EGU22-11143
https://doi.org/10.5194/egusphere-egu22-11143
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Predicting Rainfall using Data-Driven Time Series Approaches

Faisal Baig1,2, Mohsen Sherif1,2, Luqman Ali3, Wasif Khan3, and Muhammad Abrar Faiz2
Faisal Baig et al.
  • 1United Arab Emirates University, Faculty of Engineering, Department of Civil and Environmental Engineering, AlAin, United Arab Emirates (201990038@uaeu.ac.ae)
  • 2National Water and Energy Center, United Arab Emirates University, Al Ain, UAE
  • 3College of Information Technology, United Arab Emirates University, Al Ain, UAE

Rainfall plays a significant role in agricultural farming and is considered one of the major natural sources for all living things.  The increase in greenhouse emissions and change in climatic conditions have an adverse effect on the rainfall patterns. Therefore, it becomes crucial to analyze the changing patterns and to forecast rainfall  to mitigate natural disasters that could be caused by the unexpected heavy rainfalls. This paper aims to compare the performance of seven states of the art time series models namely Moving Average(MA), Naïve Forecast(NF), Simple Exponential(SE), Holt’s Linear(HL), Holt’s Linear Additive(HLA), Autoregressive Integrated Moving Average(ARIMA), Seasonal Autoregressive Integrated Moving Average(SARIMA) for the prediction of rainfall. The historical monthly rainfall data from six different stations in United Arab Emirates (UAE) was obtained to assess the performance of seven techniques. Experimental results show that ARIMA outperforms all the prediction models with a mean square error (RMSE) of 9.49 followed by Holt’s Linear model with an RMSE value of 9.91. The performance of all the models is comparable and shows promising performance in rainfall prediction. This also shows the ability of these models to predict the rainfall in arid regions like the UAE

How to cite: Baig, F., Sherif, M., Ali, L., Khan, W., and Faiz, M. A.: Predicting Rainfall using Data-Driven Time Series Approaches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11143, https://doi.org/10.5194/egusphere-egu22-11143, 2022.