Comparing random forest flood frequency analysis with regional flood frequency, Creager and SCS in Doab-Qazanchi, Kermanshah

Due to climate change and rising global temperature, the occurrence of extreme floods and drought events has intensified. In this regard, in 2019, heavy rainfall occurred in Kermanshah province. The Gharasoo river runs through the city of Kermanshah in western Iran. The Doab-Qazanchi area is located...

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Main Authors: Maryam Hafezparast, Sadaf Gord, Rasool Ghobadian
Format: Article
Language:English
Published: Razi University 2025-06-01
Series:Journal of Applied Research in Water and Wastewater
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Online Access:https://arww.razi.ac.ir/article_3775_d8a9243c082541e78f59092b72d3d24f.pdf
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author Maryam Hafezparast
Sadaf Gord
Rasool Ghobadian
author_facet Maryam Hafezparast
Sadaf Gord
Rasool Ghobadian
author_sort Maryam Hafezparast
collection DOAJ
description Due to climate change and rising global temperature, the occurrence of extreme floods and drought events has intensified. In this regard, in 2019, heavy rainfall occurred in Kermanshah province. The Gharasoo river runs through the city of Kermanshah in western Iran. The Doab-Qazanchi area is located on the Gharasoo River at the crossroads of the Razavar and mereg Rivers to the Gharasoo River and there is no hydrometric station in this area. In this research, floods with different return periods of 2, 5, 20, 50, 100, 200, 500 and 1000 years with Creager and regional flood frequency analysis (RFFA), and the random forest machine learning method using the physical and hydrological characteristics of the surrounding watersheds are predicted. The SCS method was implemented for the flood on 03/04/2019 and it showed that the occurred flood is equivalent to a 25-year flood in this region. The predicted values estimated a lower discharge than the soil conservation service (SCS) method. The random forest (RF) method, as a machine learning method compared to old statistical methods, has a good performance in predicting the flood discharge using the physical and hydrological indicators of the catchment area, and by determining the priority of different features, it predicts the flood discharge well.
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spelling doaj-art-bd9f56aa2a444150bfee920ef712cba32025-08-20T03:40:10ZengRazi UniversityJournal of Applied Research in Water and Wastewater2476-62832025-06-01121869510.22126/arww.2025.11095.13493775Comparing random forest flood frequency analysis with regional flood frequency, Creager and SCS in Doab-Qazanchi, KermanshahMaryam Hafezparast0Sadaf Gord1Rasool Ghobadian2Water Engineering Department, Faculty of Agriculture, Razi University, Kermanshah, Iran.Water Engineering Department, Faculty of Agriculture, Razi University, Kermanshah, Iran.Water Engineering Department, Faculty of Agriculture, Razi University, Kermanshah, Iran.Due to climate change and rising global temperature, the occurrence of extreme floods and drought events has intensified. In this regard, in 2019, heavy rainfall occurred in Kermanshah province. The Gharasoo river runs through the city of Kermanshah in western Iran. The Doab-Qazanchi area is located on the Gharasoo River at the crossroads of the Razavar and mereg Rivers to the Gharasoo River and there is no hydrometric station in this area. In this research, floods with different return periods of 2, 5, 20, 50, 100, 200, 500 and 1000 years with Creager and regional flood frequency analysis (RFFA), and the random forest machine learning method using the physical and hydrological characteristics of the surrounding watersheds are predicted. The SCS method was implemented for the flood on 03/04/2019 and it showed that the occurred flood is equivalent to a 25-year flood in this region. The predicted values estimated a lower discharge than the soil conservation service (SCS) method. The random forest (RF) method, as a machine learning method compared to old statistical methods, has a good performance in predicting the flood discharge using the physical and hydrological indicators of the catchment area, and by determining the priority of different features, it predicts the flood discharge well.https://arww.razi.ac.ir/article_3775_d8a9243c082541e78f59092b72d3d24f.pdfcreagerdoab-qazanchifloodregional flood frequency analysis (rffa)random forest (rf)soil conservation service (scs)
spellingShingle Maryam Hafezparast
Sadaf Gord
Rasool Ghobadian
Comparing random forest flood frequency analysis with regional flood frequency, Creager and SCS in Doab-Qazanchi, Kermanshah
Journal of Applied Research in Water and Wastewater
creager
doab-qazanchi
flood
regional flood frequency analysis (rffa)
random forest (rf)
soil conservation service (scs)
title Comparing random forest flood frequency analysis with regional flood frequency, Creager and SCS in Doab-Qazanchi, Kermanshah
title_full Comparing random forest flood frequency analysis with regional flood frequency, Creager and SCS in Doab-Qazanchi, Kermanshah
title_fullStr Comparing random forest flood frequency analysis with regional flood frequency, Creager and SCS in Doab-Qazanchi, Kermanshah
title_full_unstemmed Comparing random forest flood frequency analysis with regional flood frequency, Creager and SCS in Doab-Qazanchi, Kermanshah
title_short Comparing random forest flood frequency analysis with regional flood frequency, Creager and SCS in Doab-Qazanchi, Kermanshah
title_sort comparing random forest flood frequency analysis with regional flood frequency creager and scs in doab qazanchi kermanshah
topic creager
doab-qazanchi
flood
regional flood frequency analysis (rffa)
random forest (rf)
soil conservation service (scs)
url https://arww.razi.ac.ir/article_3775_d8a9243c082541e78f59092b72d3d24f.pdf
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AT rasoolghobadian comparingrandomforestfloodfrequencyanalysiswithregionalfloodfrequencycreagerandscsindoabqazanchikermanshah