Abstract
Satellite images are primary data in weather prediction modeling. Deep learning-based approach, a viable candidate for automatic image processing, requires large sets of annotated data with diverse characteristics for training purposes. Accuracy of weather prediction improves with data having a relatively dense temporal resolution. We have employed interpolation and data augmentation techniques for enhancement of the temporal resolution and diversifications of characters in a given dataset. Algorithm requires classical approaches during preprocessing steps. Three optical flow methods using 14 different constraint optimization techniques and five error estimates are tested here. The artificially enriched data (optimal combination from the previous exercise) are used as a training set for a convolutional neural network to classify images in terms of storm or nonstorm. Several cyclone data (eight cyclone datasets of a different class) were used for training. A deep learning model is trained and tested with artificially densified and classified storm data for cyclone classification and locating the cyclone vortex giving minimum 90% and 84% accuracy, respectively. In the final step, we show that the linear regression method can be used for predicting the path.
Highlights
Image data are downloaded from the India Meteorological Department (IMD) archive [50] for the cyclone in June 2007
A depression area was declared by IMD near east-southeast of Kakinada, Andhra Pradesh, India
3) If the high-frequency dataset is not available, RetinaNet is a better model than long short-term memory (LSTM)
Summary
R S APPLICATIONS, mainly, via satellite imagery has expanded from conventional meteorology, geological exploration, oceanography toward homeland security, urban planning, ecology and several other novel unconventional fields. Costeffective unmanned aerial vehicle (UAV) and weather balloon approaches have shared the burden but suffer from limitations such as 1) low elevation point for imaging, 2) stability issues under bad weather conditions, and 3) dependence on navigation satellites. Meteorological applications, especially weather forecasting for disaster readiness, yet, requires dedicated but costly satellite infrastructure. Manuscript received November 3, 2019; revised December 31, 2019; accepted January 18, 2020. Date of publication January 31, 2020; date of current version March 2, 2020. Date of publication January 31, 2020; date of current version March 2, 2020. (Corresponding author: Snehlata Shakya.)
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