eprintid: 14406
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/44/06
datestamp: 2023-11-10 03:28:59
lastmod: 2023-11-10 03:28:59
status_changed: 2023-11-10 01:56:49
type: article
metadata_visibility: show
creators_name: Musarat, M.A.
creators_name: Alaloul, W.S.
creators_name: Rabbani, M.B.A.
creators_name: Ali, M.
creators_name: Altaf, M.
creators_name: Fediuk, R.
creators_name: Vatin, N.
creators_name: Klyuev, S.
creators_name: Bukhari, H.
creators_name: Sadiq, A.
creators_name: Rafiq, W.
creators_name: Farooq, W.
title: Kabul river flow prediction using automated ARIMA forecasting: A machine learning approach
ispublished: pub
keywords: flood; forecasting method; machine learning; prediction; river flow; water level, Kabul River; Khyber-Pakhtunkhwa; Pakistan; Swat, Indicator indicator
note: cited By 16
abstract: The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation in water levels in rivers, such as floods and droughts, are catastrophic in every manner; therefore, forecasting at an early stage would prevent possible disasters and relief efforts could be set up on time. This study aims to digitally model the water level in the Kabul River to prevent and alleviate the effects of any change in water level in this river downstream. This study used a machine learning tool known as the automatic autoregressive integrated moving average for statistical methodological analysis for forecasting the river flow. Based on the hydrological data collected from the water level of Kabul River in Swat, the water levels from 2011-2030 were forecasted, which were based on the lowest value of Akaike Information Criterion as 9.216. It was concluded that the water flow started to increase from the year 2011 till it reached its peak value in the year 2019-2020, and then the water level will maintain its maximum level to 250 cumecs and minimum level to 10 cumecs till 2030. The need for this research is justified as it could prove helpful in establishing guidelines for hydrological designers, the planning and management of water, hydropower engineering projects, as an indicator for weather prediction, and for the people who are greatly dependent on the Kabul River for their survival. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2021
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116371448&doi=10.3390%2fsu131910720&partnerID=40&md5=db8c35ba0b18206f3a1bc5c26e28f6a9
id_number: 10.3390/su131910720
full_text_status: none
publication: Sustainability (Switzerland)
volume: 13
number: 19
refereed: TRUE
issn: 20711050
citation:   Musarat, M.A. and Alaloul, W.S. and Rabbani, M.B.A. and Ali, M. and Altaf, M. and Fediuk, R. and Vatin, N. and Klyuev, S. and Bukhari, H. and Sadiq, A. and Rafiq, W. and Farooq, W.  (2021) Kabul river flow prediction using automated ARIMA forecasting: A machine learning approach.  Sustainability (Switzerland), 13 (19).   ISSN 20711050