Record Details

Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier

DSpace at IIT Bombay

View Archive Info
 
 
Field Value
 
Title Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier
 
Creator NAYAK, M
GHOSH, S
 
Subject OBJECTIVE ANALYSIS
CONVENTIONAL DATA
ANALYSIS SCHEME
INDIA
MODEL
PRECIPITATION
SATELLITE
 
Description A major component of flood alert broadcasting is the short-term prediction of extreme rainfall events, which remains a challenging task, even with the improvements of numerical weather prediction models. Such prediction is a high priority research challenge, specifically in highly urbanized areas like Mumbai, India, which is extremely prone to urban flooding. Here, we attempt to develop an algorithm based on a machine learning technique, support vector machine (SVM), to predict extreme rainfall with a lead time of 6-48 h in Mumbai, using mesoscale (20-200 km) and synoptic scale (200-2,000 km) weather patterns. The underlying hypothesis behind this algorithm is that the weather patterns before (6-48 h) extreme events are significantly different from those of normal weather days. The present algorithm attempts to identify those specific patterns for extreme events and applies SVM-based classifiers for extreme rainfall classification and prediction. Here, we develop the anomaly frequency method (AFM), where the predictors (and their patterns) for SVM are identified with the frequency of high anomaly values of weather variables at different pressure levels, which are present before extreme events, but absent for non-extreme conditions. We observe that weather patterns before the extreme rainfall events during nighttime (1800 to 0600Z) is different from those during daytime (0600 to 1800Z) and, accordingly, we develop a two-phase support vector classifier for extreme prediction. Though there are false alarms associated with this prediction method, the model predicts all the extreme events well in advance. The performance is compared with the state-of-the-art statistical technique fingerprinting approach and is observed to be better in terms of false alarm and prediction.
 
Publisher SPRINGER WIEN
 
Date 2014-10-15T14:10:57Z
2014-10-15T14:10:57Z
2013
 
Type Article
 
Identifier THEORETICAL AND APPLIED CLIMATOLOGY, 114(3-4)583-603
0177-798X
1434-4483
http://dx.doi.org/10.1007/s00704-013-0867-3
http://dspace.library.iitb.ac.in/jspui/handle/100/15069
 
Language en