Maritime Environmental Disaster Management Using Intelligent Techniques

Akyüz, Emre; İ̇lbahar, Esra; Çebı, Selçuk; Celik, Metin

doi:10.1007/978-3-319-42993-9_7

Cited by 7 publications

(5 citation statements)

References 66 publications

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“…So, it is greatest hit with neural networks by adaptive patterns techniques. This is many applications related adaptive patterns using in remote sensing imagery like impacts of environment and disasters (Akyuz et al 2017), slope susceptibility and deforestation (Gayen et al 2019). DNNs has experts learning systems for capability of spatial patterns within identify unknown patterns by image based training model (Basheer and Hajmeer 2000).…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: A novel model of the deep neural network approach in coal mining surface pattern to assess land use classification using remote sensing image

Kumar

Das²

2022

Preprint

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Computer vision usage in coal mining land use (LU) pattern classification is an exigent application for accurate accuracy. In the context of the satellite image pattern classification, the artificial intelligence (AI) for the development of a deep neural network (DNN), we have expressed an interest in land use (LU) for coal mining activities. Also, this image is addressed an area of interest as a spatial feature pattern that characterises coal mining regions. Moreover, the DNN classification algorithm's performance depends on the quality of the dataset. In the satellite image dataset, these practices of supervised-based learning are used for the accuracy assessment of mining activities area that is categorized into five classes coal area, built-up area, barren area, vegetation area, and water area respectively. The suit of mining activities area is selected from a case study of Talcher, Odisha, India. Further, we have found performances of training, testing, and validation like 88%, 69.7%, and 73.6%, respectively. Also, the overall accuracy is 79.4%. Therefore, the potential of DNNs learning is introduced for LU classification over mining activities area.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: A novel model of the deep neural network approach in coal mining surface pattern to assess land use classification using remote sensing image

Kumar

Das²

2022

Preprint

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“…In this paper, the authors propose to utilise Data-Driven Models (DDMs) (Oneto, 2019;Shalev-Shwartz & Ben-David, 2014;Vapnik, 1998) to create models exploiting robust statistical inference procedures, and historical data to perform unbiased predictions on previously unseen cases. Recently, DDMs have proved to be valid instruments for transport related problems (Cipollini et al, 2018;Coraddu et al, 2017;Iranitalab & Khattak, 2017;Osman et al, 2019;Petersen et al, 2012;Smith et al, 2013) and, in some preliminary works (Akyuz et al, 2017;Grech et al, 2002;Hänninen, 2014;Lützhöft, 2004;Praetorius et al, 2015;Tirunagari et al, 2012), it has been shown that DDMs applied to the study of the human factor in accident analysis may produce interesting results.…”

Section: Introductionmentioning

confidence: 99%

Determining the most influential human factors in maritime accidents: A data-driven approach

et al. 2020

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Marine accidents are complex processes in which many factors are involved and contribute to accident development. For this reason, effectively analyse what combination of factors lead an accident event is a complex problem, especially when human factors are involved. State-of-the-art methods such as Human Factor Analysis and Classification System, Human reliability Assessments, and simple Statistical Analysis are not effective in many situations since they require the intervention of human experts with their limitations, biases, and high costs. The authors propose to use a data-driven approach able to utilise the information present in historical databases of marine accident for the purposes of establishing the most influential human factors. For this purpose a two-stage approach is presented: first, a data-driven predictive model is built able to predict the type of accident based on the contributing factors, and then the different contributing factors are ranked based on their ability to influence the prediction. Results on a real historical database of accidents provided by the Marine Accident Investigation Branch will support the proposed novel approach.

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“…erefore, in this paper, the aim is to examine some of the latest advancements that have been made in the search and rescue technologies using autonomous vehicles and algorithms like hex-path algorithm and planarian algorithm for path optimization. However, the paper [38] considered the oil spill, heavy rain, and collisions as the most disastrous, which cause a critical impact on marine life. e authors have considered an intelligent system consisting of a model base, environmental disaster modeling, decision support, and intelligence techniques like heuristic search algorithms and machine learning to strengthen the operating mechanism to eliminate the potential impacts.…”

Section: Previous Studiesmentioning

confidence: 99%

A Comprehensive Review on Design of Autonomous Robotic Boat for Rescue Applications

Jain

Saini

Gupta

et al. 2021

Mathematical Problems in Engineering

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New technologies are advancing and emerging day by day to improve the safety of humans by making use of various autonomous technologies. The continuous utilization of autonomous vehicles/systems in search and rescue (SAR) operations is a challenging research area particularly for marine-based activities. A comprehensive systematic literature review (SLR) providing an overview of improvisations that have been done in the field of autonomous technologies for search and rescue operation over the last five years has been compiled in this paper. A methodology for using autonomous vehicles in water for SAR operations has been incorporated and demonstrated. The focus of this study is to look at the various techniques and address different challenges faced for human beings’ safety during rescue operation. The comparison of results achieved for various technologies and algorithms is highlighted in this paper. This literature survey proves to be a good source of information for fellow researchers to precisely analyze the study results.

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Maritime Environmental Disaster Management Using Intelligent Techniques

Cited by 7 publications

References 66 publications

WITHDRAWN: A novel model of the deep neural network approach in coal mining surface pattern to assess land use classification using remote sensing image

WITHDRAWN: A novel model of the deep neural network approach in coal mining surface pattern to assess land use classification using remote sensing image

Determining the most influential human factors in maritime accidents: A data-driven approach

A Comprehensive Review on Design of Autonomous Robotic Boat for Rescue Applications

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