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• W4385212801 abstract "Digital technology has enhanced every sector of science and technology. There are many sectors, in which digital communication can be used as a primary key for performing operations. Digital communication can be used, in a positive way as well as in a negative way. Digital communication and arms and ammunition utilized in war, chemical industries, textile industries, etc. reduce the human effort required for communication. However, there are a lot of disadvantages of digital communication and arms and ammunition used in war and the chemical industry, viz. (i) destruction of soil and land, (ii) air, (iii) water, and the environment. This increases the carbon monoxide, carbon dioxide, reduces the oxygen level, and damages the ozone layer. This results in the generation of random storms through air, water, and soil, i.e., earthquakes. There is a need to resolve such an issue using conventional weather forecasting and modern techniques used for the prediction of weather using deep learning and AIML techniques. Conventional weather forecasting is far more straightforward to implement and has not been reliable enough for the prediction of an occurrence of a storm. The reason for this sheer complexity of predicting storms lies in the origin of storm forecasting, which begins with analyzing multiple geographical factors that occur in the sea waters near the coastline of continents. The oceanographic processes, which are equipped with complex dynamic mechanisms, are unpredictable due to the influence of many geographical parameters. In this research work, to predict the storm and its severity, a comparative study of random forest and SVM algorithm is being used. To evaluate this work, several parameters are used, viz. (i) crop loss, (ii) fatalities, (iii) property losses, etc. Based on these parameters, precision, recall, and accurate weather forecasting can be done using deep learning algorithms. The paper also explains the comparative study between the various machine learning algorithms considered for storm prediction." @default.
• W4385212801 created "2023-07-25" @default.
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• W4385212801 date "2023-01-01" @default.
• W4385212801 modified "2023-10-14" @default.
• W4385212801 title "Comparison of Machine Learning Algorithms Based on Damage Caused by Storms" @default.
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• W4385212801 doi "https://doi.org/10.1007/978-981-99-3315-0_48" @default.
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