A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes
The prediction of severe weather events such as hurricanes is always a challenging task in the history of climate research, and many deep learning models have been developed for predicting the severity of weather events. When a disastrous hurricane strikes a coastal region, it causes serious hazards...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/9/4129 |
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| author | Jayanthi Devaraj Sumathi Ganesan Rajvikram Madurai Elavarasan Umashankar Subramaniam |
| author_facet | Jayanthi Devaraj Sumathi Ganesan Rajvikram Madurai Elavarasan Umashankar Subramaniam |
| author_sort | Jayanthi Devaraj |
| collection | DOAJ |
| description | The prediction of severe weather events such as hurricanes is always a challenging task in the history of climate research, and many deep learning models have been developed for predicting the severity of weather events. When a disastrous hurricane strikes a coastal region, it causes serious hazards to human life and habitats and also reflects a prodigious amount of economic losses. Therefore, it is necessary to build models to improve the prediction accuracy and to avoid such significant losses in all aspects. However, it is impractical to predict or monitor every storm formation in real time. Though various techniques exist for diagnosing the tropical cyclone intensity such as convolutional neural networks (CNN), convolutional auto-encoders, recurrent neural network (RNN), etc., there are some challenges involved in estimating the tropical cyclone intensity. This study emphasizes estimating the tropical cyclone intensity to identify the different categories of hurricanes and to perform post-disaster management. An improved deep convolutional neural network (CNN) model is used for predicting the weakest to strongest hurricanes with the intensity values using infrared satellite imagery data and wind speed data from HURDAT2 database. The model achieves a lower Root mean squared error (RMSE) value of 7.6 knots and a Mean squared error (MSE) value of 6.68 knots by adding the batch normalization and dropout layers in the CNN model. Further, it is crucial to predict and evaluate the post-disaster damage for implementing advance measures and planning for the resources. The fine-tuning of the pre-trained visual geometry group (VGG 19) model is accomplished to predict the extent of damage and to perform automatic annotation for the image using the satellite imagery data of Greater Houston. VGG 19 is also trained using video datasets for classifying various types of severe weather events and to annotate the weather event automatically. An accuracy of 98% is achieved for hurricane damage prediction and 97% accuracy for classifying severe weather events. The results proved that the proposed models for hurricane intensity estimation and its damage prediction enhances the learning ability, which can ultimately help scientists and meteorologists to comprehend the formation of storm events. Finally, the mitigation steps in reducing the hurricane risks are addressed. |
| first_indexed | 2024-03-10T11:47:21Z |
| format | Article |
| id | doaj.art-9b0ca31233354f5c995513d6187abde6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:47:21Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9b0ca31233354f5c995513d6187abde62023-11-21T17:59:12ZengMDPI AGApplied Sciences2076-34172021-04-01119412910.3390/app11094129A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of HurricanesJayanthi Devaraj0Sumathi Ganesan1Rajvikram Madurai Elavarasan2Umashankar Subramaniam3Department of Information Technology, Sri Venkateswara College of Engineering, Chennai 602117, IndiaDepartment of Information Technology, Sri Venkateswara College of Engineering, Chennai 602117, IndiaClean and Resilient Energy Systems (CARES) Laboratory, Texas A&M University, Galveston, TX 77553, USADepartment of Communications and Networks, Renewable Energy Laboratory, College of Engineering, Prince Sultan University, Riyadh 12435, Saudi ArabiaThe prediction of severe weather events such as hurricanes is always a challenging task in the history of climate research, and many deep learning models have been developed for predicting the severity of weather events. When a disastrous hurricane strikes a coastal region, it causes serious hazards to human life and habitats and also reflects a prodigious amount of economic losses. Therefore, it is necessary to build models to improve the prediction accuracy and to avoid such significant losses in all aspects. However, it is impractical to predict or monitor every storm formation in real time. Though various techniques exist for diagnosing the tropical cyclone intensity such as convolutional neural networks (CNN), convolutional auto-encoders, recurrent neural network (RNN), etc., there are some challenges involved in estimating the tropical cyclone intensity. This study emphasizes estimating the tropical cyclone intensity to identify the different categories of hurricanes and to perform post-disaster management. An improved deep convolutional neural network (CNN) model is used for predicting the weakest to strongest hurricanes with the intensity values using infrared satellite imagery data and wind speed data from HURDAT2 database. The model achieves a lower Root mean squared error (RMSE) value of 7.6 knots and a Mean squared error (MSE) value of 6.68 knots by adding the batch normalization and dropout layers in the CNN model. Further, it is crucial to predict and evaluate the post-disaster damage for implementing advance measures and planning for the resources. The fine-tuning of the pre-trained visual geometry group (VGG 19) model is accomplished to predict the extent of damage and to perform automatic annotation for the image using the satellite imagery data of Greater Houston. VGG 19 is also trained using video datasets for classifying various types of severe weather events and to annotate the weather event automatically. An accuracy of 98% is achieved for hurricane damage prediction and 97% accuracy for classifying severe weather events. The results proved that the proposed models for hurricane intensity estimation and its damage prediction enhances the learning ability, which can ultimately help scientists and meteorologists to comprehend the formation of storm events. Finally, the mitigation steps in reducing the hurricane risks are addressed.https://www.mdpi.com/2076-3417/11/9/4129deep learning (DL)hurricanesconvolutional neural network (CNN)visual geometry group (VGG 19)data augmentation (DA) |
| spellingShingle | Jayanthi Devaraj Sumathi Ganesan Rajvikram Madurai Elavarasan Umashankar Subramaniam A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes Applied Sciences deep learning (DL) hurricanes convolutional neural network (CNN) visual geometry group (VGG 19) data augmentation (DA) |
| title | A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes |
| title_full | A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes |
| title_fullStr | A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes |
| title_full_unstemmed | A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes |
| title_short | A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes |
| title_sort | novel deep learning based model for tropical intensity estimation and post disaster management of hurricanes |
| topic | deep learning (DL) hurricanes convolutional neural network (CNN) visual geometry group (VGG 19) data augmentation (DA) |
| url | https://www.mdpi.com/2076-3417/11/9/4129 |
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