Sustainable flood risk assessment using deep learning-based algorithms with a blockchain technology
The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) fo...
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| Vydané v: | Geocarto international Ročník ahead-of-print; číslo ahead-of-print; s. 1 - 29 |
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| Hlavní autori: | , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Taylor & Francis
12.08.2022
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| ISSN: | 1010-6049, 1752-0762 |
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| Abstract | The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events. |
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| AbstractList | The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events. |
| Author | Costache, Romulus Sharma, Gitika Chen, Ningsheng Pal, Subodh Chandra Abdullah-Al-Mahbub, Md Rahman, Mahfuzur Islam, Abu Reza Md. Towfiqul Mia, Md. Uzzal Alam, Edris Elbeltagi, Ahmed Islam, H. M. Touhidul Washakh, Rana Muhammad Ali Islam, Md Monirul |
| Author_xml | – sequence: 1 givenname: Md. Uzzal surname: Mia fullname: Mia, Md. Uzzal organization: Department of Disaster Management, Begum Rokeya University – sequence: 2 givenname: Mahfuzur orcidid: 0000-0001-8402-156X surname: Rahman fullname: Rahman, Mahfuzur organization: Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT) – sequence: 3 givenname: Ahmed orcidid: 0000-0002-5506-9502 surname: Elbeltagi fullname: Elbeltagi, Ahmed organization: Agricultural Engineering Dept, Faculty of Agriculture, Mansoura University – sequence: 4 givenname: Md surname: Abdullah-Al-Mahbub fullname: Abdullah-Al-Mahbub, Md organization: Department of Disaster Management, Begum Rokeya University – sequence: 5 givenname: Gitika surname: Sharma fullname: Sharma, Gitika organization: Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology – sequence: 6 givenname: H. M. Touhidul surname: Islam fullname: Islam, H. M. Touhidul organization: Department of Disaster Management, Begum Rokeya University – sequence: 7 givenname: Subodh Chandra surname: Pal fullname: Pal, Subodh Chandra organization: Department of Geography, The University of Burdwan – sequence: 8 givenname: Romulus orcidid: 0000-0002-6876-8572 surname: Costache fullname: Costache, Romulus organization: Danube Delta National Institute for Research and Development – sequence: 9 givenname: Abu Reza Md. Towfiqul orcidid: 0000-0001-5779-1382 surname: Islam fullname: Islam, Abu Reza Md. Towfiqul organization: Department of Disaster Management, Begum Rokeya University – sequence: 10 givenname: Md Monirul surname: Islam fullname: Islam, Md Monirul organization: Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT) – sequence: 11 givenname: Ningsheng surname: Chen fullname: Chen, Ningsheng organization: Academy of Plateau Science and Sustainability – sequence: 12 givenname: Edris surname: Alam fullname: Alam, Edris organization: Department of Geography and Environmental Studies, University of Chittagong – sequence: 13 givenname: Rana Muhammad Ali surname: Washakh fullname: Washakh, Rana Muhammad Ali organization: School of Architecture, Neijiang Normal University |
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