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Application of Convolutional Recurrent Neural Network in Precipitation Forecasting

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dc.contributor.advisor Kumar, Bipin en_US
dc.contributor.advisor Chattopadhyay, Rajib en_US
dc.contributor.author ABHISHEK, NAMIT en_US
dc.date.accessioned 2021-11-25T03:41:39Z
dc.date.available 2021-11-25T03:41:39Z
dc.date.issued 2021-08 en_US
dc.identifier.citation 50 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/6390
dc.description.abstract In this work, we use convolutional recurrent neural network-based architectures involving ConvLSTM and ConvGRU for precipitation forecasting over the Indian region. We first compare direct and iterative approach for forecasting and find the iterative approach better for our model. We use multiple variables such as specific humidity, orography, soil moisture and surface pressure as input features for better capturing the underlying dynamics. We also analyse the forecasts over different homogeneous rainfall regions. Finally, we compare ConvLSTM and ConvGRU based models. We find similar performance in both the models, ConvGRU being faster. en_US
dc.language.iso en en_US
dc.subject Machine learning en_US
dc.subject Convolutional Recurrent Neural Networks en_US
dc.subject Medium Range Precipitation Forecasting en_US
dc.subject Spatiotemporal Forecasting en_US
dc.subject Deep Learning en_US
dc.title Application of Convolutional Recurrent Neural Network in Precipitation Forecasting en_US
dc.type Thesis en_US
dc.type.degree BS-MS en_US
dc.contributor.department Dept. of Data Science en_US
dc.contributor.registration 20161023 en_US


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  • MS THESES [1705]
    Thesis submitted to IISER Pune in partial fulfilment of the requirements for the BS-MS Dual Degree Programme/MSc. Programme/MS-Exit Programme

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