Early Prediction of Rainfall using Automated Integrated Prediction Approach (AIPA)

Agriculture is one of the significant fields in the world nowadays. It is essential to improve agricultural yield by using various advanced techniques. Rainfall prediction is the most challenging task for regular users, and it helps to improve crop yielding based on rainfall. Prediction of rainfall in the early stages provides accurate information to the farmers who need preventive measures. The proposed approach is an Automated Integrated Prediction Approach (AIPA) which is the combination of machine learning models, such as deep neural networks (DNNs), support vector machines (SVMs), and gradient boosting devices (GBMs), to accurately predict rainfall. Finally, the algorithm's versatility suits various geographical regions and climates, promoting sustainable agriculture and food security. Finally, merging DL and meteorological data analysis to develop a hybrid algorithm shows significant promise for increasing rainfall prediction accuracy. This research addresses climate change challenges while promoting agricultural sustainability and increased food production. Experimental results show that the proposed approach obtained high performance in terms of performance metrics.