A Novel Deep Learning Model for Rainfall Prediction in New Delhi

The Indian capital, New Delhi, is a landlocked metropolis.Due to its distance from the sea, Delhi experiences an extreme continental climate, with continental air predominating for most of the year. The water cycle, which includes rainfall patterns and monsoon intensity, can be impacted by air pollution. The pace at which water evaporates and enters the atmosphere is influenced by particulate matter’s ability to block solar energy from reaching the earth’s surface. Rain can temporarily remove dust in Delhi. Sulfur dioxide and ozone are two examples of contaminants that are difficult to wash away. That being said, prolonged periods of rain can significantly wipe off PM 2.5 and PM 10. There should be concern when levels of tiny particulate matter in the air (PM2.5) are deemed harmful. Exposure to dangerous levels of PM2.5 in the air can increase the risk of acquiring illnesses such as asthma, heart disease, and low birth weight. Additionally, unhealthy levels can make it harder to see and make the air seem hazy. Today, predicting rainfall for a specific time period is a difficult scientific task because abnormal climate variations have been discovered Numerous research organizations are diligently attempting to anticipate the amount of rainfall using various methods. To determine the parameter unique to a given geolocation, We develop multi-variate time series analysis for smaller temporal targets like t+1 and t+3 and longer temporals like t+6 and t+15 by finding the geolocality-specific parameters in addition to these multivariables. New Delhi is extremely humid and filthy; we must focus on that particular location and use this research to improve our understanding of the Delhi environment.