Analyzing Rainfall Trends in Konkan and Goa: A Data-Driven Approach

Rainfall variability plays a crucial role in shaping the ecological and economic landscape of coastal regions. The Konkan and Goa region, known for its heavy monsoonal rainfall, has witnessed fluctuations in precipitation patterns over the years due to climate change and other environmental factors. This study presents a comprehensive analysis of historical rainfall data to assess trends, seasonal distributions, and anomalies. Using statistical techniques, data visualization, and predictive modeling, this research explores the impact of monsoonal variability on regional water resources and agriculture. The dataset consists of multi-year rainfall records, categorized into monthly and annual precipitation levels. Data preprocessing involves handling missing values, detecting outliers, and standardizing the dataset for analysis. Exploratory Data Analysis (EDA) techniques such as correlation matrices, box plots, and time series decomposition are applied to extract insights into rainfall distribution across different months and years. Additionally, machine learning models, including regression techniques, are used to forecast future rainfall trends. The performance of these models is evaluated using statistical error metrics to ensure reliable predictions. The findings of this study highlight significant patterns in monsoon variability and offer predictions that can aid in climate adaptation strategies, agricultural planning, and disaster risk management in the Konkan and Goa region. By integrating historical climate data with predictive analytics, this research provides valuable insights for policymakers, environmentalists, and stakeholders in water resource management.