Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha
Article
Yhang, X., Mohanty, S., Parida, A., Pani, S., Dong, B. and Cheng, X. 2020. Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha. IEEE Access. 8, pp. 30223-30233. https://doi.org/10.1109/ACCESS.2020.2972435
| Type | Article |
|---|---|
| Title | Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha |
| Authors | Yhang, X., Mohanty, S., Parida, A., Pani, S., Dong, B. and Cheng, X. |
| Abstract | Rainfall is a natural demolishing phenomenon. On the other side, it also serves as a major source of water when conserved through proper channel. For this issue, estimation of rain fall is of at utmost importance. The present study employed on rain fall forecasting in annual as well as non-moon session in Odisha (India). The total annual rainfall and relative humidity data were collected from period 1991-2015 from Department of Forest and Environment Govt. of Odisha. Support Vector Regression and Multilayer perception implemented for prediction of maximum rainfall in annual and non-monsoon session. Input parameter like average temperature in month, wind velocity, humidity, and cloud cover was conceder for predicting rainfall in non-monsoon session. The performance of the results was measure with MSE (mean squared error), correlation coefficient, coefficient of efficiency and MAE (mean absolute error). The results of SVR were compared to those of MLP and simple regression technique. MLP being a computationally intensive method, SVR could be used as an efficient alternative for runoff and sediment yield prediction under comparable accuracy in predictions.SVR-MLP may be used as promising alternative forecasting tool for higher accuracy in forecasting and better generalization ability. |
| Keywords | Support vector regression (SVR), multi-layer perception, rainfall |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Journal | IEEE Access |
| ISSN | 2169-3536 |
| Publication dates | |
| Online | 10 Feb 2020 |
| 18 Feb 2020 | |
| Publication process dates | |
| Deposited | 12 Mar 2020 |
| Accepted | 21 Jan 2020 |
| Output status | Published |
| Publisher's version | License |
| Copyright Statement | This work is licensed under a Creative Commons Attribution 4.0 License. |
| Web address (URL) | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8990179 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2020.2972435 |
| Language | English |
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