Тип публикации: статья из журнала

Год издания: 2024

Идентификатор DOI: 10.3390/su16167063

Ключевые слова: landslides, climate change adaptation, disaster risk reduction, extreme weather, climate impact, climate variability, mL, classification, Deep Neural Networks, genetic algorithm

Аннотация: <jats:p>This study presents a method for classifying landslide triggers and sizes using climate and geospatial data. The landslide data were sourced from the Global Landslide Catalog (GLC), which identifies rainfall-triggered landslide events globally, regardless of size, impact, or location. Compiled from 2007 to 2018 at NASA GoddПоказать полностьюard Space Flight Center, the GLC includes various mass movements triggered by rainfall and other events. Climatic data for the 10 years preceding each landslide event, including variables such as rainfall amounts, humidity, pressure, and temperature, were integrated with the landslide data. This dataset was then used to classify landslide triggers and sizes using deep neural networks (DNNs) optimized through genetic algorithm (GA)-driven hyperparameter tuning. The optimized DNN models achieved accuracies of 0.67 and 0.82, respectively, in multiclass classification tasks. This research demonstrates the effectiveness of GA to enhance landslide disaster risk management.</jats:p>

Журнал: Sustainability

Выпуск журнала: Т. 16, № 16

ISSN журнала: 20711050

• Tynchenko Yadviga (Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)
• Kukartsev Vladislav (Department of Information Economic Systems, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Tynchenko Vadim (Information-Control Systems Department, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Kukartseva Oksana (Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)
• Panfilova Tatyana (Department of Technological Machines and Equipment of Oil and Gas Complex, Siberian Federal University, 660041 Krasnoyarsk, Russia)
• Gladkov Alexey (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)
• Nguyen Van (Institute of Energy and Mining Mechanical Engineering—Vinacomin, Hanoi 100000, Vietnam)
• Malashin Ivan (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

• Ядро РИНЦ (eLIBRARY.RU)