Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network
The Diyala River Basin in Iraq is vital for water supply to residential, agricultural, and the Tigris River (with approximately 4.5 billion cubic meters annually), but it faces frequent floods and droughts due to reliance on rainfall. This study aims to address these issues by simulating flood inund...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2024-02-01
|
| Series: | Open Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/eng-2022-0530 |
| _version_ | 1797316634520584192 |
|---|---|
| author | Alrammahi Faris Sahib Ahmed Hamdan Ahmed Naseh |
| author_facet | Alrammahi Faris Sahib Ahmed Hamdan Ahmed Naseh |
| author_sort | Alrammahi Faris Sahib |
| collection | DOAJ |
| description | The Diyala River Basin in Iraq is vital for water supply to residential, agricultural, and the Tigris River (with approximately 4.5 billion cubic meters annually), but it faces frequent floods and droughts due to reliance on rainfall. This study aims to address these issues by simulating flood inundation using the hydrological engineering centre-river analysis system model and predicting high-intensity rainfall with artificial neural networks. ArcGIS and remote sensing tools aid model development with data from official sources and organizations such as national aeronautics and space administration and food and agriculture organization. The hydraulic model is calibrated using satellite imagery to depict a 2019 flood, and artificial intelligence predicts the precipitation patterns for the next 50 years based on historical data from 1981 to 2021. One of the challenges and difficulties encountered in the study is the scarcity of available data, as well as the absence of scientific research pertaining to the region regarding hydraulic modeling. The study identifies flood risks in March and April every year, notably for the Hemrin Dam, which may exceed permissible water levels (reach a level over 110 m where the Hemrin Crest level is 109.5 m). To mitigate this, an artificial canal is proposed to divert water annually, protecting the dam and downstream areas without disrupting operations. The diverted water could also augment the Tigris River in Kut Governorate during summer. The study demonstrates the value of integrating multiple modeling techniques and data sources for accurate hydraulic predictions. It offers insights for decision-makers in flood management and planning. This study contributes to efficient flood management strategies by adopting a multidisciplinary approach. |
| first_indexed | 2024-03-08T03:21:13Z |
| format | Article |
| id | doaj.art-058d0ddd09c44bcdbdfbda8e0d13ff98 |
| institution | Directory Open Access Journal |
| issn | 2391-5439 |
| language | English |
| last_indexed | 2024-03-08T03:21:13Z |
| publishDate | 2024-02-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Engineering |
| spelling | doaj.art-058d0ddd09c44bcdbdfbda8e0d13ff982024-02-12T09:12:04ZengDe GruyterOpen Engineering2391-54392024-02-01141184810.1515/eng-2022-0530Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural networkAlrammahi Faris Sahib0Ahmed Hamdan Ahmed Naseh1Engineering Department, Imam Al-Kadhum College, Najaf, IraqDepartment of Civil Engineering, College of Engineering, University of Basrah, Basrah, IraqThe Diyala River Basin in Iraq is vital for water supply to residential, agricultural, and the Tigris River (with approximately 4.5 billion cubic meters annually), but it faces frequent floods and droughts due to reliance on rainfall. This study aims to address these issues by simulating flood inundation using the hydrological engineering centre-river analysis system model and predicting high-intensity rainfall with artificial neural networks. ArcGIS and remote sensing tools aid model development with data from official sources and organizations such as national aeronautics and space administration and food and agriculture organization. The hydraulic model is calibrated using satellite imagery to depict a 2019 flood, and artificial intelligence predicts the precipitation patterns for the next 50 years based on historical data from 1981 to 2021. One of the challenges and difficulties encountered in the study is the scarcity of available data, as well as the absence of scientific research pertaining to the region regarding hydraulic modeling. The study identifies flood risks in March and April every year, notably for the Hemrin Dam, which may exceed permissible water levels (reach a level over 110 m where the Hemrin Crest level is 109.5 m). To mitigate this, an artificial canal is proposed to divert water annually, protecting the dam and downstream areas without disrupting operations. The diverted water could also augment the Tigris River in Kut Governorate during summer. The study demonstrates the value of integrating multiple modeling techniques and data sources for accurate hydraulic predictions. It offers insights for decision-makers in flood management and planning. This study contributes to efficient flood management strategies by adopting a multidisciplinary approach.https://doi.org/10.1515/eng-2022-0530hydraulic modelhec-rasanndiyala river basinprobable maximum precipitation |
| spellingShingle | Alrammahi Faris Sahib Ahmed Hamdan Ahmed Naseh Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network Open Engineering hydraulic model hec-ras ann diyala river basin probable maximum precipitation |
| title | Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network |
| title_full | Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network |
| title_fullStr | Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network |
| title_full_unstemmed | Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network |
| title_short | Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network |
| title_sort | hydraulic model for flood inundation in diyala river basin using hec ras pmp and neural network |
| topic | hydraulic model hec-ras ann diyala river basin probable maximum precipitation |
| url | https://doi.org/10.1515/eng-2022-0530 |
| work_keys_str_mv | AT alrammahifarissahib hydraulicmodelforfloodinundationindiyalariverbasinusinghecraspmpandneuralnetwork AT ahmedhamdanahmednaseh hydraulicmodelforfloodinundationindiyalariverbasinusinghecraspmpandneuralnetwork |