Analysis of district-level monsoon rainfall patterns in India: A pilot study

Abstract

Agricultural activities in India are heavily reliant on the monsoon rainfall during July–September every year. Indian Meteorological Department has been issuing rainfall forecasts since 1886. These predictions at a country or broad region level have limited benefits since different areas may see wide variations even when the overall average for India remains stable. This study explored possibilities of creating a cluster of districts as a more granular yet cohesive unit for rainfall forecast, by using different weather and atmospheric variables for past 12 months. Analytically, Principal Component Analysis (PCA) was used to reduce data dimensionality before creating an optimal cluster solution. Subsequently, a set of cluster-level linear regression models was found to perform better than a single regression model based on the entire sample. While district-level predictions showed limited value, the sequential combination of unsupervised and supervised techniques showed promising results at an overall level. These results will serve as a strong baseline for the planned extension of this pilot study which will use advanced machine learning techniques to improve upon the prediction performance further.