Agriculture plays a fundamental role in ensuring food security and economic stability worldwide. However, unpredictable rainfall patterns, exacerbated by climate change, pose a significant threat to agricultural productivity. Farmers and agricultural planners rely heavily on accurate weather forecasts to determine optimal sowing periods, irrigation scheduling, and harvesting times. Unfortunately, traditional meteorological models often lack the precision, adaptability, and real-time capabilities necessary for effective agricultural planning. Consequently, unreliable weather forecasts contribute to water mismanagement, crop failure, and financial losses in the agricultural sector. This research presents a novel AI-driven real-time early warning system that enhances rainfall forecasting accuracy using advanced machine learning (ML) techniques. The system integrates a comprehensive dataset comprising historical rainfall records, satellite-based meteorological observations, and real-time sensor data. By employing deep learning architectures, such as Long Short-Term Memory (LSTM) networks, alongside machine learning algorithms like Random Forest (RF) and Support Vector Machines (SVM), the proposed system identifies intricate patterns in weather data and generates high-precision short-term and long-term rainfall predictions. These predictive models significantly outperform conventional forecasting techniques by dynamically adapting to climatic variations and reducing error margins. The study follows a structured methodology that includes extensive data preprocessing, feature selection, and model training. To assess the reliability of the proposed forecasting system, performance evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R²) are applied. The results demonstrate that AI-driven forecasting models exhibit superior accuracy compared to traditional statistical approaches. The system effectively provides timely early warnings about impending droughts, excessive rainfall, and seasonal anomalies, thereby enabling farmers to make informed, proactive decisions that mitigate risks and optimize agricultural productivity. Furthermore, the proposed system has been validated through real-world case studies across multiple climatic zones. These studies highlight the system’s practical utility in guiding farmers toward sustainable agricultural practices and resource management. The integration of real-time updates enhances the reliability of the predictions, making it a valuable tool for policymakers and agricultural stakeholders. By leveraging AI for meteorological forecasting, this research contributes to strengthening climate resilience in the agricultural sector, ensuring food security, and promoting sustainable farming practices in the face of global climate uncertainty.
Volume 17 | Issue 1
Pages: 35-47