[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126779-en":3,"doc-seo-126779-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126779,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Approach for Catastrophe Risk Assessment and Management Using Remote Sensing Data","Remote sensing and geographic information systems are central to catastrophe risk assessment and management for disasters such as earthquakes, landslides, and floods. When the right methods for organizing large multi-source datasets are unavailable, reliable map generation and analysis become impractical. This study applies machine learning for identifying and assessing areas affected by floods, earthquakes, avalanches, landslides, and wildfires. After enhancing imagery with filters, it segments images using thresholding and performs both supervised and unsupervised classifications. Data are collected before and after events and processed using Python-based tools, ArcGIS, ERDAS, and QGIS to analyze damage.","Machine Learning Approach for Catastrophe Risk Assessment and Management Using Remote Sensing  \nData  \nNita Nimbarte1, Bharati Masram1, Archana Tiwari, Sanjay Balamwar  \n1Department of Electronics & Telecommunication Engineering  \nYeshwantrao Chavan College of Engineering, Nagpur  \nIndia  \n[nitangp@gmail.com](nitangp@gmail.com), [bharatimasram@gmail.com](bharatimasram@gmail.com)  \n2Department of Electronics Engineering ,  \nShri Ramdeobaba College of Enginering Nagpur,  \nIndia  \ne-mail: [tiwariar@rknec.edu](tiwariar@rknec.edu)  \n3Maharashtra Remote Sensing and Applications Centre (MRSAC)  \nVNIT Campus,  \nNagpur, India  \n[Sanjay.balamwar@mrsac.maharashtra.gov.in](Sanjay.balamwar@mrsac.maharashtra.gov.in)  \nAbstract—With emergency programs for disaster preparedness and warning phases for earthquakes, landslides, and floods in recent years, remote sensing and geographic information systems have played a crucial role in Catastrophe Risk assessment and management. It has also been a key focus in the field of technology. Without the right tool for organizing massive volumes of data and gathering information from many sources, such maps or measurement channels, it would not be feasible to employ sensory data. In order to identify and assess areas affected by floods, earthquakes, avalanches, landslides, and wildfires, this study employs machine learning approaches. Following the application of filters to enhance image quality, the images are segmented through thresholding technique and classified using supervised and unsupervised classification methods. Images from before and after disasters are gathered from MRSAC Nagpur and processed using Python-based tools, ArcGIS, ERDAS, and QGIS for the purpose of analyzing devastation.  \nKeywords-Catastrophe Assessment, Remote Sensing, Earthquake, Flood, Landslide, Avalanche, Wildfire.  \nI. INTRODUCTION  \nDisasters cause various types of devastation to people and property all around the world. Higher population strain on the earth's resources has resulted in increased susceptibility of humans and their facilities to environmental hazards that have always existed. Earthquakes, floods, landslides, and forest fires that occur often must be examined using today's advance technology in order to identify efficient preventive methods. Better future scenario projections, identification of catastropheprone locations, location of protective measures and safe alternate routes, and other uses of space technology can aid disaster prevention. Satellite data collected after a disaster aids in disaster recovery and the damage claim procedure [1-2] .  \nBinary Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), and Decision Tree Classifierare presented by M. M. A. Syeed et al. [3] . A comparison research was conducted to ascertain the model that gives the maximum accuracy. T. Sharma et al. [4] reviewed several papers and find that there are algorithms like SVM, Regression, Random Forest techniques, Neural Networks, Bayesian  \nNetworks, and so on, with Random Forest and Neural Networks doing better than the others. There are several websites and sources that provide rainfall data for Indian states. This review report focused on three states: Uttar Pradesh, Bihar, and Kerala. According to B. Li et al. analysis [5], MODIS images with a 250 m resolution were utilized to monitor the area that was submerged and the dynamic changes that occurred in the flooding area between 2000 and 2010. The data showed that the area flooded by Poyang Lake in 2010 was more than in any other year. Between 2000 and 2010, Poyang Lake's inundated area grew, whereas the east and southwest Dongting Lakes exhibited a clear downward trend. Karamat Ali et al. [6] demonstrate the various parameters of flood risk assessment. Assessment steps are as area description, calculating intensity and hazard level, assessing vulnerability. Also reported advances in remote sensing, geographic information systems (GIS) and hydraul","cbCaip88ZgGFQKmf","https://ap.wps.com/l/cbCaip88ZgGFQKmf","pdf",1412691,1,9,"English","en",105,"# Introduction\n## Motivation and need for advanced technologies\n# Related Work\n## Machine learning methods for catastrophe and disaster applications","[{\"question\":\"What role do remote sensing and GIS play in catastrophe risk management?\",\"answer\":\"They support catastrophe risk assessment and management, especially during disaster preparedness and warning phases for events like earthquakes, landslides, and floods.\"},{\"question\":\"How does the study process remote sensing images before classification?\",\"answer\":\"It first applies filters to enhance image quality, then segments images using a thresholding technique.\"},{\"question\":\"Which machine learning classification approaches are used in the workflow?\",\"answer\":\"The study uses both supervised and unsupervised classification methods after segmentation.\"}]","Machine Learning Approach for Catastrophe Risk Assessment and Management Using Remote Sensing Data | 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