Disaster-Based Geographical Region Analysis Using Climate Change Detection Using Deep Learning Algorithm

被引：0

作者：

P. K. Swapna ^{[1
]}

Rahul Ganpat Mapari ^{[2
]}

Elangovan Muniyandy ^{[3
]}

Jacquline Tham ^{[4
]}

M. Sandra Carmel Sophia ^{[5
]}

Ritwik Haldar ^{[1
]}

机构：

[1] Department of English, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vaddeswaram, Guntur

[2] Electronics and Telecommunication Engineering, Pimpri Chinchwad College of Engineering and Research, Haveli, Pune

[3] Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai

[4] Applied Science Research Center, Applied Science Private University, Amman

[5] Management and Science University, Shah Alam, Selangor

[6] Department of Electronics and Communication Engineering, Haldia Institute of Technology, West Bengal, Haldia

来源：

Remote Sensing in Earth Systems Sciences | 2025年 / 8卷 / 2期

关键词：

Climate change detection; Deep learning model; Fuzzy encoder; Geographical region changes; U-net component analysis;

D O I：

10.1007/s41976-025-00216-5

中图分类号：

学科分类号：

摘要：

An increasingly important field of research has emerged in recent decades: the interaction between urban infrastructure, human lives, and natural disasters. Analysis and reaction to natural disasters have made extensive use of deep learning (DL) techniques using semantic segmentation networks. The aim of this research is to develop novel method in disaster-based geographical region changes and climate change detection utilising DL model. Here, input is collected as disaster-based geographical region and climate change dataset as well as processed for noise removal and smoothening. Then, this image feature has been extracted and classified using watershed U-net component analysis with Hopfield variational fuzzy encoder neural networks. The fundamental component of the suggested approach is a deep learning procedure that makes it possible to identify soccer fields and analyse helicopter landing sites. The technique uses satellite photos and provided geographic data to train a deep learning autoencoder. Experimental analysis has been carried out in terms of classification accuracy, specificity, sensitivity, ROC, and F-measure. The proposed technique obtained 96% of classification accuracy, 94% of sensitivity, 98% of specificity, 95% of ROC, and 97% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

引用

页码：555 / 565

页数：10

共 17 条

[1]

Yang L., Ji X., Li M., Yang P., Jiang W., Chen L., Li Y., A comprehensive framework for assessing the spatial drivers of flood disasters using an optimal parameter-based geographical detector–machine learning coupled model, Geosci Front, 15, 6, (2024)

[2]

Wang H., Xu S., Xu H., Wu Z., Wang T., Ma C., Rapid prediction of urban flood based on disaster-breeding environment clustering and Bayesian optimized deep learning model in the coastal city, Sustain Cities Soc, 99, (2023)

[3]

Albahri A.S., Khaleel Y.L., Habeeb M.A., Ismael R.D., Hameed Q.A., Deveci M., Alzubaidi L., A systematic review of trustworthy artificial intelligence applications in natural disasters, Comput Electr Eng, 118, (2024)

[4]

Tang J., Wang J., Li J., Zhao P., Lyu W., Zhai W., Yang C., Predicting human mobility flows in response to extreme urban floods: a hybrid deep learning model considering spatial heterogeneity, Comput Environ Urban Syst, 113, (2024)

[5]

Akhyar A., Zulkifley M.A., Lee J., Song T., Han J., Cho C., Hong B.W., Deep artificial intelligence applications for natural disaster management systems: a methodological review, Ecol Indic, 163, (2024)

[6]

Huang W., Ding M., Li Z., Yu J., Ge D., Liu Q., Yang J., Landslide susceptibility mapping and dynamic response along the Sichuan-Tibet transportation corridor using deep learning algorithms, CATENA, 222, (2023)

[7]

Ali S.A., Parvin F., Anjum R., GIS-based disaster risk analysis of floods using certainty factor (CF) and its ensemble with deep learning neural network (DLNN): a case study of Dima Hasao District of Assam, India, Emerging technologies for water supply, conservation and management, pp. 209-234, (2023)

[8]

Volke I., M Abarca-Del-Rio, Cost-effective disaster-induced land cover analysis: a semi-automatic methodology using machine learning and satellite imagery, Int J Remote Sens, 45, 2, pp. 279-305, (2024)

[9]

Ashifa K.M., Babu J., Safaei M., Arumugam T., Environmental impact assessment for spatial data analysis in disaster management using machine learning multi-criteria resources, Remote Sens in Earth Syst Sci, pp. 1-11, (2024)

[10]

Ren F., Isobe K., Landslide risk prediction and regional dependence evaluation based on disaster history using machine learning and deep learning, In International Symposium on Construction Resources for Environmentally Sustainable Technologies, pp. 57-67, (2023)

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