India, a land deeply intertwined with its monsoons, relies heavily on rainfall for everything from nourishing its vast agricultural lands to replenishing its vital water reservoirs. The rhythm of the rains dictates the pulse of the nation, influencing water resource management, urban infrastructure, and even the preparedness for natural disasters. However, rainfall in India is notoriously unpredictable, characterised by its varied forms, regional climatic phenomena, and the powerful, yet erratic, monsoonal capabilities. This inherent unpredictability poses significant challenges for accurate forecasting, a critical need in a country where millions depend on timely and precise rainfall information.
This is the challenge a recent study by the India Meteorological Department, Hyderabad, set out to tackle. The team studied the complex patterns of urban precipitation in major Indian cities using a combination of statistical and machine learning techniques.
The researchers analysed rainfall patterns across six of India’s bustling metropolises: Hyderabad, Delhi, Mumbai, Chennai, Kolkata, and Bangalore. Their findings paint a diverse picture of how rainfall behaves in these urban centres. Through statistical analysis, they uncovered distinct trends and seasonal variations. For instance, seasonal decomposition revealed subtle but significant trend means in rainfall, ranging from a slight increase of 1.7 millimetres in Delhi to a more substantial 4.9 millimetres in Kolkata. Monthly rainfall peaks also varied dramatically, with Chennai experiencing its wettest month in November (averaging 12.1 mm), while Kolkata and Mumbai saw their highest rainfall in July (20.2 mm and 18.5 mm, respectively).
Looking at the bigger picture of yearly trends, the study utilised linear regression to identify long-term changes. Hyderabad and Mumbai showed increasing rainfall trends, with positive slopes of 2.0 and 3.1, respectively, suggesting a need for improved flood management. Conversely, Delhi and Kolkata exhibited declining rainfall trends, with negative slopes of -1.5 and -2.4, raising concerns about future water supply and agricultural practices. Bangalore stood out with the most significant upward trend, a slope of 4.3, underscoring the urgency for robust flood strategies in that city.
The research team then turned to machine learning for rainfall prediction. They tested several models, including XGBoost, Random Forest, and Gradient Boosting, as well as other models such as Support Vector Machines and Neural Networks. The results were impressive: Random Forest and Gradient Boosting models achieved the highest predictive accuracy, both at 76.6%, closely followed by XGBoost at 76.5%. This demonstrates the remarkable capability of these AI models to learn from historical data and forecast future rainfall events.
However, they also identified a challenge: the models sometimes struggled to accurately predict Rain days compared to No Rain days, a common issue known as class imbalance, which occurs when there are many more no-rain days than rain days in the dataset. To overcome this, the researchers combined established statistical methods with the machine learning model. First, they gathered gridded rainfall data from the India Meteorological Department (IMD) spanning from 1981 to 2023 for the six chosen cities.
The statistical toolkit they employed was robust. Principal Component Analysis (PCA), a smart filter that identifies the most important patterns in complex data, was used to simplify the vast dataset. This allowed them to see shared patterns in rainfall variability across cities. Next, they used Seasonal Decomposition, specifically the LOESS method, a technique that breaks down rainfall data over time into three main parts: the long-term trend, the predictable seasonal variations (like monsoons), and the random, unpredictable residual component. This separation helps to understand the underlying drivers of rainfall changes. Finally, for forecasting, they used ARIMA models, a classic statistical method for time-series data that considers past values, differences between them, and past errors to predict future trends.
On the machine learning front, the researchers leveraged a family of algorithms known for their ability to handle structured datasets effectively. XGBoost, Random Forest, and Gradient Boosting are all ensemble methods. These models were chosen for their proven efficiency and accuracy with medium-sized structured datasets. After training, the models’ performance was rigorously evaluated using metrics like accuracy, precision, recall, and F1-score, which help assess how well the models predicted both rainy and non-rainy days.
Combining both classical statistical methods and machine learning allowed the researchers to create a more powerful and comprehensive framework for localised rainfall prediction. By incorporating recent advancements in machine learning (post-2020) and validating their results against real-world observational data, the study strengthens the reliability and relevance of its findings.
However, the researchers acknowledge certain limitations. For instance, they note that more complex deep learning models, such as LSTMs (Long Short-Term Memory networks) or CNNs (Convolutional Neural Networks), were not explored in this study. This was primarily due to computational limitations and the relatively small size of the dataset available for this specific research. They suggest that future work could explore these advanced techniques for even higher predictive precision. Other limitations include inherent data quality concerns, such as occasional missing values or measurement inconsistencies, and potential biases within the machine learning models themselves. The study also highlights that regional variability may impact the generalizability of the findings to other cities or regions. Furthermore, while the models achieved good accuracy, the challenge of class imbalance (predicting Rain days versus No Rain days) means there’s still room for improvement in identifying those crucial rainy instances. Future research could address this by employing strategies like resampling or cost-sensitive learning.
By providing more accurate and localised rainfall predictions, this study offers invaluable insights for urban planning and disaster management. Cities experiencing increasing rainfall trends, such as Hyderabad, Mumbai, and Bangalore, can prioritise and invest in robust flood management infrastructure, preventing widespread damage and loss of life. Conversely, cities facing declining rainfall, like Delhi and Kolkata, can develop more sustainable water resource planning strategies to address potential shortages, ensuring water security for their populations and agricultural needs. This integrated approach to understanding and predicting rainfall patterns is a critical step towards building climate resilience.
This article was written with the help of generative AI and edited by an editor at Research Matters.