Assessment of Artificial Neural Network through Drought Indices
Prediction of potential evapotranspiration (PET) using an artificial neural network (ANN) with a different network architecture is not uncommon. Most researchers select the optimal network using statistical indicators. However, there is still a gap to be filled in future applications in various drou...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Eng |
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| Online Access: | https://www.mdpi.com/2673-4117/4/1/3 |
| _version_ | 1827749956977426432 |
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| author | Smit Chetan Doshi Mohana Sundaram Shanmugam Shatirah Akib |
| author_facet | Smit Chetan Doshi Mohana Sundaram Shanmugam Shatirah Akib |
| author_sort | Smit Chetan Doshi |
| collection | DOAJ |
| description | Prediction of potential evapotranspiration (PET) using an artificial neural network (ANN) with a different network architecture is not uncommon. Most researchers select the optimal network using statistical indicators. However, there is still a gap to be filled in future applications in various drought indices and of assessment of location, duration, average, maximum and minimum. The objective was to compare the performance of PET computed using ANN to the Penman–Monteith technique and compare drought indices standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), using two different computed PET for the durations of 1, 3, 6, 9, and 12–months. Statistical performance of predicted PET shows an RMSE of 9.34 mm/month, RSR of 0.28, R<sup>2</sup> of 1.00, NSE of 0.92, and PBIAS of −0.04. Predicted PET based on ANN is lower than that the Penman–Monteith approach for maximum values and higher for minimum values. SPEI–Penman–Monteith and SPI have a monthly correlation of greater than 0.95 and similar severity categories, but SPEI is lower than SPI. The average monthly index values for SPEI prediction show that SPEI–ANN captures drought conditions with higher values than SPEI–Penman–Monteith. PET–based ANN, performs robustly in prediction, fails by a degree of severity classification to capture drought conditions when utilized. |
| first_indexed | 2024-03-11T06:35:49Z |
| format | Article |
| id | doaj.art-5b4fb1af1c8b4fcdb69807f37d9a1874 |
| institution | Directory Open Access Journal |
| issn | 2673-4117 |
| language | English |
| last_indexed | 2024-03-11T06:35:49Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj.art-5b4fb1af1c8b4fcdb69807f37d9a18742023-11-17T10:53:02ZengMDPI AGEng2673-41172022-12-0141314610.3390/eng4010003Assessment of Artificial Neural Network through Drought IndicesSmit Chetan Doshi0Mohana Sundaram Shanmugam1Shatirah Akib2Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, Bangkok 12120, ThailandWater Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, Bangkok 12120, ThailandDepartment of Civil Engineering, School of Architecture, Design and the Built Environment, Nottingham Trent University, Nottingham NG1 4FQ, UKPrediction of potential evapotranspiration (PET) using an artificial neural network (ANN) with a different network architecture is not uncommon. Most researchers select the optimal network using statistical indicators. However, there is still a gap to be filled in future applications in various drought indices and of assessment of location, duration, average, maximum and minimum. The objective was to compare the performance of PET computed using ANN to the Penman–Monteith technique and compare drought indices standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), using two different computed PET for the durations of 1, 3, 6, 9, and 12–months. Statistical performance of predicted PET shows an RMSE of 9.34 mm/month, RSR of 0.28, R<sup>2</sup> of 1.00, NSE of 0.92, and PBIAS of −0.04. Predicted PET based on ANN is lower than that the Penman–Monteith approach for maximum values and higher for minimum values. SPEI–Penman–Monteith and SPI have a monthly correlation of greater than 0.95 and similar severity categories, but SPEI is lower than SPI. The average monthly index values for SPEI prediction show that SPEI–ANN captures drought conditions with higher values than SPEI–Penman–Monteith. PET–based ANN, performs robustly in prediction, fails by a degree of severity classification to capture drought conditions when utilized.https://www.mdpi.com/2673-4117/4/1/3artificial neural networkdrought indicesstatistical analysisevapotranspirationUnited Kingdom |
| spellingShingle | Smit Chetan Doshi Mohana Sundaram Shanmugam Shatirah Akib Assessment of Artificial Neural Network through Drought Indices Eng artificial neural network drought indices statistical analysis evapotranspiration United Kingdom |
| title | Assessment of Artificial Neural Network through Drought Indices |
| title_full | Assessment of Artificial Neural Network through Drought Indices |
| title_fullStr | Assessment of Artificial Neural Network through Drought Indices |
| title_full_unstemmed | Assessment of Artificial Neural Network through Drought Indices |
| title_short | Assessment of Artificial Neural Network through Drought Indices |
| title_sort | assessment of artificial neural network through drought indices |
| topic | artificial neural network drought indices statistical analysis evapotranspiration United Kingdom |
| url | https://www.mdpi.com/2673-4117/4/1/3 |
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