Paper Info
Title
Early Prediction of Extreme Rainfall Events: A Deep Learning Approach.
Abstract
Prediction of heavy rainfall is an extremely important problem in the field of meteorology as it has a great impact on the life and economy of people. Every year many people in different parts of the world suffer from the severe consequences of heavy rainfall like flood, spread of diseases, etc. We have proposed a model based on deep neural network to predict extreme rainfall from the previous climatic parameters. Our model comprising of a stacked auto-encoder has been tested for Mumbai and Kolkata, India, and found to be capable of predicting heavy rainfall events over both these regions. The model is able to predict extreme rainfall events 6 to 48 h before their occurrence. However it also predicts several false positives. We compare our results with other methods and find our method doing much better than the other methods used in literature. Predicting heavy rainfall 1 to 2 days earlier is a difficult task and such an early prediction can help in avoiding a lot of damages. This is where we find that our model can give a promising solution. Compared to the conventional methods used, our method reduces the number of false alarms; on further analysis of our results we find that in many cases false alarm has been raised when there has been rainfall in the surrounding regions. Thus our model generates warning for heavy rain in surrounding regions as well.
Year
DOI
Venue
2016
10.1007/978-3-319-41561-1_12
ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS
Keywords
Field
DocType
Machine learning,Deep learning,Stacked auto-encoder
Meteorology,False alarm,Computer science,Artificial intelligence,Deep learning,Artificial neural network,Flood myth,False positive paradox,Precipitation
Conference
Volume
ISSN
Citations 
9728
0302-9743
3
PageRank 
References 
Authors
0.37
10
4
Name
Order
Citations
PageRank
Sulagna Gope130.37
Sudeshna Sarkar2423210.58
Pabitra Mitra31729126.79
Subimal Ghosh431.39