TY - JOUR
T1 - A Hybrid Model for Statistical Downscaling of Daily Rainfall
AU - Pour, Sahar Hadi
AU - Shahid, Shamsuddin
AU - Chung, Eun Sung
N1 - Publisher Copyright:
© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
PY - 2016
Y1 - 2016
N2 - The robustness of random forest (RF) in classification and superiority of support vector machine (SVM) to fit highly non-linear data were used to develop a hybrid model for statistical downscaling of daily rainfall. The RF was used to predict whether rain will occur in a day or not and SVM was used to predict amount of rainfall in rainfall occurring days. The capability of proposed hybrid model was verified by downscaling daily rainfall at three rain-gauge locations in the east cost of peninsular Malaysia. Obtained results reveal that the hybrid model can downscale rainfall with Nash-Sutcliff efficiency in the range of 0.90-0.93, which is much higher compared to RF and SVM downscaling models. The hybrid model was also found to replicate the variability, number of consecutive wet days, 95-percentile rainfall amount in each months as well as distribution of observed rainfall reliably.
AB - The robustness of random forest (RF) in classification and superiority of support vector machine (SVM) to fit highly non-linear data were used to develop a hybrid model for statistical downscaling of daily rainfall. The RF was used to predict whether rain will occur in a day or not and SVM was used to predict amount of rainfall in rainfall occurring days. The capability of proposed hybrid model was verified by downscaling daily rainfall at three rain-gauge locations in the east cost of peninsular Malaysia. Obtained results reveal that the hybrid model can downscale rainfall with Nash-Sutcliff efficiency in the range of 0.90-0.93, which is much higher compared to RF and SVM downscaling models. The hybrid model was also found to replicate the variability, number of consecutive wet days, 95-percentile rainfall amount in each months as well as distribution of observed rainfall reliably.
KW - daily rainfall
KW - hybrid model
KW - random forest
KW - Statistical downscaling
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84997770183&partnerID=8YFLogxK
U2 - 10.1016/j.proeng.2016.07.514
DO - 10.1016/j.proeng.2016.07.514
M3 - Conference article
AN - SCOPUS:84997770183
SN - 1877-7058
VL - 154
SP - 1424
EP - 1430
JO - Procedia Engineering
JF - Procedia Engineering
T2 - 12th International Conference on Hydroinformatics - Smart Water for the Future, HIC 2016
Y2 - 21 August 2016 through 26 August 2016
ER -