Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Aamir, Muhammad; Ali, Tariq; Irfan, Muhammad; Shaf, Ahmad; Azam, Muhammad Zeeshan; Głowacz, Adam; Brumerčík, František; Głowacz, W.; Alqhtani, Samar M.; Rahman, Saifur

doi:10.3390/s21082648

Cited by 31 publications

(10 citation statements)

References 52 publications

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“…Major earthquakes and their subsequent geological hazards caused huge damage to natural landscapes and ecosystems, resulting in heavy loss of life and property, ecological degradation, as well as landscape fragmentation [12,52,53]. It has been proven that land-…”

Section: Fragmentation Of Landscape Patternsmentioning

confidence: 99%

Landscape Fragmentation and Spatial Autocorrelation of a Typical Watershed in the Wenchuan Earthquake-Affected Area—A Case Study in the Longxi River Basin

Tian,

Yang,

et al. 2023

Forests

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The 2008 8.0 Ms Wenchuan earthquake caused serious ecological degradation in the northwest of China’s Sichuan Province. Elucidating landscape fragmentation and spatial pattern of post-earthquake landscape is of great interest in order to improve ecological restoration and predict the spatial distribution of damaged ecosystems in earthquake-affected areas. We used four real-time remote sensing images to analyze the landscape pattern characteristics and spatial autocorrelation of the Longxi River Basin before and after the Wenchuan earthquake during the period from 2005 to 2015. In the study period, the degree of landscape fragmentation increased 1 year after the earthquake in 2009 but decreased in 2011 and 2015. The spatial distribution of forest, farmland, and shrub-grassland had significantly positive spatial correlation and the characteristics of spatial aggregation from 2005 to 2015. Construction land had no significant spatial correlation before the earthquake in 2005, but significantly positive spatial correlation after the earthquake, while traffic land had no significant spatial correlation across the study period. Unlike the other landscape types, geological disasters changed from a significantly negative spatial correlation before the earthquake to a significantly positive spatial correlation after the earthquake. However, the positive autocorrelation of all landscape types decreased with the increase of space distance, but in different distance-decay rates. The High-high spatial aggregation areas of geological disasters and construction land clustered gradually upstream of the basin and distributed in Longchi town, respectively; those of farmland distributed in the southeast of the basin increased in 2009 but then decreased, while those of forests had the opposite pattern as the dominant landscape type after the earthquake. Our results elucidated the spatial structure and distribution features of the Longxi River Basin to give a theoretical foundation for assessing the effects of ecological restoration and reconstruction management in earthquake-affected areas.

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Section: Fragmentation Of Landscape Patternsmentioning

confidence: 99%

Landscape Fragmentation and Spatial Autocorrelation of a Typical Watershed in the Wenchuan Earthquake-Affected Area—A Case Study in the Longxi River Basin

Tian,

Yang,

et al. 2023

Forests

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“…A warning system is necessary because it can save lives, protect the environment, and prevent economic damage. Recent devastating floods in Kerala killed people, killed animals, and ruined property, all of which had an impact on the state's economy [ [6] , [7] , [8] ]. An early warning, detection, and rescue system for natural disasters like floods and earthquakes is one possible solution.…”

Section: Introductionmentioning

confidence: 99%

Hybrid deep learning model with enhanced sunflower optimization for flood and earthquake detection

E S,

Thatha,

Mamidisetti

et al. 2023

Heliyon

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“…Aamir et al [6] proposed a model which works in two blocks: The first block consists of a neural network for identifying and detecting the event of a natural disaster. This block ensures to accurately classify the images into the corresponding disaster.…”

Section: Introductionmentioning

confidence: 99%

A disaster classification application using convolutional neural network by performing data augmentation

Sobhana¹,

Ch²,

Indira³

et al. 2022

IJEECS

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Natural disasters are catastrophic events and cause havoc to human life. These events occur in the most unpredictable times and are beyond human control. The aftermath of the disasters is devastating ranging from loss of life to relocation of large groups of the population. With the development in the domains of computer vision (CV) and Image processing, machine learning and deep learning models can integrate images and perform predictions. Deep learning techniques employ many robust techniques and provide significant results even in the case of images. The detection of natural disasters without human intervention requires the help of deep learning techniques. The project aims to employ a multi-layered convolutional neural network (CNN) organization to classify the images related to natural disasters related to earthquakes, floods, cyclones, and wildfires.

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Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Cited by 31 publications

References 52 publications

Landscape Fragmentation and Spatial Autocorrelation of a Typical Watershed in the Wenchuan Earthquake-Affected Area—A Case Study in the Longxi River Basin

Landscape Fragmentation and Spatial Autocorrelation of a Typical Watershed in the Wenchuan Earthquake-Affected Area—A Case Study in the Longxi River Basin

Hybrid deep learning model with enhanced sunflower optimization for flood and earthquake detection

A disaster classification application using convolutional neural network by performing data augmentation

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