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@article{159482,
        author = {Kajal Gangele and Priyanka Karande  and Ashwini Thappa  and Sujata Kullur },
        title = {Prediction of Rainfall and its Affecting Factors using Machine Learning },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {12},
        pages = {11-16},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159482},
        abstract = {Predicting rainfall is the most difficult aspect of weather forecasting. Accurate rainfall forecasting is now more challenging than ever because of significant climatic changes. So, it is essential to study how rain acts in connection to the variables
    • namely, temperature, humidity, pressure, and wind speed. Then, and only then, will we be able to accurately predict the rain. Forecasting is the process of using past data to create assumptions about future variations in rainfall amounts. Furthermore, time forecasting is concerned with the creation of models and methods that lead to precise forecasts and predictions. This study, which is a survey, used a thorough mapping study and a methodical literature review. The most popular linear time series models used in time series forecasting are ARIMA, Prophet, and Holt Winter’s Exponential Smoothing, which have been around for a while due to their high predicting accuracy and will be employed in this study. The major objective of this project is to raise an understanding of time series forecasting and its techniques and to develop a system that not only forecasts rainfall but also the variables that influence it.},
        keywords = {Rainfall, Temperature, humidity, wind speed, pressure, Time series, forecasting, ARIMA, Holt Winter’s Expo- nential Smoothing, and Prophet},
        month = {},
        }