A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin Technology
Flood is regarded as common disaster which could cause serious devastation in any country. Typically, it is caused due to precipitation & river runoffs, specifically at the time of excessive rainfall season. The technology of sensor network has been used to monitor changes in landcovers and...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10959713/ |
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| author | Se-Jung Lim K. Sakthidasan Sankaran Anandakumar Haldorai |
| author_facet | Se-Jung Lim K. Sakthidasan Sankaran Anandakumar Haldorai |
| author_sort | Se-Jung Lim |
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| description | Flood is regarded as common disaster which could cause serious devastation in any country. Typically, it is caused due to precipitation & river runoffs, specifically at the time of excessive rainfall season. The technology of sensor network has been used to monitor changes in landcovers and water level fluctuations. Moreover, effective disaster monitoring & notification system in real-time becomes a crucial part which needs to be overcome. For this reason, the proposed methodology is designed aiming at developing natural disaster prediction and monitoring system for alerting that aids in offering right decision at right time. At first, remote sensing image data are collected and preprocessed using Frequency Ratio and Multi-collinearity test (MCT) to ensure noise removal and image augmentation by enhancing their quality. A feature extraction process is carried with the use of Deep Convolution VGGNet-16 from which optimal features are selected using Improved Harris Hawks Optimization algorithm (IHHOA). Then, a Flexible Spatio-temporal image fusion (F-SPTF) approach is employed to fuse images. After this, Deep cascaded RNN classifier is employed for predicting flood occurrence and to map flood susceptibility areas. This, in turn, classifies the normal and abnormal condition of flood occurrence thus giving alerts in case of natural disaster occurrences which could be visualized through digital twin technologies. The suggested scheme offers an accuracy rate of about (99.89%), precision (99.37%), recall (99.82%), and <italic>F</italic>1-score (99.74%). The error rates estimated like RMSE (0.784), MAE (0.764), and MAPE (0.102) also seems to be lower than other existing models compared. |
| format | Article |
| id | doaj-art-e1abe8a7b6f74d038cb409b373a8e493 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-e1abe8a7b6f74d038cb409b373a8e4932025-08-20T03:52:43ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118115471156010.1109/JSTARS.2025.355920510959713A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin TechnologySe-Jung Lim0K. Sakthidasan Sankaran1https://orcid.org/0000-0002-5905-0809Anandakumar Haldorai2School of Electrical and Computer Engineering, Yeosu Campus, Chonnam National University, Yeosu-si, South KoreaDepartment of Electronics and Communication Engineering, Hindustan Institute of Technology and Science, Chennai, IndiaDepartment of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, IndiaFlood is regarded as common disaster which could cause serious devastation in any country. Typically, it is caused due to precipitation & river runoffs, specifically at the time of excessive rainfall season. The technology of sensor network has been used to monitor changes in landcovers and water level fluctuations. Moreover, effective disaster monitoring & notification system in real-time becomes a crucial part which needs to be overcome. For this reason, the proposed methodology is designed aiming at developing natural disaster prediction and monitoring system for alerting that aids in offering right decision at right time. At first, remote sensing image data are collected and preprocessed using Frequency Ratio and Multi-collinearity test (MCT) to ensure noise removal and image augmentation by enhancing their quality. A feature extraction process is carried with the use of Deep Convolution VGGNet-16 from which optimal features are selected using Improved Harris Hawks Optimization algorithm (IHHOA). Then, a Flexible Spatio-temporal image fusion (F-SPTF) approach is employed to fuse images. After this, Deep cascaded RNN classifier is employed for predicting flood occurrence and to map flood susceptibility areas. This, in turn, classifies the normal and abnormal condition of flood occurrence thus giving alerts in case of natural disaster occurrences which could be visualized through digital twin technologies. The suggested scheme offers an accuracy rate of about (99.89%), precision (99.37%), recall (99.82%), and <italic>F</italic>1-score (99.74%). The error rates estimated like RMSE (0.784), MAE (0.764), and MAPE (0.102) also seems to be lower than other existing models compared.https://ieeexplore.ieee.org/document/10959713/Deep cascaded RNN (DC-RNN)deep convolution VGGNet-16 (DCVGGNet16)flexible-spatiotemporal image fusion (F-SPTF)floodimproved Harris Hawks optimization algorithm (IHHOA)natural disaster |
| spellingShingle | Se-Jung Lim K. Sakthidasan Sankaran Anandakumar Haldorai A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin Technology IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep cascaded RNN (DC-RNN) deep convolution VGGNet-16 (DCVGGNet16) flexible-spatiotemporal image fusion (F-SPTF) flood improved Harris Hawks optimization algorithm (IHHOA) natural disaster |
| title | A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin Technology |
| title_full | A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin Technology |
| title_fullStr | A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin Technology |
| title_full_unstemmed | A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin Technology |
| title_short | A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin Technology |
| title_sort | framework for flood disaster detection from remote sensing images using spatiotemporal fusion with digital twin technology |
| topic | Deep cascaded RNN (DC-RNN) deep convolution VGGNet-16 (DCVGGNet16) flexible-spatiotemporal image fusion (F-SPTF) flood improved Harris Hawks optimization algorithm (IHHOA) natural disaster |
| url | https://ieeexplore.ieee.org/document/10959713/ |
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