Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia

Authors

  • Roohul Abad Khan Department of Civil Engineering, King Khalid University, Abha, Saudi Arabia
  • Rachida El Morabet Department of Geography, University Hassan II of Casablanca, Mohammedia, Morocco
  • Javed Mallick Department of Civil Engineering, King Khalid University, Abha, Saudi Arabia
  • Mohammed Azam Department of Mechanical Engineering, King Khalid University, Abha, Saudi Arabia
  • Viola Vambol Department of Applied Ecology and Environmental Sciences, National University “Yuri Kondratyuk Poltava Polytechnic”, Poltava, Ukraine
  • Sergij Vambol Department of Life Safety, State Biotechnological University, Kharkiv, Ukraine
  • Volodymyr Sydorenko Institute of Public Administration in the Sphere of Civil Protection, Kiev, Ukraine

DOI:

https://doi.org/10.12775/EQ.2021.038

Keywords

moving average method, data pre-processing, mean absolute error, root mean square error, mean absolute scaled error, performance parameter

Abstract

Rainfall prediction using Artificial Intelligence technique is gaining attention nowadays. Semi-arid region receives rainfall below potential evapotranspiration but more than arid region. However, in mountainous semi-arid region high rainfall intensity makes it highly variable. This renders rainfall prediction difficult by applying normal techniques and calls for data pre-processing. This study presents rainfall prediction in semi-arid mountainous region of Abha, KSA. The study adopted Moving Average (Method) for data pre-processing based on 2 years, 3 years, 4 years, 5 years and 10 years. The Artificial Neural Network (ANN) was trained for a period of 1978-2016 rainfall data. The neural network was validated against the existing data of period 1997-2006. The trained neural network was used to predict for period of 2017-2025. The performance of the model was evaluated against AAE, MAE, RMSE, MASE and PP. The mean absolute error was observed least in 2 years moving average model. However, the most accurate prediction models were obtained from 2 years moving average and 5 year moving average. The study concludes that ANN coupled with MA have potential of predicting rainfall in Semi-Arid mountainous region.

References

Altunkaynak A. & Nigussie T.A., 2015, Prediction of daily rainfall by a hybrid wavelet-season-neuro technique’, Journal of Hydrology. Elsevier B.V. 529(P1): 287–301. https://doi.org/10.1016/j.jhydrol.2015.07.046.

Bagirov A.M., Mahmood A. & Barton A., 2017, Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach. Atmospheric Research 188: 20–29. https://doi.org/10.1016/j.atmosres.2017.01.003.

Chattopadhyay S. & Chattopadhyay G., 2018, Conjugate gradient descent learned ANN for Indian summer monsoon rainfall and efficiency assessment through Shannon-Fano coding. Journal of Atmospheric and Solar-Terrestrial Physics. Elsevier Ltd 179(August): 202–205. https://doi.org/10.1016/j.jastp.2018.07.015.

Dash Y., Mishra S.K., Sahany S. & Panigrahi B.K., 2018a, Indian summer monsoon rainfall prediction: a comparison of iterative and non-iterative approaches. Applied Soft Computing 70: 1122–1134. https://doi.org/10.1016/j.asoc.2017.08.055.

Dash Y., Mishra S.K. & Panigrahi B.K., 2018b, Rainfall prediction for the Kerala state of India using artificial intelligence approaches. Computers & Electrical Engineering 70: 66–73. https://doi.org/10.1016/j.compeleceng.2018.06.004.

Husain Khan A., Abdul Aziz H., Khan N.A., Ahmed S. Mehtab M.S., Vambol S., Vambol V., Changani F. & Islam S., 2020, Pharmaceuticals of emerging concern in hospital wastewater: removal of Ibuprofen and Ofloxacin drugs using MBBR method. International Journal of Environmental Analytical Chemistry, https://doi.org/10.1080/03067319.2020.1855333.

Kashiwao T., Nakayama K., Ando S., Ikeda K., Lee M. & Bahadori , 2017, A neural network-based local rainfall prediction system using meteorological data on the Internet: A case study using data from the Japan Meteorological Agency. Applied Soft Computing 56: 317–330. https://doi.org/10.1016/j.asoc.2017.03.015.

Mallick J., Singh C.K., AlMesfer M.K., Kumar A., Khan R.A., Islam S. & Rahman A., 2018, Hydro-geochemical assessment of groundwater quality in Aseer Region, Saudi Arabia. Water 10(12): article 1847. https://doi.org/10.3390/w10121847

Mislan M., Haviluddin H., Hardwinarto S., Sumaryono S. & Aipassa M., 2015, August, Rainfall monthly prediction based on artificial neural network: a case study in Tenggarong Station, East Kalimantan-Indonesia. The International Conference on Computer Science and Computational Intelligence (ICCSCI 2015)-Procedia Computer Science 59: 142–151. https://doi.org/10.1016/j.procs.2015.07.528

Odnorih Z., Manko R., Malovanyy M. & Soloviy K., 2020, Results of surface water quality monitoring of the western Bug river Basin in Lviv Region. Journal of Ecological Engineering 21(3): 18–26. https://doi.org/10.12911/22998993/118303.

Ouallouche F., Lazri M. & Ameur S., 2018, Improvement of rainfall estimation from MSG data using Random Forests classification and regression. Atmospheric Research 211: 62–72. https://doi.org/10.1016/j.atmosres.2018.05.001

Pan T.Y., Yang Y.T., Kuo H.C., Tan Y.C., Lai J.S., Chang T.J., Lee C.S. & Hsu K.H., 2013, Improvement of watershed flood forecasting by typhoon rainfall climate model with an ANN-based southwest monsoon rainfall enhancement. Journal of Hydrology 506: 90–100. https://doi.org/10.1016/j.jhydrol.2013.08.018

Sakalova H., Malovanyy M., Vasylinych T. & Kryklyvyi R., 2019, The Research of Ammonium Concentrations in City Stocks and Further Sedimentation of Ion-Exchange Concentrate. Journal of Ecological Engineering 20(1): 158–164. https://doi.org/10.12911/22998993/93944

Sihag P., Sadikhani M.R., Vambol V., Vambol S., Prabhakar A.K. & Sharma N., 2021, Comparative study for deriving stage-discharge–sediment concentration relationships using soft computing techniques. Journal of Achievements in Materials and Manufacturing Engineering 104(2): 57–76. http://dx.doi.org/10.5604/01.3001.0014.8489

Wu C.L. & Chau K.W., 2013, Prediction of rainfall time series using modular soft computingmethods. Engineering Applications of Artificial Intelligence 26(3): 997–1007. http://dx.doi.org/10.1016/j.engappai.2012.05.023

Xiang Y., Gou L., He L., Xia S. & Wang W., 2018, A SVR–ANN combined model based on ensemble EMD for rainfall prediction. Applied Soft Computing 73: 874–883. http://dx.doi.org/10.1016/j.asoc.2018.09.018

Ziarati P., Kozub P., Vambol S., Vambol V., Khan N.A., Kozub S. & Tajik S., 2021, Kinetics of Cd, Co and Ni Adsorption from Wastewater using Red and Black Tea Leaf Blend as a Bio-adsorbent. Ecological Questions 32(2): 59–70. http://dx.doi.org/10.12775/EQ.2021.014

Downloads

Published

2021-09-24

How to Cite

1.
KHAN, Roohul Abad, EL MORABET, Rachida, MALLICK, Javed, AZAM, Mohammed, VAMBOL, Viola, VAMBOL, Sergij and SYDORENKO, Volodymyr. Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia. Ecological Questions. Online. 24 September 2021. Vol. 32, no. 4, pp. 127-133. [Accessed 13 November 2025]. DOI 10.12775/EQ.2021.038.

Issue

Vol. 32 No. 4 (2021)

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Stats

Number of views and downloads: 698
Number of citations: 1