The purpose of this study is to identify the exact location of cyclones to avert cyclone-related damage. Knowing about natural disasters like cyclones in advance will help with planning and preparations as they can be extremely dangerous. Numerous methods have been developed in the past to forecast cyclones and gauge their severity. It is a difficult task that demands swiftness and effectiveness. On the INSAT-3D dataset, a hybrid model of CNN and Bidi-rectional GRU is created in this study to estimate the position of the next cyclone. The INSAT-3D satellite pictures’ IR and visible images are analyzed and segmented using K-means clustering. The model’s training time is 400 ms, as observed. There have been comparisons between a variety of prior methods, including CNN-LSTM and a hybrid model that combines CNN and Bidirectional-LSTM. The suggested model’s experimental results show a training loss of 58.6879.
Cyclones are one of the deadliest natural calamities capable of causing immense destruction. Knowing about natural disasters like cyclones in advancehelps in planning and preparations as they can beextremely dangerous. Numerous methods have beendeveloped in the past to track cyclones and gaugetheir severity after eye formation. The purpose ofthis paper is to track the movement of cyclones beforethe eye formation to avert cyclone-related damage ina fast and efficient way. This paper majorly focuseson cyclone forecasting before the formation of the eyewhich is a difficult task and demands effectiveness.The INSAT-3D satellite pictures consisting of IR andvisible images are analyzed and segmented using Detectron. Comparisons have been made between various alternate models, including CNN-LSTM and ahybrid model that combines CNN and BidirectionalLSTM. The model put forth in this paper is a combination of CNN and Bidirectional-GRU. The hybridmodel of CNN and Bidirectional GRU is trained onthe INSAT-3D dataset to estimate the next positionof the cyclone. The suggested model’s experimental results show an MSE of 1613.65 and an SSMI of(1.0,1.0).
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