A novel rainfall prediction model for North-East region of India using stacked LSTM model
Paper Details
A novel rainfall prediction model for North-East region of India using stacked LSTM model
Abstract
The main objective of this work is to analyze different atmospheric factors and their correlation to daily rainfall, and then further compare them using various machine learning models such as Multivariate Linear Regression (MLR), Decision Tree Regressor, Random Forest Regressor, XGBoost Regressor and our proposed Stacked LSTM RNN model for forecasting rainfall. Daily rainfall data for almost 40 years are taken as input for this study and comparison of the above mentioned models have been carried out with respect to the Mean Absolute Error (MAE), Coefficient of Regression (R2) and the Mean Squared Error (MSE). The LSTM model emerged as the top performer, displaying the lowest MAE score. Hence, it was chosen to forecast daily rainfall for 7 days, weekly rainfall for 4 weeks and monthly rainfall for 12 consecutive months. Notably, the model’s accuracy improves as the duration of recorded observations are increased. The key novelty presented in this work is the proposed stacked LSTM Model and the comparative study of the model to predict rainfall in daily, weekly and monthly format. Our study prominently underscores the effectiveness of the proposed stacked LSTM Model in accurately forecasting rainfall.
Majumdar S, Biswas SK, Purkayastha B, Sanyal S. 2023. Rainfall Forecasting for Silchar City using Stacked- LSTM. 11th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON), Jaipur, India. pp. 1-5. DOI: 10.1109/IEMECON56962.2023.10092355.
Liyew CM, Melese HA. 2021. Machine Learning Techniques to Predict Daily Rainfall Amount. Journal of big Data 8, 153. https://doi.org/10.1186/s40537-021-00545-4
Salehin I, Talha IM, Hasan MM, Dip ST, Saifuzzaman M, Moon NN. 2020. An Artificial Intelligence Based Rainfall Prediction Using LSTM and Neural Network. IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Bhubaneswar, India. pp. 5-8. DOI: 10.1109/WIECON-ECE52138.2020.9398022
Gomathy CK., Reddy ABN, Kumar AP, Lokesh A. 2021. A Study Of Rainfall Prediction Techniques. International Journal of Scientific Research in Engineering and Management 5(10), 1-15.
Python Decision Tree Regression using sklearn. Available from: https://www.geeksforgeeks.org/python-decision-tree-regression-using-sklearn/
Jonsson E., Fredrikson S. 2021. An Investigation Of How Well Random Forest Regression Can Predict Demand.
Browniee J. 2021. XGBoost for Regression. Available from: https://machinelearningmastery.com/xgboost-for-regression
The Value of LSTM in Time Series Forecasting. Available from: https://www.predicthq.com/events/lstm-time-series-forecasting
Angela, Shi K. 2023. Decision Tree Regressor- A Visual Guide with Scikit Lear. Towards Data Science.
Chugh A. 2020. MAE, MSE, RMSE, Coefficient of Determination, Adjusted R Squared-Which metric is better. Analytics Vidhya.
Introduction to Recurrent Neural Networks. Available from: https://www.geeksforgeeks.org/introduction-to-recurrent-neural-network
Poornima S, Pushpalatha M. 2019. Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units. Atmosphere 10(11), 668. https://doi.org/10.3390/atmos10110668
Chhetri M., Kumar S, Roy PP, Kim B-G. 2020. Deep BLSTM-GRU Model for Monthly Rainfall Prediction: A Case Study of Simtokha, Bhutan. Remote Sensing 12(9), 3174. https://doi.org/10.3390/rs12193174
Satyajit Sarmah, Rajdeep Kr. Dutta, Chandrikka Pathak, Rubul Kumar Bania, 2023. A novel rainfall prediction model for North-East region of India using stacked LSTM model. J. Biodiv. Environ. Sci., 23(5), 23-30.
Copyright © 2023 by the Authors. This article is an open access article and distributed under the terms and conditions of the Creative Commons Attribution 4.0 (CC BY 4.0) license.