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@article{178036,
        author = {Harsh kumar and Aditya thakur and Varun kumar and Keshav kumar and Hariom upadhyay},
        title = {Rainfall prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4023-4027},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178036},
        abstract = {This study aims to enhance rainfall fore-casting through a deep neural network (DNN) model. The model combines meteorological data like temperature, humidity, and rainfall records to forecast rainfall events. It employs sophisticated methods such as Swish activation, L2 regularization, and dropout to improve accuracy and avoid overfitting. The model provides binary predictions (rain/no rain) with probability estimates. Results indicate substantial improvements in prediction accuracy over conventional methods. This study offers valuable in-sights for agriculture, water management, and climate resilience by providing more accurate rainfall forecasts.},
        keywords = {Rainfall Forecasting, Deep Neural Network, Swish activation, prediction accuracy},
        month = {May},
        }