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Open AccessArticle

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Department of Computer Science & Engineering, SRM Institute of Science and Technology, Kattangulathur 603203, India
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Atmosphere 2019, 10(11), 668; https://doi.org/10.3390/atmos10110668
Received: 4 September 2019 / Revised: 14 October 2019 / Accepted: 25 October 2019 / Published: 31 October 2019
Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall. View Full-Text
Keywords: long short-term memory; predictive analytics; rainfall prediction; recurrent neural network long short-term memory; predictive analytics; rainfall prediction; recurrent neural network
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Poornima, S.; Pushpalatha, M. Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units. Atmosphere 2019, 10, 668.

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