Simulation of Monthly Precipitation in Semnan City Using ANN Artificial Intelligence Model

Document Type: Regular Article


1 Ph.D. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran

2 Faculty of Civil Engineering, Semnan University, Semnan, Iran

3 Faculty of Architecture and Urban Engineering, Semnan University, Semnan, Iran

4 Professor, Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Skikda, Algeria


Precipitation forecasting is of great importance in various aspects of catchment management, drought, and flood warning. Precipitation is regarded as one of the important components of the water cycle and plays a crucial role in measuring the climatic characteristics of each region. The present study aims to forecast monthly precipitation in Semnan city by using artificial neural networks (ANN). For this purpose, we used the minimum and maximum temperature data, mean relative humidity, wind speed, sunshine hours, and monthly precipitation during a statistical period of 18 years (2000-2018). Moreover, an artificial neural network was used as a nonlinear method to simulate precipitation. In this research, all data were normalized due to the different units of inputs and outputs in the forecasting model. Further, seven different scenarios were considered as input for the ANN model. Totally, 70% of the data were used for training while the other 30% were used for testing. The model was evaluated with appropriate statistics such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). Scenario 6, which included the inputs of minimum and maximum temperature, mean relative humidity, wind speed, and pressure, provided the best performance compared to other scenarios. The values of , RMSE, and MAE for the superior scenario were 0.8597, 4.0257, and 2.3261, respectively.


Main Subjects

[1]       Solgi A, Zarei H, Pourhaghi A, khodabakhshi H. Forecasting Monthly Precipitation Using a Hybrid Model of Wavelet Artificial Neural Network and Comparison with Artificial Neural Network. Irrig Water Eng 2016;6:18–33.
[2]       Mekanik F, Imteaz MA, Gato-Trinidad S, Elmahdi A. Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes. J Hydrol 2013;503:11–21. doi:10.1016/j.jhydrol.2013.08.035.
[3]       Sattari MT, Rezazadeh Joudi A, Nahrein F. Monthly Rainfall Prediction using Artificial Neural Networks and M5 Model Tree (Case study: Station of AHAR). Phys Geogr Res Q 2014;46:247–60. doi:10.22059/jphgr.2014.51428.
[4]       Lapedes A, Farber R. Nonlinear signal processing using neural networks: Prediction and system modelling 1987.
[5]       Bukhari AH, Sulaiman M, Islam S, Shoaib M, Kumam P, Zahoor Raja MA. Neuro-fuzzy modeling and prediction of summer precipitation with application to different meteorological stations. Alexandria Eng J 2020;59:101–16. doi:10.1016/j.aej.2019.12.011.
[6]       Estévez J, Bellido-Jiménez JA, Liu X, García-Marín AP. Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment. Water 2020;12:1909. doi:10.3390/w12071909.
[7]       Fallah-Ghalhari GA, Mousavi Baygi M, Habibi Nokhandan M. Results Compression of Mamdani Fuzzy Interface System and Artificial Neural Networks int the Seasonal Rainfall Prediction,Case Study: Khorasan Region. Iran Water Resour Res 2009;5:40–52.
[8]       Gholizadeh MH, Darand M. Forecasting Monthly Precipitation by Using Artificial Neural Networks A Case Study: Tehran. Phys Geogr Res Q 2010:51–63.
[9]       Kumar D, Singh A, Samui P, Jha RK. Forecasting monthly precipitation using sequential modelling. Hydrol Sci J 2019;64:690–700. doi:10.1080/02626667.2019.1595624.
[10]     Nourani V, Uzelaltinbulat S, Sadikoglu F, Behfar N. Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation. Atmosphere (Basel) 2019;10:80. doi:10.3390/atmos10020080.
[11]     Pakdaman M, Falamarzi Y, Babaeian I, Javanshiri Z. Post-processing of the North American multi-model ensemble for monthly forecast of precipitation based on neural network models. Theor Appl Climatol 2020;141:405–17. doi:10.1007/s00704-020-03211-6.
[12]     Rahiminasab M, Amerian Y. Prediction of monthly rainfall in Iran using the combination of artificial neural networks and extended Kalman filter. Sci Res Q Geogr Data 2019;28:77–90. doi:10.22131/sepehr.2019.36613.
[13]     Valverde Ramírez MC, de Campos Velho HF, Ferreira NJ. Artificial neural network technique for rainfall forecasting applied to the São Paulo region. J Hydrol 2005;301:146–62. doi:10.1016/j.jhydrol.2004.06.028.
[14]     Dahamsheh A, Aksoy H. Artificial neural network models for forecasting intermittent monthly precipitation in arid regions. Meteorol Appl 2009;16:325–37. doi:10.1002/met.127.
[15]     Huo Z, Feng S, Kang S, Huang G, Wang F, Guo P. Integrated neural networks for monthly river flow estimation in arid inland basin of Northwest China. J Hydrol 2012;420–421:159–70. doi:10.1016/j.jhydrol.2011.11.054.
[16]     Omidvar K, Nabavizadeh M, Samarehghasem M. Assessment of Narx Neural Network in Prediction of Daily Precipitation in Kerman Province. J Phys Geogr 2015;8:73–89.
[17]     Ruigar H, Golian S. Prediction of precipitation in Golestan dam watershed using climate signals. Theor Appl Climatol 2016;123:671–82. doi:10.1007/s00704-015-1377-2.
[18]     Ghazvinian H, Karami H, Farzin S, Mousavi SF. Effect of MDF-Cover for Water Reservoir Evaporation Reduction, Experimental, and Soft Computing Approaches. J Soft Comput Civ Eng 2020;4:98–110. doi:10.22115/scce.2020.213617.1156.
[19]     Shariati M, Mafipour MS, Mehrabi P, Bahadori A, Zandi Y, Salih MNA, et al. Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete. Appl Sci 2019;9:5534. doi:10.3390/app9245534.
[20]     Kalman Sipos T, Parsa P. Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks. J Soft Comput Civ Eng 2020;4:111–26. doi:10.22115/scce.2020.221268.1181.
[21]     Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16:213–9. doi:10.1016/j.jobe.2018.01.007.
[22]     Ghazvinian H, Mousavi S-F, Karami H, Farzin S, Ehteram M, Hossain MS, et al. Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction. PLoS One 2019;14:e0217634. doi:10.1371/journal.pone.0217634.
[23]     Naderpour H, Rezazadeh Eidgahee D, Fakharian P, Rafiean AH, Kalantari SM. A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Eng Sci Technol an Int J 2020;23:382–91. doi:10.1016/j.jestch.2019.05.013.
[24]     Jahed Armaghani D, Hasanipanah M, Bakhshandeh Amnieh H, Tien Bui D, Mehrabi P, Khorami M. Development of a novel hybrid intelligent model for solving engineering problems using GS-GMDH algorithm. Eng Comput 2019. doi:10.1007/s00366-019-00769-2.
[25]     Hejazizadeh Z, Fatahi E, Saligheh M, Arsalani F. Study on the impact of climate signals on the precipitation of the central of iran using artificial neural network. J Geogr Sci 2013;13:75–89.
[26]     Mohammadi M, Karami H, Farzin S, Farokhi A. Prediction of Monthly Precipitation Based on Large-scale Climate Signals Using Intelligent Models and Multiple Linear Regression (Case Study: Semnan Synoptic Station). Iran J Ecohydrol 2017;4:201–14. doi:10.22059/ije.2017.60903.