رستمی، س.، شاهی نژاد، ب.، الله یونسی، ح.، ترابی پوده، ح.، و دهقانی، ر. (1400). ارزیابی کارایی مدل‌های هوش مصنوعی هیبریدی نوین در برآورد دبی سیلابی. هیدروژئومورفولوژی، 8(29)، 187-201.
زینلی، م.ج.، و خاشعی سیوکی، ع. (1397). ارزیابی دقت مدل‌های هم‌زمان سری زمانی و شبکه عصبی در مدلسازی بارش-رواناب (مطالعه موردی: حوضه آبریز نازلو‍چای). پژوهش‌های حفاظت آب و خاک، 25(2)، 315-321.
غفاری.، و وفاخواه، م. (1392). شبیه‍سازی فرآیند بارش-رواناب با استفاده از شبکه عصبی مصنوعی و سیستم عصبی-فازی تطبیقی (مطالعه موردی: حوضه آبخیز حاجی‍قوشان). پژوهشنامه مدیریت حوزه آبخیز، 4(8)، 120-136.
قربانی، م.ع.، ازانی، ع.، و نقی پور، ل. (1395). مقایسه عملکرد ماشین بردار پشتیبان با سایر مدل‌های هوشمند در شبیه‍سازی فرآیند بارش-رواناب. پژوهشنامه مدیریت حوزه آبخیز، 7(13)، 92-103.
مومنه، ص. (1401). مقایسۀ عملکرد مدل‌های هوش مصنوعی با مدل IHACRES در مدلسازی جریان حوضۀ آبریز رودخانۀ گاماسیاب. مدلسازی و مدیریت آب و خاک، 2(3)، 1-16.
میرزانیا، ا.، ملک احمدی، ح.، شاه‌محمدی، ی.، و ابراهیم‌زاده، ع. (1400). تأثیر موجک بر افزایش دقت مدل‌های تخمینی در مدلسازی بارش-رواناب. مدلسازی و مدیریت آب و خاک، 1(3)، 67-79.
نجیب‌زاده، ن.، قادری، ک.، و احمدی، م.م. (1398). بهره‍گیری از روشهای رگرسیون بردار پشتیبان و شبکه عصبی مصنوعی در مدلسازی بارش رواناب (مطالعه موردی: حوضه آبریز سد صفارود). آبیاری و زهکشی ایران، 13(6)، 1709-1720.
Adamowski, J. (2013). Using support vector regression to predict direct runoff, base flow and total flow in a mountainous watershed with limited data in Uttaranchal, India. Annals of Warsaw University of Life Sciences-SGGW. Land Reclamation, 45(1).
Afan, H.A., El-Shafie, A., Yaseen, Z.M., Hameed, M.M., Wan Mohtar, W.H.M., & Hussain, A. (2015). ANN based sediment prediction model utilizing different input scenarios. Water Resources Management, 29(4), 1231-1245.
Ahmed, J.A., & Sarma, A.K. (2007). Artificial neural network model for synthetic streamflow generation. Water Resources Management, 21(6), 1015-1029.
Al-Abadi, A.M., & Shahid, S. (2016). Spatial mapping of artesian zone at Iraqi southern desert using a GIS-based random forest machine learning model. Modeling Earth Systems and Environment, 2(2), 1-17.
Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & Structures, 169, 1-12.
Aytek, A., Asce, M., & Alp, M. (2008). An application of artificial intelligence for rainfall-runoff modeling. Journal of Earth System Science, 117(2), 145-155.
Barzegari Banadkooki, F., Ehteram, M., Panahi, F., Sammen, S.Sh., Binti Othman, F., & El Shafie, A. (2020). Estimation of total dissolved solids (TDS) using new hybrid machine learning models. Journal of Hydrology, 587, 124989.
Behzad, M., Asghari, K., Eazi, M., & Palhang, M. (2009). Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems With Applications, 36(4), 7624-7629.
Beven, K. (1989). Changing ideas in hydrology- The case of physically-based models. Journal of Hydrology, 105(1-2), 157-172.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
Choy, K.Y., & Chan, C.W. (2003). Modelling of river discharges and rainfall using radial basis function networks based on support vector regression. International Journal of Systems Science, 34(14-15), 763-773.
Cutler, A., Cutler, D.R., & Stevens, J.R. (2012). Random forests. Pp. 157-175, In: Ensemble machine learning. Springer, Boston, MA.
Danandeh Mehr, A., Kahya, E., Şahin, A., & Nazemosadat, M.J. (2015). Successive-station monthly streamflow prediction using different artificial neural network algorithms. International Journal of Environmental Science and Technology, 12(7), 2191-2200.
Dawson, C.W., & Wilby, R.L. (2001). Hydrological modelling using artificial neural networks. Progress in Physical Geography: Earth and Environment, 25(1), 80-108.
Dehghani, R., & Poudeh, H.T. (2021). Applying hybrid artificial algorithms to the estimation of river flow: a case study of Karkheh catchment area. Arabian Journal of Geosciences, 14(9), 1-19.
Dehghani, R., & Torabi Poudeh, H. (2022). Application of novel hybrid artificial intelligence algorithms to groundwater simulation. International Journal of Environmental Science and Technology, 19(5), 4351-4368.
Dehghani, R., Poudeh, H.T., Younesi, H., & Shahinejad, B. (2020). Daily streamflow prediction using support vector machine-artificial flora (SVM-AF) hybrid model. Acta Geophysica, 68(6), 1763-1778.
Dehghani, R., Torabi, H., Younesi, H., & Shahinejad, B. (2021). Application of wavelet support vector machine (WSVM) model in predicting river flow (Case study: Dez basin). Watershed Engineering and Management, 13(1), 98-110.
Deo, R.C., & Şahin, M. (2016). An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland. Environmental Monitoring and Assessment, 188(2), 1-24.
Flint, A.L., Flint, L.E., Bodvarsson, G.S., Kwicklis, E.M., & Fabryka-Martin, J. (2001). Evolution of the conceptual model of unsaturated zone hydrology at Yucca Mountain, Nevada. Journal of Hydrology, 247(1-2), 1-30.
Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., & Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing, 11(2), 196.
Ghafari, G.A., & Vafakhah, M. (2014). Simulation of rainfall-runoff process using artificial neural network and adaptive neuro-fuzzy interface system (case study: Hajighoshan Watershed). Journal of Watershed Management Research, 4(8), 120-136 (in Persian).
Ghorbani, M.A., Azani, A., & Naghipour, L. (2016). Comparison of the performance of support vector machine with other intelligent techniques to simulate rainfall-runoff process. Journal of Watershed Management Research, 7(13), 92-103 (in Persian).
Guven, A., & Kişi, Ö. (2011). Daily pan evaporation modeling using linear genetic programming technique. Irrigation Science, 29(2), 135-145.
Hadi, S.J., & Tombul, M. (2018). Streamflow forecasting using four wavelet transformation combinations approaches with data-driven models: A comparative study. An International Journal Water Resources Management, 32(14), 4661-4679.
Hajian, R., Jalali, M.R., & Mastouri, R. (2022). Multi-step Lake Urmia water level forecasting using ensemble of bagging based tree models. Earth Science Informatics, 15, 2515–2543.
Hassanien, A.E., Rizk-Allah, R.M., & Elhoseny, M. (2018). A hybrid crow search algorithm based on rough searching scheme for solving engineering optimization problems. Journal of Ambient Intelligence and Humanized Computing, 1-25.
Hussien, A.G., Amin, M., Wang, M., Liang, G., Alsanad, A., Gumaei, A., & Chen, H. (2020). Crow search algorithm: theory, recent advances, and applications. IEEE Access, 8, 173548-173565.
Kambalimath, S.S., & Deka, P.C. (2021). Performance enhancement of SVM model using discrete wavelet transform for daily streamflow forecasting. Environmental Earth Sciences, 80(3), 1-16.
Khosravi, K., Cooper, J.R., Daggupati, P., Pham, B.T., & Bui, D.T. (2020). Bedload transport rate prediction: Application of novel hybrid data mining techniques. Journal of Hydrology, 585, 124774.
Khosravi, K., Mao, L., Kisi, O., Yaseen, Z.M., & Shahid, S. (2018). Quantifying hourly suspended sediment load using data mining models: case study of a glacierized Andean catchment in Chile. Journal of Hydrology, 567, 165-179.
Khosravi, K., Miraki, S., Saco, P.M., & Farmani, R. (2021). Short-term river streamflow modeling using ensemble-based additive learner approach. Journal of Hydro-environment Research, 39, 81-91.
Liaw, A., & Wiener, M. (2002). Classification and regression by random forest. R News, 2(3), 18-22.
Malik, A., Tikhamarine, Y., Souag-Gamane, D., Kisi, O., & Pham, Q.B. (2020). Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction. Stochastic Environmental Research and Risk Assessment, 34(11), 1755-1773.
Mamun, A.A., Islam, A.R.M.T., Khosravi, K., & Singh, S.K. (2022). Suspended sediment load prediction using hybrid bagging-based Heuristic Search Algorithm. Geocarto International, 37(27), 1-32.
Meraihi, Y., Gabis, A.B., Ramdane-Cherif, A., & Acheli, D. (2021). A comprehensive survey of crow search algorithm and its applications. Artificial Intelligence Review, 54(4), 2669-2716.
Mirzania, E., Malek Ahmadi, H., Shahmohammadi, Y., & Ebrahim Zadeh, A. (2021). Impact of wavelet on accuracy of estimated models in rainfall-runoff modeling (Case study: Sufi Chay). Water and Soil Management and Modeling (WSMM), 1(3), 67-79 (in Persian).
Misra, D., Oommen, T., Agarwal, A., Mishra, S.K., & Thompson, A.M. (2009). Application and analysis of support vector machine based simulation for runoff and sediment yield. Biosystems Engineering, 103(4), 527-535.
Moldovan, D., Chifu, V., Pop, C., Cioara, T., Anghel, I., & Salomie, I. (2018). Chicken swarm optimization and deep learning for manufacturing processes. 17th RoEduNet conference: networking in education and research (RoEduNet), Cluj-Napoca, Romania, Pp. 1-6.
Momeneh, S. (2022). Performance comparison of artificial intelligence models with IHACRES model in streamflow modeling of the Gamasiab River catchment. Water and Soil Management and Modeling, 2(3), 1-16 (in Persian).
Najibzade, N., Qaderi, K., Ahmadi, M.M., (2020). Rainfall-runoff modelling using support vector regression and artificial neural network models (case study: SafaRoud Dam Watershed). Iranian Journal of Irrigation & Drainage, 13(6), 1709-1720 (in Persian).
Naghibi, S.A., Ahmadi, K., & Daneshi, A. (2017). Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31(9), 2761-2775.
Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A., & Pereira, J.M. (2012). Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. Forest Ecology and Management, 275, 117-129.
Panah, P.G., Bornapour, M., Hemmati, R., & Guerrero, J. M. (2021). Charging station stochastic programming for hydrogen/battery electric buses using multi-criteria crow search algorithm. Renewable and Sustainable Energy Reviews, 144, 111046.
Pandhiani, S.M., Sihag, P., Shabri, A.B., Singh, B., & Pham, Q.B. (2020). Time-series prediction of streamflows of Malaysian rivers using data-driven techniques. Journal of Irrigation and Drainage Engineering, 146(7), 04020013.
Pijarski, P., & Kacejko, P. (2019). A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG). Engineering Optimization, 51(12), 2049-2068.
Rashidi, S., Vafakhah, M., Lafdani, E.K., & Javadi, M.R. (2016). Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arabian Journal of Geosciences, 9(11), 1-15.
Remesan, R., Shamim, M.A., Han, D., & Mathew, J. (2009). Runoff prediction using an integrated hybrid modelling scheme. Journal of Hydrology, 372(1-4), 48-60.
Roshni, T., Mirzania, E., Hasanpour Kashani, M., Bui, Q. A.T., & Shamshirband, S. (2022). Hybrid support vector regression models with algorithm of innovative gunner for the simulation of groundwater level. Acta Geophysica, 70(4), 1885-1898.
Roustami, S., Shahinejad, B., Younesi, H., Torabi poudeh, H., & Dehghani, R. (2022). Analysis of new hybrid artificial intelligence models in estimating flood flow. Hydrogeomorphology, 8(29), 187-201 (in Persian).
Samantaray, S., Tripathy, O., Sahoo, A., & Ghose, D.K. (2020). Rainfall forecasting through ANN and SVM in Bolangir Watershed, India. Smart Intelligent Computing and Applications, 159, 767-774.
Samsudin, R., Saad, P., & Shabri, A. (2011). River flow time series using least squares support vector machines. Hydrology and Earth System Sciences, 15(6), 1835-1852.
Sarma, S.K. (2021). Optimally configured deep convolutional neural network for attack detection in internet of things: Impact of algorithm of the innovative gunner. An International Journal Wireless Personal Communications, 118(1), 239-260.
Shahdad, M., & Saber, B. (2022). Drought forecasting using new advanced ensemble-based models of reduced error pruning tree. Acta Geophysica, 70(2), 697-712.
Sharafati, A., Khosravi, K., Khosravinia, P., Ahmed, K., Salman, S.A., Yaseen, Z.M., & Shahid, S. (2019). The potential of novel data mining models for global solar radiation prediction. International Journal of Environmental Science and Technology, 16(11), 7147-7164.
Sudheer, C., Maheswaran, R., Panigrahi, B.K., & Mathur, S. (2014). A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Computing and Applications, 24(6), 1381-1389.
Vapnik, V., Golowich, S., & Smola, A. (1996). Support vector method for function approximation, regression estimation and signal processing. Advances in neural information processing systems, 9.
Were, K., Bui, D.T., Dick, B., & Singh, B.R. (2015). A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, 52, 394-403.
Yousefi, V., Kheiri, S., & Rajebi, S. (2020). Evaluation of K-nearest neighbor, bayesian, perceptron, RBF and SVM neural networks in diagnosis of dermatology disease. International Journal on Technical and Physical Problems of Engineering, 129(1), 114-120.
Yu, P.S., Chen, S.T., & Chang, I.F. (2006). Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328(3-4), 704-716.
Zhu, M., Sun, W., Hahn, A., Wen, Y., Xiao, C., & Tao, W. (2020). Adaptive modeling of maritime autonomous surface ships with uncertainty using a weighted LS-SVR robuts to outliers. Ocean Engineering, 200(2), 107053.
Zeinali, M.J., & Khashei Siuki, A. (2018). Assessing the Accuracy of Contemporaneous time series and neural network models in modeling rainfall-runoff (case study: Nazloochaei Catchment). Journal of Water and Soil Conservation, 25(2), 315-321 (in Persian).
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