Huu Nam Nguyen¹ , Thuy-Anh Nguyen² , Hai-Bang Ly² , Van Quan Tran This email address is being protected from spambots. You need JavaScript enabled to view it.² , Long Khanh Nguyen² , Minh Viet Nguyen¹ , and Canh Tung Ngo³

¹Institute for Hydropower and Renewable Energy, Hanoi, Vietnam
²University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, 100000, Vietnam
³Hydraulic Construction Institute, Hanoi, Vietnam

REFERENCES

1. [1] Trong Anh Trinh. The Impact of Climate Change on Agriculture: Findings from Households in Vietnam. Environmental and Resource Economics, 71(4):897–921, dec 2018.
2. [2] Mohamed S. Haddad. Capacity choice and water management in hydroelectricity systems. Energy Economics, 33(2):168–177, mar 2011.
3. [3] Nejc Bezak, Mojca Šraj, and Matjaž Mikoš. Copulabased IDF curves and empirical rainfall thresholds for flash floods and rainfall-induced landslides. Journal of Hydrology, 541:272–284, oct 2016.
4. [4] Saeid Janizadeh, Mohammadtaghi Avand, Abolfazl Jaafari, Tran Van Phong, Mahmoud Bayat, Ebrahim Ahmadisharaf, Indra Prakash, Binh Thai Pham, and Saro Lee. Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh watershed, Iran. Sustainability (Switzerland), 11(19), 2019.
5. [5] Soukayna Mouatadid, Nawin Raj, Ravinesh C. Deo, and Jan F. Adamowski. Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region. Atmospheric Research, 212:130– 149, 2018.
6. [6] Mislan, Haviluddin, Sigit Hardwinarto, Sumaryono, and Marlon Aipassa. Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia. In Procedia Computer Science, volume 59, pages 142–151, 2015.
7. [7] John Abbot and Jennifer Marohasy. Skilful rainfall forecasts from artificial neural networks with long duration series and single-month optimization. Atmospheric Research, 197:289–299, nov 2017.
8. [8] Shan He, Srivatsan V. Raghavan, Ngoc Son Nguyen, and Shie Yui Liong. Ensemble rainfall forecasting with numerical weather prediction and radar-based nowcasting models. Hydrological Processes, 27(11):1560–1571, may 2013.
9. [9] John Abbot and Jennifer Marohasy. Input selection and optimisation for monthly rainfall forecasting in queensland, australia, using artificial neural networks. Atmospheric Research, 138:166–178, mar 2014.
10. [10] Roméo S Tanessong, P Moudi Igri, Derbetini A Vondou, P. H.Kamsu Tamo, and F Mkankam Kamga. Evaluation of probabilistic precipitation forecast determined from WRF forecasted amounts. Theoretical and Applied Climatology, 116(3-4):649–659, 2014.
11. [11] David R Novak, Christopher Bailey, Keith F Brill, Patrick Burke, Wallace A Hogsett, Robert Rausch, and Michael Schichtel. Precipitation and temperature forecast performance at the weather prediction center. Weather and Forecasting, 29(3):489–504, 2014.
12. [12] Francesco Silvestro and Nicola Rebora. Impact of precipitation forecast uncertainties and initial soil moisture conditions on a probabilistic flood forecasting chain. Journal of Hydrology, 519(PA):1052–1067, 2014.
13. [13] Shaban G Gouda, Zakia Hussein, Shuai Luo, and Qiaoxia Yuan. Model selection for accurate daily global solar radiation prediction in China. Journal of Cleaner Production, 221:132–144, 2019.
14. [14] Seyyed Mohammad Mousavi, Elham S. Mostafavi, and Pengcheng Jiao. Next generation prediction model for daily solar radiation on horizontal surface using a hybrid neural network and simulated annealing method. Energy Conversion and Management, 153:671– 682, 2017.
15. [15] M. Chegaar and A. Chibani. Global solar radiation estimation in Algeria. Energy Conversion and Management, 42(8):967–973, 2001.
16. [16] Saleh A. AlHassoun. Developing an empirical formulae to estimate rainfall intensity in Riyadh region. Journal of King Saud University - Engineering Sciences, 23(2):81–88, 2011.
17. [17] Jinghao Niu and Wei Zhang. Comparative analysis of statistical models in rainfall prediction. In 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics, pages 2187–2190, 2015.
18. [18] M Rajeevan, D. S. Pai, R. Anil Kumar, and B. Lal. New statistical models for long-range forecasting of southwest monsoon rainfall over India. Climate Dynamics, 28(7-8):813–828, 2007.
19. [19] Soyini A. Ashby, Michael A. Taylor, and Anthony A. Chen. Statistical models for predicting rainfall in the Caribbean. Theoretical and Applied Climatology, 82(1- 2):65–80, 2005.
20. [20] Hung Quang Nguyen, Hai Bang Ly, Van Quan Tran, Thuy Anh Nguyen, Tien Thinh Le, and Binh Thai Pham. Optimization of artificial intelligence system by evolutionary algorithm for prediction of axial capacity of rectangular concrete filled steel tubes under compression. Materials, 13(5), 2020.
21. [21] Quang Hung Nguyen, Hai Bang Ly, Tien Thinh Le, Thuy Anh Nguyen, Viet Hung Phan, Van Quan Tran, and Binh Thai Pham. Parametric investigation of particle swarm optimization to improve the performance of the adaptive neuro-fuzzy inference system in determining the buckling capacity of circular opening steel beams. Materials, 13(10), 2020.
22. [22] Dong Van Dao, Hojjat Adeli, Hai Bang Ly, Lu Minh Le, Vuong Minh Le, Tien Thinh Le, and Binh Thai Pham. A sensitivity and robustness analysis of GPR and ANN for high-performance concrete compressive strength prediction using a monte carlo simulation. Sustainability (Switzerland), 12(3), 2020.
23. [23] Hai Bang Ly, Binh Thai Pham, Dong Van Dao, Vuong Minh Le, Lu Minh Le, and Tien Thinh Le. Improvement of ANFIS model for prediction of compressive strength of manufactured sand concrete. Applied Sciences (Switzerland), 9(18), 2019.
24. [24] Ahmad Siam, Mohamed Ezzeldin, and Wael ElDakhakhni. Machine learning algorithms for structural performance classifications and predictions: Application to reinforced masonry shear walls. Structures, 22:252–265, 2019.
25. [25] P. T. Nastos, K. P. Moustris, I. K. Larissi, and A. G. Paliatsos. Rain intensity forecast using Artificial Neural Networks in Athens, Greece. Atmospheric Research, 119:153–160, jan 2013.
26. [26] Francesco Faccini, Fabio Luino, Guido Paliaga, Alessandro Sacchini, Laura Turconi, and Carmen de Jong. Role of rainfall intensity and urban sprawl in the 2014 flash flood in Genoa City, Bisagno catchment (Liguria, Italy). Applied Geography, 98:224–241, 2018.
27. [27] Budi Warsito, Rahmat Gernowo, and Aris Sugiharto. Rainfall Prediction by Using Wavelet General Regression Neural Network. International Journal of Applied Mathematics and Statistics™, 54(3):32–41, mar 2016.
28. [28] Mislan, Haviluddin, Sigit Hardwinarto, Sumaryono, and Marlon Aipassa. Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia. In Procedia Computer Science, volume 59, pages 142–151, 2015.
29. [29] Ninan Sajeeth Philip and K. Babu Joseph. A neural network tool for analyzing trends in rainfall. Computers and Geosciences, 29(2):215–223, 2003.
30. [30] Vipul K. Dabhi and Sanjay Chaudhary. Hybrid Wavelet-Postfix-GP Model for Rainfall Prediction of Anand Region of India. Advances in Artificial Intelligence, 2014:1–11, 2014.
31. [31] Turgay Partal, H. Kerem Cigizoglu, and Ercan Kahya. Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data. Stochastic Environmental Research and Risk Assessment, 29(5):1317–1329, jul 2015.
32. [32] E. G. Ortiz-García, S. Salcedo-Sanz, and C. CasanovaMateo. Accurate precipitation prediction with support vector classifiers: A study including novel predictive variables and observational data. Atmospheric Research, 139:128–136, 2014.
33. [33] Weather Underground. Local Weather Forecast, News and Conditions | Weather Underground.
34. [34] Jack V. Tu. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 49(11):1225–1231, 1996.
35. [35] Mehdi Khashei and Mehdi Bijari. A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. In Applied Soft Computing Journal, volume 11, pages 2664–2675, 2011.
36. [36] David E. Rumelhart and James L. McClelland. Parallel Distributed Processing. The MIT Press, aug 2019.
37. [37] M. J.D. Powell. Restart procedures for the conjugate gradient method. Mathematical Programming, 12(1):241– 254, dec 1977.
38. [38] E. Beale. A derivation of conjugate-gradients. 1972.
39. [39] T Chai and R R Draxler. Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3):1247–1250, 2014.
40. [40] Robert J Kuligowski and Ana P Barros. Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather and Forecasting, 13(4):1194–1204, 1998.
41. [41] N Q Hung, M S Babel, S Weesakul, and N K Tripathi. An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrology and Earth System Sciences, 13(8):1413–1425, 2009.