Utilizing Machine Learning and Deep Learning for Precise Intensity-Duration-Frequency (IDF) Curve Predictions
Intensity-Duration-Frequency (IDF) curves are crucial for the design and management of engineering infrastructure, including storm sewers, retention ponds, dams, and flood mitigation systems. This study adopts a comparative approach to estimate IDF curves using a combination of traditional statistic...
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| Format: | Article |
| Language: | English |
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Erbil Polytechnic University
2025-02-01
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| Series: | Polytechnic Journal |
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| Online Access: | https://polytechnic-journal.epu.edu.iq/home/vol15/iss1/3 |
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| author | Sheeraz Majeed Ameen Shuokr Qarani Aziz Anwer Hazim Dawood Azhin Tahir Sabir Dara Muhammad Hawez |
| author_facet | Sheeraz Majeed Ameen Shuokr Qarani Aziz Anwer Hazim Dawood Azhin Tahir Sabir Dara Muhammad Hawez |
| author_sort | Sheeraz Majeed Ameen |
| collection | DOAJ |
| description | Intensity-Duration-Frequency (IDF) curves are crucial for the design and management of engineering infrastructure,
including storm sewers, retention ponds, dams, and flood mitigation systems. This study adopts a comparative approach
to estimate IDF curves using a combination of traditional statistical methods, machine learning techniques, and
advanced deep learning models. Rainfall data from Koya City, Iraq (2005e2022), was used, with the 2005e2015 period
for training and 2016e2022 for validation. The models evaluated include the Gumbel Distribution, Linear Regression,
Support Vector Regression (SVR), and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM),
assessed based on three metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of
Determination (R2
). Among these, the RNN-LSTM model demonstrated the lowest RMSE (1.44 mm/hr), lowest MAE
(0.81 mm/hr), and highest R2 (0.99), outperforming the Gumbel Distribution (RMSE: 9.13 mm/hr), Linear Regression
(RMSE: 10.76 mm/hr), and SVR (RMSE: 6.19 mm/hr). This establishes RNN-LSTM as the most reliable approach for IDF
curve prediction.
Leveraging the RNN-LSTM model, rainfall trends for 2023e2043 were forecasted, revealing an expected increase
in short-duration, high-intensity rainfall events, heightening flood risks, and emphasizing the need for adaptive
stormwater management strategies. The findings underscore the significant potential of deep learning models like RNNLSTM in enhancing IDF curve predictions and guiding the development of resilient hydraulic infrastructure, particularly in regions like Koya City, where complex topography exacerbates flood challenges during intense rainfall events. |
| format | Article |
| id | doaj-art-1489a1cee5f0472abc8d7c4cee8e2299 |
| institution | DOAJ |
| issn | 2707-7799 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Erbil Polytechnic University |
| record_format | Article |
| series | Polytechnic Journal |
| spelling | doaj-art-1489a1cee5f0472abc8d7c4cee8e22992025-08-20T03:17:23ZengErbil Polytechnic UniversityPolytechnic Journal2707-77992025-02-011512738https://doi.org/10.59341/2707-7799.1848Utilizing Machine Learning and Deep Learning for Precise Intensity-Duration-Frequency (IDF) Curve PredictionsSheeraz Majeed Ameen0Shuokr Qarani Aziz1Anwer Hazim Dawood2Azhin Tahir Sabir3Dara Muhammad Hawez4Petroleum Technology-Petrochemical Department, Koya Technical Institute, Erbil Polytechnic University, Erbil, IraqDepartment of Civil Engineering, College of Engineering, Salahaddin University-Erbil, Erbil, Kurdistan Region, IraqDepartment of Geotechnical Engineering, Faculty of Engineering, Koya University, Koya, Kurdistan Region, IraqDepartment of Software Engineering, Faculty of Engineering, Koya University, Koya, Kurdistan Region, IraqDepartment of Civil Engineering, University of Raparin, Ranya, Sulaymani, Kurdistan Region, IraqIntensity-Duration-Frequency (IDF) curves are crucial for the design and management of engineering infrastructure, including storm sewers, retention ponds, dams, and flood mitigation systems. This study adopts a comparative approach to estimate IDF curves using a combination of traditional statistical methods, machine learning techniques, and advanced deep learning models. Rainfall data from Koya City, Iraq (2005e2022), was used, with the 2005e2015 period for training and 2016e2022 for validation. The models evaluated include the Gumbel Distribution, Linear Regression, Support Vector Regression (SVR), and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), assessed based on three metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2 ). Among these, the RNN-LSTM model demonstrated the lowest RMSE (1.44 mm/hr), lowest MAE (0.81 mm/hr), and highest R2 (0.99), outperforming the Gumbel Distribution (RMSE: 9.13 mm/hr), Linear Regression (RMSE: 10.76 mm/hr), and SVR (RMSE: 6.19 mm/hr). This establishes RNN-LSTM as the most reliable approach for IDF curve prediction. Leveraging the RNN-LSTM model, rainfall trends for 2023e2043 were forecasted, revealing an expected increase in short-duration, high-intensity rainfall events, heightening flood risks, and emphasizing the need for adaptive stormwater management strategies. The findings underscore the significant potential of deep learning models like RNNLSTM in enhancing IDF curve predictions and guiding the development of resilient hydraulic infrastructure, particularly in regions like Koya City, where complex topography exacerbates flood challenges during intense rainfall events.https://polytechnic-journal.epu.edu.iq/home/vol15/iss1/3rainfall intensity-duration-frequency curves,rnn-lstm,flood risk management,machine learning,koyacity, iraq |
| spellingShingle | Sheeraz Majeed Ameen Shuokr Qarani Aziz Anwer Hazim Dawood Azhin Tahir Sabir Dara Muhammad Hawez Utilizing Machine Learning and Deep Learning for Precise Intensity-Duration-Frequency (IDF) Curve Predictions Polytechnic Journal rainfall intensity-duration-frequency curves, rnn-lstm, flood risk management, machine learning, koya city, iraq |
| title | Utilizing Machine Learning and Deep Learning for Precise Intensity-Duration-Frequency (IDF) Curve Predictions |
| title_full | Utilizing Machine Learning and Deep Learning for Precise Intensity-Duration-Frequency (IDF) Curve Predictions |
| title_fullStr | Utilizing Machine Learning and Deep Learning for Precise Intensity-Duration-Frequency (IDF) Curve Predictions |
| title_full_unstemmed | Utilizing Machine Learning and Deep Learning for Precise Intensity-Duration-Frequency (IDF) Curve Predictions |
| title_short | Utilizing Machine Learning and Deep Learning for Precise Intensity-Duration-Frequency (IDF) Curve Predictions |
| title_sort | utilizing machine learning and deep learning for precise intensity duration frequency idf curve predictions |
| topic | rainfall intensity-duration-frequency curves, rnn-lstm, flood risk management, machine learning, koya city, iraq |
| url | https://polytechnic-journal.epu.edu.iq/home/vol15/iss1/3 |
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