E3S Web Conf.
Volume 65, 2018International Conference on Civil and Environmental Engineering (ICCEE 2018)
|Number of page(s)||10|
|Section||Hydrology & Hydraulics|
|Published online||26 November 2018|
Improvement of SVR-Based Drought Forecasting Models using Wavelet Pre-Processing Technique
Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Bandar Sungai Long, Cheras, 43000 Kajang, Selangor, Malaysia
* Corresponding author: firstname.lastname@example.org
Drought is a damaging natural hazard due to the lack of precipitation from the expected amount for a period of time. Mitigations are required to reduced its impact. Due to the difficulty in determining the onset and offset of droughts, accurate drought forecasting approaches are required for drought risk management. Given the growing use of machine learning in the field, Wavelet-Boosting Support Vector Regression (W-BS-SVR) was proposed for drought forecasting at Langat River Basin, Malaysia. Monthly rainfall, mean temperature and evapotranspiration for years 1976 - 2015 were used to compute Standardized Precipitation Evapotranspiration Index (SPEI) in this study, producing SPEI-1, SPEI-3 and SPEI-6. The 1-month lead time SPEIs forecasting capability of W-BS-SVR model was compared with the Support Vector Regression (SVR) and Boosting-Support Vector Regression (BS-SVR) models using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) and Adjusted R2. The results demonstrated that W-BS-SVR provides higher accuracy for drought prediction in Langat River Basin.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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