Deep Learning Approach Using Long Short Term Memory Technique for Monthly Rainfall Prediction in Chhattisgarh, India

Nisha Thakur¹ , Sanjeev Karmakar²

Section:Research Paper, Product Type: Journal-Paper
Vol.9 , Issue.1 , pp.8-13, Feb-2021

Online published on Feb 28, 2021

Copyright © Nisha Thakur, Sanjeev Karmakar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Rainfall is an essential factor in Chhattisgarh state as the economy is dependent on agriculture here. Time Series forecasting approach for monthly rainfall prediction is done using Long Short Term Memory [LSTM] Model applying on 1404 months data of Chhattisgarh state. The factors taken for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Cosine Similarity (CS) and Correlation Coefficient (r). Various learning rate like (? = 0.01, 0.05, 0.001, 0.005) for various epoch(s) such as 200, 400, 600, 800 and 1000 respectively are done for LSTM approach. The experimental results show that Long Short Term Memory gave significant results than ANN for 200 epochs.

Key-Words / Index Term :
Forecasting; Long short-term memory; Recurrent neural networks; Time series; Artificial Neural Network (ANN)

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