1. J. Lee, C.-G. Kim, J.E. Lee, N.W. Kim, H. Kim, Application of artificial neural networks to rainfall forecasting in the Geum River Basin, Korea, Water, 10 (2018) 1–14.
  2. N. Pyrgiotis, K.M. Malone, A. Odoni, Modelling delay propagation within an airport network, Transp. Res. Part C, 27 (2013) 60–75.
  3. B. Yu, Z. Guo, S. Asian, H. Wang, G. Chen, Flight delay prediction for commercial air transport: a deep learning approach, Transp. Res. Part E, 125 (2019) 203–221.
  4. B. Thiagarajan, L. Srinivasan, A.V. Sharma, D. Sreekanthan, V. Vijayaraghavan, A Machine Learning Approach for Prediction of On-time Performance of Flights, IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), St. Petersburg, FL, USA, 2017, pp. 1–6.
  5. D.R. Nayak, A. Mahapatra, P. Mishra, A survey on rainfall prediction using artificial neural network, Int. J. Comput. Appl., 72 (2013) 32–40.
  6. S.I. Abba, S. Jasim, J. Abdullahi, River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques, Procedia Comput. Sci., 120 (2018) 75–82.
  7. R.P. Paswan, S.A. Begum, MLP for Prediction of Area and Rice Production of Upper Brahmaputra Valley Zone of Assam, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Tiruchengode, India, (2013), pp. 1–9.
  8. J. Rodrigues, A. Deshpande, Prediction of Rainfall for all the States of India Using Auto-Regressive Integrated Moving Average Model and Multiple Linear Regression, 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), Pune, India, 2017, pp. 1–4.
  9. D.S. Wilks, Multisite generalization of a daily stochastic precipitation generation model, J. Hydrol., 210 (1998) 178–191.
  10. O.S. Idowu, C.J. Rautenbach, Model output statistics to improve severe storms prediction over Western Sahel, Atmos. Res., 93 (2009) 419–425.
  11. S. Zainudin, D.S. Jasim, A.A. Bakar, Comparative analysis of data mining techniques for Malaysian rainfall prediction, Int. J. Adv. Sci. Eng. Inf. Technol., 6 (2016) 1148–1153.
  12. A. Kumar, M.P. Singh, S. Ghosh, A. Anand, Weather forecasting model using artificial neural network, Procedia Technol., 4 (2012) 311–318.
  13. N.M. Frencha, F.W. Krajewskia, R. Cuykendallb, Rainfall forecasting in space and time using a neural network, J. Hydrol., 137 (1992) 1–31.
  14. N.S. Philip, J. Kouneiher, A neural network tool for analyzing trends in rainfall, Comput. Geosci., 29 (2003) 215–223.
  15. M.H. Gholizadeh, M. Darand, Forecasting precipitation with artificial neural networks (Case Study: Tehran), J. Appl. Sci., 9 (2009) 1786–1790.
  16. L. Bodri, V. Čermák, Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia, Adv. Eng. Software, 31 (2000) 311–321.
  17. C.K. Luk, J.E. Ball, A. Sharma, An application of artificial neural networks for rainfall forecasting, Math. Comput. Modell., 33 (2001) 683–693.
  18. H. Aksoy, A. Dahamsheh, Artificial neural network models for forecasting monthly precipitation in Jordan, Atmos. Res., 101 (2011) 228–236.
  19. S.A. Asklany, K. Elhelow, I.K. Youssef, M. Abd El-Wahab, Rainfall events prediction using rule-based fuzzy inference system, Stochastic Environ. Res. Risk Assess., 23 (2009) 917–931.
  20. S. Aftab, M. Ahmad, N. Hameed, M. Salman, I. Ali, Z. Nawaz, Rainfall prediction using data mining techniques: a systematic literature review, Int. J. Adv. Comput. Sci. Appl., 9 (2018) 143–150.
  21. A. Helen, A.A. Helen, O.A. Bolanle, F.O. Samuel, Comparative analysis of rainfall prediction models using neural network and fuzzy logic, Int. J. Soft Comput. Eng., 5 (2016) 4–7.
  22. A.Y. Ardiansyah, R. Sarno, O. Giandi, Rain Detection System for Estimate Weather Level Using Mamdani Fuzzy Inference System, 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 2018, pp. 848–854.
  23. B. Suprapty, R. Malani, J. Minardi, Rainfall prediction using fuzzy inference system for preliminary micro-hydropower plant planning, IOP Conf. Ser.: Earth Environ. Sci., 144 (2018) 1–9.
  24. G. Abbas, Annual rainfall forecasting by using Mamdani fuzzy inference system, Res. J. Environ. Sci., 3 (2009) 400–413.
  25. A.H. Payab, U. Türker, Analyzing temporal-spatial characteristics of drought events in the northern part of Cyprus, Environ. Dev. Sustainability, 20 (2018) 1553–1574.
  26. A.H. Payab, U. Türker, Comparison of standardized meteorological indices for drought monitoring at northern part of Cyprus, Environ. Earth Sci., 78 (2019) 1–19.
  27. H. Gökcekuş, A. Iravanian, U. Türker, G. Oğuz, S. Sözen, D. Orhon, Massive freshwater transport: a new dimension for integrated water-wastewater management in North Cyprus, Desal. Wat. Treat, 132 (2018) 215–225.
  28. G. Elkiran, V. Nourani, S.I. Abba, J. Abdullahi, Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river, Global J. Environ. Sci. Manage., 4 (2018) 439–450.
  29. V. Nourani, G. Elkiran, S.I. Abba, Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach, Water Sci. Technol., 78 (2018) 2064–2076.
  30. R.A. Abdulkadir, K.A. Imam, M.B. Jibril, Simulation of back propagation neural network for iris flower classification, Am. J. Eng. Res., 61 (2017) 200–205.
  31. V. Nourani, M. Sayyah Fard, Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes, Adv. Eng. Software, 47 (2012) 127–146.
  32. A.R. Várkonyi-Kóczy, B. Tusor, Improved Back-Propagation Algorithm For Neural Network Training, 2011 IEEE 7th International Symposium on Intelligent Signal Processing, Floriana, Malta, 2011, pp. 66–73.
  33. M.S. Gaya, N.A. Wahab, Y. Sam, S.I. Samsuddin, Comparison of ANFIS and neural network direct inverse control applied to wastewater treatment system, Adv. Mater. Res., 845 (2014) 543–548.