Predicting Rainfall Using Machine Learning, Deep Learning, and Time Series Models Across an Altitudinal Gradient in the North-Western Himalayas

Full Article

Predicting rainfall is crucial for minimizing societal impacts, especially in agriculture-dependent regions like India. The study emphasizes the need for accurate rainfall forecasts to prepare for disasters linked to heavy rains. It proposes using advanced Machine Learning and Deep Learning algorithms to enhance prediction accuracy in the North-Western Himalayas, where traditional methods fall short due to limited meteorological data.

The research evaluates various algorithms, including Random Forest, Support Vector Regression, and Long Short-Term Memory networks, to determine their effectiveness in rainfall prediction. Results indicate that Deep Learning methods outperform others, highlighting the importance of altitude in model accuracy. This innovative approach could significantly transform rainfall forecasting, leading to better preparedness and response strategies.

Key AI Highlights in this Article

• Deep Learning methods show highest accuracy in rainfall prediction.

• Altitude significantly affects the accuracy of rainfall prediction models.

Key AI Terms Mentioned in this Article

Machine Learning

Machine Learning algorithms like Random Forest and Support Vector Regression are applied for rainfall prediction.

Deep Learning

Deep Learning techniques, including LSTM and Bi-directional LSTM, enhance prediction accuracy.

Time Series Analysis

Time series techniques like ARIMA-X and TBATS are utilized for forecasting rainfall patterns.

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