@inproceedings{5b4526cb78fc4eeb8879f85b75c56fc4,
title = "Efficient Forecasting of Precipitation Using LSTM",
abstract = "Precipitation is one of those many critical elements of the hydrological cycle that has a direct impact on human life in many aspects. An accurate and early detection of a future precipitation event can help in preventing human and financial losses. Therefore, it is vital to design a framework that can predict the precipitation with a significant accuracy. Accordingly, in this paper we have proposed a Long-Short-Term-Memory (LSTM) based forecast model which can predict the precipitation values efficiently using the historical values. The design of forecast model is made simple to avoid the heavy training time. Furthermore, a transformation technique was applied on the precipitation dataset to make the data more normal distribution-like. The proposed model was evaluated in terms of Root Mean Square Error (RMSE). The proposed model achieves a 6.186 RMSE and outperforms the traditional persistence and average forecast models.",
author = "{Salman Pathan}, Muhammad and Mayank Jain and {Hui Lee}, Yee and T. Alskaif and Soumyabrata Dev",
year = "2021",
doi = "10.1109/PIERS53385.2021.9694772",
language = "English",
isbn = "9781728172477",
series = " Progress in Electromagnetic Research Symposium (PIERS) ",
publisher = "IEEE",
editor = "{Au Kong}, J. and {Cho Chew}, W. and S. He",
booktitle = "Progress in Electromagnetic Research Symposium (PIERS)",
address = "United States",
note = "2021 Photonics & Electromagnetics Research Symposium (PIERS) ; Conference date: 21-11-2021 Through 25-11-2021",
}