Abstract
Agriculture, transportation, forestry, and tourism are just a few of the human efforts that are impacted by rainfall, which is a climatic factor. As Rainfall is the climatic element that governments have all voiced worry about how challenging it is to predict when it will rain. Because it is most frequently associated with undesirable natural events including landslides, flooding, mass movements, and avalanches, rainfall prediction is essential in this respect. These events have had a lasting effect on society. Therefore, by implementing a reliable method for predicting rainfall, it is feasible to take precautionary measures for these natural occurrences. Because meteorological systems are not consistently recognized across time, predicting rainfall is difficult. This study forecasts the rainfall using deep learning techniques. As an outcome, in this research, we propose a model for forecasting the rate of rainfall using the convolution neural network (CNN) technique. As a consequence, we were able to generate rainfall model with an average root mean squared error of 2.82% and a standardized mean absolute error of 2.43%. Our method is unique in that we have developed a neural network-based model that uses cyclone datasets to forecast the rate of precipitation.
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Lakshmi Sujitha, T., Anuradha, T., Akshitha, G. (2023). A Satellite-Based Rainfall Prediction Model Using Convolution Neural Networks. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_23
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DOI: https://doi.org/10.1007/978-981-99-6702-5_23
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