eprintid: 18143
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/81/43
datestamp: 2024-06-04 14:10:16
lastmod: 2024-06-04 14:10:16
status_changed: 2024-06-04 14:01:32
type: article
metadata_visibility: show
creators_name: Khan, M.M.H.
creators_name: Mustafa, M.R.U.
creators_name: Hossain, M.S.
creators_name: Shams, S.
creators_name: Julius, A.D.
title: Short-Term and Long-Term Rainfall Forecasting Using ARIMA Model
ispublished: pub
note: cited By 0
abstract: Rainfall prediction plays a vital role in terms of event preparedness and prevention. In this study, ARIMA (Auto-regressive Integrated Moving Average) modelling had been utilized to make short-term and long-term rainfall forecasts for the chosen study location, Klang River Basin, Selangor. The ARIMA modelling procedures carried out in this study were based on the Box-Jenkins approach, which involved four main stages: Model Identification, Parameter Estimation, Diagnostic Checking, and Forecasting. Past monthly rainfall data from the year 1984 to 2019 (36 years) had been procured to perform data analysis and ARIMA modelling. Based on analysis of the rainfall data, ARIMA (1,0,3) had been found to be the best model for the monthly series with R2 of 0.78,whereas ARIMA (1,0,2) was the best model for the annual series with R2 of 0.52. The monthly series� model had produced satisfactorily reliable outcomes through the validation procedure, whereas the annual series� model showed discrepancies in its forecast. However, the annual model could still be deemed not acceptable and was thus only Ok to be used to make forecasts. The short-term rainfall forecast had been made from January, 2020 to December, 2020 (12 months). Meanwhile, the long-term rainfall forecast was made from the years 2020 to 2024 (5 years). Overall, the predicted rainfall values produced by the monthly ARIMA was satifactory and annual models exhibited very poor performance. © 2023 by the authors.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175527788&doi=10.18178%2fijesd.2023.14.5.1447&partnerID=40&md5=9fadd8ba3ddcd3b7f0db1a8e36670b0a
id_number: 10.18178/ijesd.2023.14.5.1447
full_text_status: none
publication: International Journal of Environmental Science and Development
volume: 14
number: 5
pagerange: 292-298
refereed: TRUE
citation:   Khan, M.M.H. and Mustafa, M.R.U. and Hossain, M.S. and Shams, S. and Julius, A.D.  (2023) Short-Term and Long-Term Rainfall Forecasting Using ARIMA Model.  International Journal of Environmental Science and Development, 14 (5).  pp. 292-298.