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Fidelity of AI Model in Representing the Different Types of Cyclone Track Estimation

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Fidelity of AI Model in Representing the Different Types of Cyclone Track Estimation International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 08 Issue: 08 | Aug 2021 www.irjet.net p-ISSN: 2395-0072 Fidelity of AI Model in Representing the Different Types of Cyclone Track Estimation Smruti Vyavahare1, Dr. H.K.Khanuja2 1Student, Dept. of Technology, SPPU, Maharashtra, India 2Head of Dept., Dept. of Computer Engineering, MMCOE, Pune, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - Tropical cyclones have devastating effects on Cyclonic Route followed by traditional methods such as human life and the natural environment and cause great Arabian Sea - Mumbai - Ratnagiri- Gujarat. economic losses. There are traditional methods such as numerical weather forecast models for tracking cyclones, The super cyclone storm Amphan was a very strong, but these are complex, require high computing power,are powerful and deadly tropical cyclone that caused time consuming, and are costly. In today’s era of remote widespread damage in the East Indies, particularly in West Bengal and Odisha. This cyclone formed in the Bay of sensing, satellites and radars are widely used to collect Bengal (BoB) followed by the traditional method as the weather data in the form of grids and images. In this study, Bay of Bengal–West Bengal–Odisha. this visual imagery will be used to predict the trajectory of cyclones that have formed over the Arabian Sea (AS) and Visual imaging data is used to predict the trajectory of the Bay of Bengal (BoB) using the Artificial Intelligence cyclones that have formed over the Arabian Sea (AS) and Convolutional Neural Network (CNN) technique. The the Bay of Bengal (BoB) using the Artificial Intelligence cyclone trajectory simulated by the traditional PNT and AI Convolutional Neural Network (CNN) technique simulated models was compared to the observation trajectory to see by traditional NWP and AI models. with observation the accuracy of the AI models for the two different cases of tracking to see the accuracy of the AI models for the two cyclones formed. across two different seas, i.e. AS and BoB, different cases of cyclones formed in two different seas, i.e. which have different characteristics and opposite cyclone AS and BoB with different characteristics and opposite traces. cyclone traces. Key Words: Convolutional Neural Network (CNN), The effects and devastation of the cyclones are the sea Numerical Weather Prediction(NWP), Cyclones, Linear brought into the city by the strong cyclone winds, the Regression, Data Analysis, Global Forecast system (GFS), waves rustle with fear, the church roofs are broken and European centre for Medium range Weather forecast the huge stones carry enormous distances, two thousand (ECMWF), Meteorological and Oceanographic Satellite people died. This shows cyclones. They have economic and Data Archival Center (MOSDAC). social effects that can be reduced by using better forecasting systems and accurate estimates of the course of cyclones. This explores the importance of artificial 1.INTRODUCTION intelligence techniques in predicting cyclone trail estimation, which is time efficient, cheaper and simpler Cyclone is the weather phenomenon, The System of winds compared to conventional numerical weather forecasting rotating inwards to an area of the low barometric (NWP). pressure, with the anticlockwise or fierce rotation. Cyclones are classified on basis of the wind speed as 2. LITERATURE SURVEY follow:- Deep Depression ( 50-61 km/h), Cyclonic Storm ( 62-88 Tropical cyclones (TC) are considered to be extreme km/h), Severe Cyclonic Storm (89-117 km/h), Very Severe climatic events with strong winds, storms and floods that Cyclonic Storm(118-166 km/h), Extremely Severe can cause great damage to coasts around the world. Over Cyclonic Storm (166-221 km/h), Super Cyclonic Storm (> the past century, numerous meteorologists and warning 222km/h). centers have devoted themselves to researching advances in observation technology,the interactions of the In this paper two cyclones discussed, they are:- atmospheric environment, the atmospheric boundary layer and the air-sea interface as well as forecasting 1. Cyclone Nisarga 2. Cyclone Amphan techniques. However, there are many problems with the remaining forecasting capabilities, particularly with Cyclone Nisarga was a strong cyclone current forecasting the eruption, intensity and risk of the tropical (SCS) with a high wind speed of 110 km / hr near cyclone. Ratnagiri on June 2, 2020 and was the strongest tropical cyclone to hit the Indian state of Maharashtra in June since In general, the most popular dynamic tropical cyclone 1891. It was formed in the Arabian Sea (AS). Nisarga forecast models have relatively poor accuracy, an incomplete representation of complex physical processes, © 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 369 International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 08 Issue: 08 | Aug 2021 www.irjet.net p-ISSN: 2395-0072 and approximate resolution. There are few studies Many places around the world are exposed to tropical showing that the lack of representations of air-sea energy cyclones and the associated storm flow. Despite huge exchange at very high wind speeds prevents more efforts, a great number of people pass away each year as a effective simulation of CT intensity. result of the cyclone events. To lighten damage, the improved prediction techniques must be developed. The Satellite imagery is key data for weather forecast technique presented uses artificial neural networks to modeling. With the deep learning approach, the automatic interpret NOAA-AVHRR satellite imagery data. A multi- image processing requires large data sets, which are layer neural network, favouring the human visual system, explained with the various properties for training was trained to forecast the movement of cyclones based purposes. The accuracy of the weather forecast is best on the satellite imagery data. The trained network with data with a relatively dense temporal resolution. produced correct directional forecasts for 98% of test There are three optical flow methods using 14 different images, that shows a good generalization capability. constraint optimization techniques, and the five error Future work will include extension of the present network estimates are tested here.Cyclone data sets of another to handle the wide range of cyclones and to take into class) were used for training. The artificially enriched data account supplementary information, such as wind speeds, (optimal combination from the previous exercise) are humidity, air pressure and water temperature. used as a training set for a neural convolution network to classify images in terms of thunderstorms or no clouds. The probability of using air temperature and moisture profiles for the resolution of pressure in the tropical A strong tropical cyclone can turn into a typhoon or cyclones (TC). The Tropical Cyclone middle pressure hurricane, a very devastating and unstoppable natural values calculated from the satellite data were compared disaster that is responsible for death and property every with the estimates of the Japan Meteorological Agency. year. For example, in 2017 alone, there were eight The accuracy of an air temperature and moisture profile typhoons that landed in China, affecting around 6 million renewal from the radiometer data is verified using the people and scarring around $ 5 billion in financial direct radiosonde measurements together with the terms.Typhoons have multiplied, making tropical cyclone weather station data for the Northwest Pacific. The forecasting even more valuable and citizens and comparison with the estimates of a Joint Typhoon governments accept that they are better prepared when Warning Center (USA) demonstrates bad agreement. faced with such a calamity. Seascape, the coastline of the marine areas and the landscape of the inland areas. In A convolutional neural network (CNN) and Deep Learning addition, when a tropical cyclone forms, it can be method was evolved to predict the movement direction of influenced by a number of factors, including the tropical cyclones or typhoons over the Northwestern meteorological environment, thermodynamics, and Pacific basin from Himawari-8 (H-8) satellite imagery kinetics of the tropical cyclone system.All of the difficult data. problems make predicting the course of tropical cyclones a major challenge. Given the impact of tropical Cyclones in Predicting the course of a tropical cyclone is critical to the society and the complexity of their prediction, it is safety of people and property. Even though dynamic therefore important to research and apply new techniques predictive models can provide very accurate short term for forecasting the course of tropical cyclones. There are predictions and are computationally intensive, current many studies that use artificial intelligence techniques to statistical predictive models offer plenty of room for detect typhoons, cyclones, and hurricanes around the improvement as the database of past hurricanes is world; Few studies have highlighted so far, but smaller or constantly growing and compound relationships have only no studies found that examined the two different recently been tested for this application. A new proposal is categories and different trajectory of cyclones over the a neural network model that brings together data from
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