Analyzing Climate Trends and Predictive Modeling in Major Indian Cities - A Big Data Approach

Climate change poses significant challenges worldwide, with urban areas particularly susceptible to its impacts. Understanding local climate trends is essential for informed decision-making and proactive measures towards climate resilience. This paper presents a pioneering study that investigates climate dynamics in major cities of India using advanced big data technologies, namely Hadoop and Spark. Spanning from 1995 to 2020, the dataset comprises temperature records of key cities, enabling a comprehensive analysis of long-term climate trends. Leveraging MapReduce for data preprocessing and Spark for analysis and visualization, the research explores temperature variations across regions and cities, with a focus on seasonal patterns and anomalies. Specifically, the study delves into the climate trends of Delhi, offering insights into its seasonal fluctuations. Furthermore, predictive modeling techniques such as Autoregressive Moving Average (ARMA) and Autoregressive (AR) models are employed to forecast future temperature trends, aiding in proactive climate adaptation strategies. The findings contribute to a deeper understanding of climate dynamics in urban India and provide valuable insights for policymakers, urban planners, and environmentalists in devising effective climate resilience and sustainable development strategies.