التفاصيل البيبلوغرافية
| العنوان: |
Integrating Harris Hawks optimization and TensorFlow deep learning for flash flood susceptibility mapping using geospatial data. |
| المؤلفون: |
Tinh, Le Duc1 (AUTHOR), Thao, Do Thi Phuong1 (AUTHOR), Bui, Dieu Tien2 (AUTHOR), Trong, Nguyen Gia1,3 (AUTHOR) nguyengiatrong@humg.edu.vn |
| المصدر: |
Earth Science Informatics. Aug2024, Vol. 17 Issue 4, p3397-3412. 16p. |
| مصطلحات موضوعية: |
*ARTIFICIAL neural networks, *FLOOD forecasting, *NATURAL disasters, *DATABASES, *BASIC needs, *DEEP learning |
| مستخلص: |
Flash floods are recognized as some of the most devastating natural disasters globally, causing significant damage to socio-economic infrastructures, ecosystems, and human lives, thus highlighting the critical need for accurately identifying areas at risk. In order to address this challenge, our study introduces a novel approach by integrating Harris Hawks Optimization (HHO) with the TensorFlow Deep Neural Network (TFDNN), termed HHO-TFDNN, for assessing flash flood susceptibility. The innovation of HHO-TFDNN resides in its dual structure: TFDNN is employed to develop flash flood prediction models, while HHO is utilized to optimize their parameters. This methodology was applied to a region in northern Vietnam, frequently impacted by flash floods. A detailed flash flood database was assembled using various geospatial data sources for the model's training and validation. The results underscore the model's exceptional predictive accuracy, demonstrated by a high F-score of 0.913, a Kappa statistic of 0.825, and an overall accuracy of 91.2%. These findings establish HHO-TFDNN as a highly effective tool for predictive modeling in flash flood management. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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