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.
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Acknowledgements
This research was supported and funded by Indian Institute of Technology, Kharagpur, India and MHRD, India under the project named “Feature Extraction and Data Mining from Climate Data (FAD)”. We would like to thank IIT Kharagpur, MHRD and also the IMD(India Meteorological Society) for their helpful suggestions and support. Without their help this work would not have been completed.
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Gope, S., Sarkar, S., Mitra, P., Ghosh, S. (2016). Early Prediction of Extreme Rainfall Events: A Deep Learning Approach. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_12
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DOI: https://doi.org/10.1007/978-3-319-41561-1_12
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