References
- Apaydin, H., Feizi, H., Sattari, M. T., Colak, M. S., Shamshirband, S., & Chau, K.-W. (2020). Comparative analysis of Recurrent Neural Network Architectures for reservoir inflow orecasting. Water, 12(5), 1500. https://doi.org/10.3390/w12051500
- Bakiş, R., & Göncü, S. (2015). Completion of missing data in rivers flow measurement: Case study of Zab river basin. Anadolu University Journal of Science and Technology-A Applied Sciences and Engineering, 16(1), 63–79. https://doi.org/10.18038/btd-a.45640
- Bielenki Junior, C., Santos, F. M. d., Povinelli, S. C. S., & Mauad, F. F. (2018). Alternative methodology to gap filling for generation of monthly rainfall series with GIS approach. RBRH, 23. https://doi.org/10.1590/2318-0331.231820170171
- Canchala-Nastar, T., Carvajal-Escobar, Y., Alfonso-Morales, W., Loaiza Cerón, W., & Caicedo, E. (2019). Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks. Data in Brief, 26, 104517. https://doi.org/10.1016/j.dib.2019.104517
- Che Ghani, N. Z., Abu Hasan, Z., & Tze Liang, L. (2014). Estimation of missing rainfall data using GEP: Case study of Raja river, Alor Setar, Kedah. Advances in Artificial Intelligence, 2014, 1–5. https://doi.org/10.1155/2014/716398
- De Martonne, E. (1923). Aridité et Indices D’Aridité. Académie Des Sciences. Comptes Rendus, 182, 1935–1938.
- Dembélé, M., Oriani, F., Tumbulto, J., Mariéthoz, G., & Schaefli, B. (2019). Gap-filling of daily streamflow time series using Direct Sampling in various hydroclimatic settings. Journal of Hydrology, 569, 573–586. https://doi.org/10.1016/j.jhydrol.2018.11.076
- Demirci, M. (2019). Destek Vektör Makineleri ve M5 Karar ağacı yöntemleri Kullanılarak Yağış Akış İlişkisinin Tahmini. DÜMF Mühendislik Dergisi, 10(3), 1113–1124. https://doi.org/10.24012/dumf.525658
- Fotovatikhah, F., Herrera, M., Shamshirband, S., Chau, K., Faizollahzadeh Ardabili, S., & Piran, M. J. (2018). Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work. Engineering Applications of Computational Fluid Mechanics, 12(1), 411–437. https://doi.org/10.1080/19942060.2018.1448896
- Ghorbani, M. A., Kazempour, R., Chau, K.-W., Shamshirband, S., & Taherei Ghazvinei, P. (2018). Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: A case study in Talesh, northern Iran. Engineering Applications of Computational Fluid Mechanics, 12(1), 724–737. https://doi.org/10.1080/19942060.2018.1517052
- Githungo, W., Otengi, S., Wakhungu, J., & Masibayi, E. (2016). Infilling monthly rain gauge data gaps with satellite estimates for ASAL of Kenya. Hydrology, 3(4), 40. https://doi.org/10.3390/hydrology3040040
- Homsi, R., Shiru, M. S., Shahid, S., Ismail, T., Harun, S. B., Al-Ansari, N., Chau, K.-W., & Yaseen, Z. M. (2020). Precipitation projection using a CMIP5 GCM ensemble model: A regional investigation of Syria. Engineering Applications of Computational Fluid Mechanics, 14(1), 90–106. https://doi.org/10.1080/19942060.2019.1683076
- Kamwaga, S., Mulungu, D. M. M., & Valimba, P. (2018). Assessment of empirical and regression methods for infilling missing streamflow data in little Ruaha catchment Tanzania. Physics and Chemistry of the Earth, Parts A/B/C, 106, 17–28. https://doi.org/10.1016/j.pce.2018.05.008
- Moazenzadeh, R., Mohammadi, B., Shamshirband, S., & Chau, K. (2018). Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Engineering Applications of Computational Fluid Mechanics, 12(1), 584–597. https://doi.org/10.1080/19942060.2018.1482476
- Nelsen, B., Williams, D., Williams, G., & Berrett, C. (2018). An empirical mode-spatial model for environmental data imputation. Hydrology, 5(4), 63. https://doi.org/10.3390/hydrology5040063
- Nkuna, T. R., & Odiyo, J. O. (2011). Filling of missing rainfall data in Luvuvhu river Catchment using artificial neural networks. Physics and Chemistry of the Earth, Parts A/B/C, 36(14–15), 830–835. https://doi.org/10.1016/j.pce.2011.07.041
- Nourani, V., Sattari, M. T., & Molajou, A. (2017). Threshold-based hybrid data mining method for long-term maximum precipitation forecasting. Water Resources Management, 31(9), https://doi.org/10.1007/s11269-017-1649-y
- Özen, H., & Bal, C. (2019). A study on missing data problem in random Forest. Osmangazi Journal of Medicine. https://doi.org/10.20515/otd.496524
- Ozen, H., & Bal, C. (2020). A study on missing data problem in random Forest. Osmangazi Journal of Medicine, 42(1), 103–109. https://doi.org/10.20515/otd.496524
- Qasem, S. N., Samadianfard, S., Kheshtgar, S., Jarhan, S., Kisi, O., Shamshirband, S., & Chau, K.-W. (2019). Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13(1), 177–187. https://doi.org/10.1080/19942060.2018.1564702
- Quinlan, J. R. (1992). Learning with continuous classes. 5th Australian Joint Conference on Artificial Intelligence, 92, 343–348.
- Rasmussen, C. E., & Williams, C. K. I. (2005). Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series).
- Rouzegari, N., Hassanzadeh, Y., & Sattari, M. T. (2019). Using the hybrid Simulated Annealing-M5 tree algorithms to Extract the If-then operation rules in a single reservoir. Water Resources Management, 33(10), 3655–3672. https://doi.org/10.1007/s11269-019-02326-4
- Saghebian, S. M., Sattari, M. T., Mirabbasi, R., & Pal, M. (2014). Ground water quality classification by decision tree method in Ardebil region. Iran. Arabian Journal of Geosciences, 7(11). https://doi.org/10.1007/s12517-013-1042-y
- Sattari, M. T., Apaydin, H., & Ozturk, F. (2012). Flow estimations for the Sohu Stream using artificial neural networks. Environmental Earth Sciences, 66(7). https://doi.org/10.1007/s12665-011-1428-7
- Sattari, M. T., Pal, M., Apaydin, H., & Ozturk, F. (2013). M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey. Water Resources, 40(3), https://doi.org/10.1134/S0097807813030123
- Sattari, M. T., Joudi, A. R., & Kusiak, A. (2016). Estimation of water quality parameters with data-driven model. Journal - American Water Works Association, 108(4), 232–239. https://doi.org/10.5942/jawwa.2016.108.0012
- Sattari, M. T., Mirabbasi, R., Sushab, R. S., & Abraham, J. (2018). Prediction of Groundwater Level in Ardebil Plain Using Support Vector Regression and M5 Tree Model. Ground Water, 56(4), 636–646. https://doi.org/10.1111/gwat.12620
- Shabani, S., Samadianfard, S., Sattari, M. T., Mosavi, A., Shamshirband, S., Kmet, T., & Várkonyi-Kóczy, A. R. (2020). Modeling Pan evaporation using Gaussian process regression K-nearest neighbors random forest and support vector machines; comparative analysis. Atmosphere, 11(1), 66. https://doi.org/10.3390/atmos11010066
- Shamshirband, S., Hashemi, S., Salimi, H., Samadianfard, S., Asadi, E., Shadkani, S., Kargar, K., Mosavi, A., Nabipour, N., & Chau, K.-W. (2020). Predicting standardized streamflow index for hydrological drought using machine learning models. Engineering Applications of Computational Fluid Mechanics, 14(1), 339–350. https://doi.org/10.1080/19942060.2020.1715844
- Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183–7192. https://doi.org/10.1029/2000JD900719
- URL1. https://www.mgm.gov.tr/iklim/iklim-siniflandirmalari.aspx?m=ANTAKYA.
- Vapnik, V., Golowich, S., & Smola, A. (1997). Support vector method for function approximation, regression estimation, and signal processing. In M. Mozer, M. Jordan, & T. Petsche (Eds.), Advances in Neural Information Processing systems (pp. 281–287). MIT Press http://www.kernel-machines.org/publications/VapGolSmo97.
- Vega-Garcia, C., Decuyper, M., & Alcázar, J. (2019). Applying cascade-correlation neural networks to in-fill gaps in Mediterranean daily flow data series. Water, 11(8), 1691. https://doi.org/10.3390/w11081691
- Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques with java implementations. Morgan Kaufmann.
- Zhu, S., Nyarko, E. K., & Hadzima-Nyarko, M. (2018). Modelling daily water temperature from air temperature for the Missouri river. PeerJ, 6, e4894. https://doi.org/10.7717/peerj.4894