tang, R., Liao, Z., Zhai, B. and Fan, J. 2022. A novel hybrid GOA-XGB model for estimating wheat aboveground biomass using UAV-based multispectral vegetation indices. Remote Sensing. 14(14): 3506
Hansen, N. and Ostermeier, A. 2001. Completely derandomized self-adaptation in evolution strategies. Evolutionary computation. 9(2): 159-195
Hansen, N., Müller, S. D. and Koumoutsakos, P. 2003. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary computation. 11(1): 1-18
Katipoğlu, O. M. and Sarıgöl, M. 2023. Prediction of flood routing results in the Central Anatolian region of Türkiye with various machine learning models. Stochastic Environmental Research and Risk Assessment. 37(6): 2205-2224
Lam, F. M., Leung, J. C. and Kwok, T. C. 2019. The clinical potential of frailty indicators on identifying recurrent fallers in the community: The Mr. Os and Ms. OS cohort study in Hong Kong. Journal of the American Medical Directors Association. 20(12): 1605-1610
Loshchilov, I., Schoenauer, M. and Sebag, M. 2013. Bi-population CMA-ES algorithms with surrogate models and line searches. In Proceedings of the 15th annual conference companion on Genetic and evolutionary computation. 1177-1184
Norouzi, H. and Bazargan, J. 2019. Using the linear muskingum method and the particle swarm optimization (PSO) algorithm for calculating the depth of the rivers flood. Iran-Water Resources Research. 15(3): 344-347
Pal, S. C., Chowdhuri, I., Das, B., Chakrabortty, R., Roy, P., Saha, A. and Shit, M. 2022. Threats of climate change and land use patterns enhance the susceptibility of future floods in India. Journal of environmental management. 305: 114317
Safavi, H.R. 2011. Engineering Hydrology. Arkan Danesh. Isfahan.724
Sarıgöl, M. and Katipoğlu, O. M. 2023. Estimation of hourly flood hydrograph from daily flows using machine learning techniques in the Büyük Menderes River. Natural Hazards. 119(3): 1461-1477
Sharifi, A. 2020. Flood mapping using relevance vector machine and SAR data: A case study from Aqqala, Iran. Journal of the Indian Society of Remote Sensing. 48(9), 1289-1296
Sultana, Z., Sieg, T., Kellermann, P., Müller, M. and Kreibich, H. 2018. Assessment of business interruption of flood-affected companies using random forests. Water. 10(8): 1049
Tayfur, G. 2017. Modern optimization methods in water resources planning, engineering and management. Water Resources Management. 31: 3205-3233
Tawfik, A. M. 2023. River flood routing using artificial neural networks. Ain Shams engineering journal. 14(3): 101904.
Tayfur, G., Moramarco, T. and Singh, V. P. 2007. Predicting and forecasting flow discharge at sites receiving significant lateral inflow. Hydrological Processes: An International Journal. 21 (14): 1848-1859.
Tipping, M. 1999. The relevance vector machine. Advances in neural information processing systems.12
Valikhan Anaraki, M., Mousavi, S. F., Farzin, S., Karami, H., Ehteram, M., Kisi, O., Fai, CM., Hossain, MS., Hayder, G., Ahmed, AN. and El-Shafie, A. 2019. Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies. Sustainability. 11(8): 2337
Valikhan Anaraki, M., Farzin, S., Ahmadianfar, I. and Shams, A. 2024. Development a novel discharge routing method based on the large discharge dataset, Muskingum model, optimization methods, and multi-criteria decision making. Journal of Soft Computing in Civil Engineering. 8(4).
Wang, Z., Chen, H., Zhu, J. and Ding, Z. 2022. Daily PM2. 5 and PM10 forecasting using linear and nonlinear modeling framework based on robust local mean decomposition and moving window ensemble strategy. Applied Soft Computing Journal. 114: 108110
Yuan, X., Zhang, X. and Tian, F. 2020. Research and application of an intelligent networking model for flood forecasting in the arid mountainous basins. Journal of Flood Risk Management .13: 12638.
Zare, M. and Koch, M. 2014. An analysis of MLR and NLP for use in river flood routing and comparison with the Muskingum method. In 11th International Conference on Hydroscience & Engineering (ICHE).
Zhou, L. and Kang, L. 2023. A comparative analysis of multiple machine learning methods for flood routing in the Yangtze River. Water. 15(8): 1556