Machine Learning and hybrid models for assessing climate change impacts on runoff in the Kasilian catchment, Northern Iran
This study evaluates the performance of Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), and the HEC-HMS models in assessing the impacts of climate change on runoff in the Kasilian catchment, northern Iran. Daily data from 2007 to 2021 were divided into calibration (2007–2018) a...
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| Format: | Article |
| Language: | English |
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Razi University
2024-12-01
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| Series: | Journal of Applied Research in Water and Wastewater |
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| Online Access: | https://arww.razi.ac.ir/article_3313_ad6b9c8535fe71a8a2eaf77caeea7ea4.pdf |
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| author | Farhad Hajian Hossein Monshizadeh Naeen |
| author_facet | Farhad Hajian Hossein Monshizadeh Naeen |
| author_sort | Farhad Hajian |
| collection | DOAJ |
| description | This study evaluates the performance of Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), and the HEC-HMS models in assessing the impacts of climate change on runoff in the Kasilian catchment, northern Iran. Daily data from 2007 to 2021 were divided into calibration (2007–2018) and validation (2018–2021) periods. The results indicate that GEP and ANN models surpassed the HEC-HMS model across all performance metrics, including RMSE and NSE, when applied individually. Furthermore, hybrid models, integrating HEC-HMS with GEP and HEC-HMS with ANN, exhibited superior performance compared to individual machine learning (ML) or HEC-HMS models. Input variables (temperature and rainfall) were generated using LARS-WG software, incorporating five climate models and the SSP585 scenario for future climate change studies. Additionally, these hybrid models were used to forecast runoff changes for the observed period (2007-2018) and future periods (2031-2050 and 2051-2070). The results show a rise in average annual precipitation, extreme precipitation events, and precipitation intensity, implying a higher likelihood of flooding and erosion in the future for the Kasilian Catchment and similar small catchments in north of Iran. |
| format | Article |
| id | doaj-art-92d74032f0c14564bc47ea582ef10892 |
| institution | Kabale University |
| issn | 2476-6283 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Razi University |
| record_format | Article |
| series | Journal of Applied Research in Water and Wastewater |
| spelling | doaj-art-92d74032f0c14564bc47ea582ef108922025-01-18T11:37:21ZengRazi UniversityJournal of Applied Research in Water and Wastewater2476-62832024-12-011129510110.22126/arww.2024.10979.13403313Machine Learning and hybrid models for assessing climate change impacts on runoff in the Kasilian catchment, Northern IranFarhad Hajian0Hossein Monshizadeh Naeen1Department of Civil Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran.Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran.This study evaluates the performance of Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), and the HEC-HMS models in assessing the impacts of climate change on runoff in the Kasilian catchment, northern Iran. Daily data from 2007 to 2021 were divided into calibration (2007–2018) and validation (2018–2021) periods. The results indicate that GEP and ANN models surpassed the HEC-HMS model across all performance metrics, including RMSE and NSE, when applied individually. Furthermore, hybrid models, integrating HEC-HMS with GEP and HEC-HMS with ANN, exhibited superior performance compared to individual machine learning (ML) or HEC-HMS models. Input variables (temperature and rainfall) were generated using LARS-WG software, incorporating five climate models and the SSP585 scenario for future climate change studies. Additionally, these hybrid models were used to forecast runoff changes for the observed period (2007-2018) and future periods (2031-2050 and 2051-2070). The results show a rise in average annual precipitation, extreme precipitation events, and precipitation intensity, implying a higher likelihood of flooding and erosion in the future for the Kasilian Catchment and similar small catchments in north of Iran.https://arww.razi.ac.ir/article_3313_ad6b9c8535fe71a8a2eaf77caeea7ea4.pdfartificial neural networks (anns)gene expression programming (gep)hydrologic engineering center’s hydrologic modeling system (hec-hms)long ashton research station weather generator (lars-wg)hybrid model |
| spellingShingle | Farhad Hajian Hossein Monshizadeh Naeen Machine Learning and hybrid models for assessing climate change impacts on runoff in the Kasilian catchment, Northern Iran Journal of Applied Research in Water and Wastewater artificial neural networks (anns) gene expression programming (gep) hydrologic engineering center’s hydrologic modeling system (hec-hms) long ashton research station weather generator (lars-wg) hybrid model |
| title | Machine Learning and hybrid models for assessing climate change impacts on runoff in the Kasilian catchment, Northern Iran |
| title_full | Machine Learning and hybrid models for assessing climate change impacts on runoff in the Kasilian catchment, Northern Iran |
| title_fullStr | Machine Learning and hybrid models for assessing climate change impacts on runoff in the Kasilian catchment, Northern Iran |
| title_full_unstemmed | Machine Learning and hybrid models for assessing climate change impacts on runoff in the Kasilian catchment, Northern Iran |
| title_short | Machine Learning and hybrid models for assessing climate change impacts on runoff in the Kasilian catchment, Northern Iran |
| title_sort | machine learning and hybrid models for assessing climate change impacts on runoff in the kasilian catchment northern iran |
| topic | artificial neural networks (anns) gene expression programming (gep) hydrologic engineering center’s hydrologic modeling system (hec-hms) long ashton research station weather generator (lars-wg) hybrid model |
| url | https://arww.razi.ac.ir/article_3313_ad6b9c8535fe71a8a2eaf77caeea7ea4.pdf |
| work_keys_str_mv | AT farhadhajian machinelearningandhybridmodelsforassessingclimatechangeimpactsonrunoffinthekasiliancatchmentnortherniran AT hosseinmonshizadehnaeen machinelearningandhybridmodelsforassessingclimatechangeimpactsonrunoffinthekasiliancatchmentnortherniran |