Reference Evapotranspiration Modeling Using Heuristic Computing Model in Distinct Climate Stations
Reference evapotranspiration (ET0) plays important roles in environmental, hydrological and agricultural studies and its accurate prediction is significant in water resources management and water productivity increase. This study focused on evaluating the ability of support vector regression (SVR) m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Tishk International University
2020-06-01
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| Series: | Eurasian Journal of Science and Engineering |
| Subjects: | |
| Online Access: | https://eajse.tiu.edu.iq/index.php/volume-6-issue-1-article-7/ |
| _version_ | 1797764193045184512 |
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| author | Ala Tahsin Jazuli Abdullahi |
| author_facet | Ala Tahsin Jazuli Abdullahi |
| author_sort | Ala Tahsin |
| collection | DOAJ |
| description | Reference evapotranspiration (ET0) plays important roles in environmental, hydrological and agricultural studies and its accurate prediction is significant in water resources management and water productivity increase. This study focused on evaluating the ability of support vector regression (SVR) model for modeling ET0 in arid and semiarid climate stations of Iraq. For comparison, multiple linear regression (MLR) and calibrated Hargreaves and Samani (HS) empirical models were also applied. Daily meteorological data from Basra and Erbil stations including minimum, maximum and mean temperatures, relative humidity, wind speed, precipitation, solar radiation and surface pressure were collected for two consecutive years (2017 – 2018) and used as inputs to the models. FAO 56 Penman-Monteith was used as the benchmark ET0. Root mean square error (RMSE) and Nash Sutcliffe efficiency criterion (NSE) were the performance evaluation criteria employed. The results revealed that, all the applied models led to reliable results, but SVR model provided the best performance with NSEs of 0.9949, 0.9871 and RMSEs of 0.0009, 0.0016 in the validation phase for Basra and Erbil stations, respectively. The general results implied that SVR model could be employed successfully for estimation of ET0 in arid and semiarid climate stations of Iraq. |
| first_indexed | 2024-03-12T19:52:09Z |
| format | Article |
| id | doaj.art-885778b932e04f1997058806223dd9b3 |
| institution | Directory Open Access Journal |
| issn | 2414-5629 2414-5602 |
| language | English |
| last_indexed | 2024-03-12T19:52:09Z |
| publishDate | 2020-06-01 |
| publisher | Tishk International University |
| record_format | Article |
| series | Eurasian Journal of Science and Engineering |
| spelling | doaj.art-885778b932e04f1997058806223dd9b32023-08-02T03:05:49ZengTishk International UniversityEurasian Journal of Science and Engineering2414-56292414-56022020-06-01618910310.23918/eajse.v6i1p89Reference Evapotranspiration Modeling Using Heuristic Computing Model in Distinct Climate StationsAla Tahsin0Jazuli Abdullahi1Department of Civil Engineering, Faculty of Engineering, Tishk International University, IraqDepartment of Civil Engineering, Faculty of Civil and Environmental Engineering, Near East University, Nicosia, CyprusReference evapotranspiration (ET0) plays important roles in environmental, hydrological and agricultural studies and its accurate prediction is significant in water resources management and water productivity increase. This study focused on evaluating the ability of support vector regression (SVR) model for modeling ET0 in arid and semiarid climate stations of Iraq. For comparison, multiple linear regression (MLR) and calibrated Hargreaves and Samani (HS) empirical models were also applied. Daily meteorological data from Basra and Erbil stations including minimum, maximum and mean temperatures, relative humidity, wind speed, precipitation, solar radiation and surface pressure were collected for two consecutive years (2017 – 2018) and used as inputs to the models. FAO 56 Penman-Monteith was used as the benchmark ET0. Root mean square error (RMSE) and Nash Sutcliffe efficiency criterion (NSE) were the performance evaluation criteria employed. The results revealed that, all the applied models led to reliable results, but SVR model provided the best performance with NSEs of 0.9949, 0.9871 and RMSEs of 0.0009, 0.0016 in the validation phase for Basra and Erbil stations, respectively. The general results implied that SVR model could be employed successfully for estimation of ET0 in arid and semiarid climate stations of Iraq.https://eajse.tiu.edu.iq/index.php/volume-6-issue-1-article-7/deep excavationfinite elementpre-stressed tie back anchorscontiguous pile wallplaxishorizontal deflectionground settlement |
| spellingShingle | Ala Tahsin Jazuli Abdullahi Reference Evapotranspiration Modeling Using Heuristic Computing Model in Distinct Climate Stations Eurasian Journal of Science and Engineering deep excavation finite element pre-stressed tie back anchors contiguous pile wall plaxis horizontal deflection ground settlement |
| title | Reference Evapotranspiration Modeling Using Heuristic Computing Model in Distinct Climate Stations |
| title_full | Reference Evapotranspiration Modeling Using Heuristic Computing Model in Distinct Climate Stations |
| title_fullStr | Reference Evapotranspiration Modeling Using Heuristic Computing Model in Distinct Climate Stations |
| title_full_unstemmed | Reference Evapotranspiration Modeling Using Heuristic Computing Model in Distinct Climate Stations |
| title_short | Reference Evapotranspiration Modeling Using Heuristic Computing Model in Distinct Climate Stations |
| title_sort | reference evapotranspiration modeling using heuristic computing model in distinct climate stations |
| topic | deep excavation finite element pre-stressed tie back anchors contiguous pile wall plaxis horizontal deflection ground settlement |
| url | https://eajse.tiu.edu.iq/index.php/volume-6-issue-1-article-7/ |
| work_keys_str_mv | AT alatahsin referenceevapotranspirationmodelingusingheuristiccomputingmodelindistinctclimatestations AT jazuliabdullahi referenceevapotranspirationmodelingusingheuristiccomputingmodelindistinctclimatestations |