Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India
Accurate prediction of pan evaporation and mean temperature is crucial for effective water resources management, influencing the hydrological cycle and impacting water availability. This study focused on New Delhi?s semi-arid climate, data spanning 31 years (1990?2020) were used to predict these var...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
IWA Publishing
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833349414172753920 |
|---|---|
| author | Rajput J. Kushwaha N.L. Srivastava A. Pande C.B. Suna T. Sena D.R. Singh D.K. Mishra A.K. Sahoo P.K. Elbeltagi A. |
| author2 | 57211190879 |
| author_facet | 57211190879 Rajput J. Kushwaha N.L. Srivastava A. Pande C.B. Suna T. Sena D.R. Singh D.K. Mishra A.K. Sahoo P.K. Elbeltagi A. |
| author_sort | Rajput J. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Accurate prediction of pan evaporation and mean temperature is crucial for effective water resources management, influencing the hydrological cycle and impacting water availability. This study focused on New Delhi?s semi-arid climate, data spanning 31 years (1990?2020) were used to predict these variables using advanced algorithms such as Bagging, Random Subspace (RSS), M5P, and REPTree. The models were rigorously evaluated using 10 performance metrics, including correlation coefficient, mean absolute error (MAE), and Nash?Sutcliffe Efficiency (NSE) model coefficient. The Bagging model emerged as the best model with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90, and 22.0, respectively, during model testing phase for pan evaporation prediction. In predicting mean temperature, the Bagging model reported the best results with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90 and 22.0, respectively, during the model testing phase. These findings offer valuable insights for enhancing relative humidity prediction models in diverse climatic conditions. The Bagging model?s robust performance underscores its potential application in water resource management. ? 2024 The Authors. |
| format | Article |
| id | my.uniten.dspace-36504 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | IWA Publishing |
| record_format | dspace |
| spelling | my.uniten.dspace-365042025-03-03T15:42:46Z Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India Rajput J. Kushwaha N.L. Srivastava A. Pande C.B. Suna T. Sena D.R. Singh D.K. Mishra A.K. Sahoo P.K. Elbeltagi A. 57211190879 57219726089 57221943932 57193547008 57726828300 6603383474 57198856885 57214672235 57203256213 57204724397 Resource allocation Water management Evaporation temperature Index values Mean absolute error Mean temperature Model testing Pan evaporation Performance indices Prediction indices Testing phase Water resources management Prediction models Accurate prediction of pan evaporation and mean temperature is crucial for effective water resources management, influencing the hydrological cycle and impacting water availability. This study focused on New Delhi?s semi-arid climate, data spanning 31 years (1990?2020) were used to predict these variables using advanced algorithms such as Bagging, Random Subspace (RSS), M5P, and REPTree. The models were rigorously evaluated using 10 performance metrics, including correlation coefficient, mean absolute error (MAE), and Nash?Sutcliffe Efficiency (NSE) model coefficient. The Bagging model emerged as the best model with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90, and 22.0, respectively, during model testing phase for pan evaporation prediction. In predicting mean temperature, the Bagging model reported the best results with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90 and 22.0, respectively, during the model testing phase. These findings offer valuable insights for enhancing relative humidity prediction models in diverse climatic conditions. The Bagging model?s robust performance underscores its potential application in water resource management. ? 2024 The Authors. Final 2025-03-03T07:42:46Z 2025-03-03T07:42:46Z 2024 Article 10.2166/wpt.2024.144 2-s2.0-85201638540 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201638540&doi=10.2166%2fwpt.2024.144&partnerID=40&md5=7599fe0437cfa4630e17763e96ec88da https://irepository.uniten.edu.my/handle/123456789/36504 19 7 2655 2655 2672 All Open Access; Gold Open Access IWA Publishing Scopus |
| spellingShingle | Resource allocation Water management Evaporation temperature Index values Mean absolute error Mean temperature Model testing Pan evaporation Performance indices Prediction indices Testing phase Water resources management Prediction models Rajput J. Kushwaha N.L. Srivastava A. Pande C.B. Suna T. Sena D.R. Singh D.K. Mishra A.K. Sahoo P.K. Elbeltagi A. Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India |
| title | Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India |
| title_full | Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India |
| title_fullStr | Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India |
| title_full_unstemmed | Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India |
| title_short | Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India |
| title_sort | development of machine learning models for estimation of daily evaporation and mean temperature: a case study in new delhi, india |
| topic | Resource allocation Water management Evaporation temperature Index values Mean absolute error Mean temperature Model testing Pan evaporation Performance indices Prediction indices Testing phase Water resources management Prediction models |
| url_provider | http://dspace.uniten.edu.my/ |