Wani, OA, Mahdi, SS, Yeasin, M, Kumar, SS, Gagnon, A, Danish, F, Al-Ansari, N, El-Hendawy, S and Mattar, MA (2024) Predicting Rainfall Using Machine Learning, Deep Learning, and Time Series Models Across an Altitudinal Gradient in the North-Western Himalayas. Scientific Reports, 14.

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Abstract

Predicting rainfall is a challenging and critical task due to its significant impact on society. Timely and accurate predictions are essential for minimising human and financial losses. The dependence of approximately 60% of agricultural land in India on monsoon rainfall underscores the crucial nature of accurate rainfall prediction. Furthermore, precise rainfall forecasts can facilitate early preparedness for disasters associated with heavy rains, enabling the public and government to take necessary precautions. In the North-western Himalayas, where meteorological data are limited, the need for improved accuracy in traditional modelling methods for rainfall forecasting is pressing. To address this, our study proposes the application of advanced Machine Learning (ML) algorithms - including Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) - along with various Deep Learning (DL) algorithms such as Long Short-Term Memory (LSTM), Bi-directional LSTM, Deep LSTM, Gated Recurrent Unit (GRU), and simple Recurrent Neural Network (RNN). These advanced techniques hold the potential to significantly improve the accuracy of rainfall prediction, offering hope for more reliable forecasts. Additionally, time series techniques, including autoregressive integrated moving average (ARIMA-X) and trigonometric, Box-Cox transform, arma errors, trend, and seasonal components (TBATS), are proposed for predicting rainfall across the altitudinal gradients of India's North-western Himalayas. This approach can potentially revolutionise how we approach rainfall forecasting, ushering in a new era of accuracy and reliability. The effectiveness and accuracy of the proposed algorithms were assessed using meteorological data obtained from six weather stations at different elevations spanning from 1980 to 2021. The results indicate that DL methods exhibit the highest accuracy in predicting rainfall, as measured by the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), followed by ML algorithms and time series techniques. Among the DL algorithms, the accuracy order was Bi-directional LSTM, LSTM, RNN, Deep LSTM, and GRU. For the ML algorithms, the accuracy order was ANN, KNN, SVR, and RF. In terms of time series techniques, TBATS demonstrated higher accuracy than ARIMA-X. Furthermore, the findings suggest that altitude significantly affects the accuracy of the models, highlighting the need for additional weather stations in this mountainous region to enhance the precision of rainfall prediction.

Item Type:	Article
Uncontrolled Keywords:	altitudinal gradient; Bi-directional Long-Short-Term Memory; machine and deep learning models; North-western Himalayas; rainfall prediction
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences T Technology > T Technology (General)
Divisions:	Biological and Environmental Sciences (from Sep 19)
Publisher:	Nature Research
SWORD Depositor:	A Symplectic
Date Deposited:	20 Nov 2024 11:46
Last Modified:	20 Nov 2024 12:00
URI:	https://researchonline.ljmu.ac.uk/id/eprint/24682

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