Earthquake risk assessment in NE India using deep learning and geospatial analysis

Jena, Ratiranjan; Pradhan, Biswajeet; Naik, Sambit Prasanajit; Alamri, Abdullah

doi:10.1016/j.gsf.2020.11.007

Cited by 45 publications

(13 citation statements)

References 66 publications

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“…To prepare for the occurrence of a hazardous material explosion, Korea’s Korea off-site risk assessment (KORA) quantitatively evaluates safety by taking into account the number of components, population density, and importance of surrounding buildings for the predicted impact range. As such, in previous studies, unlike those for earthquakes [ 17 , 18 , 19 , 20 , 21 ] and floods [ 22 , 23 , 24 , 25 ], risk assessment methods for judging the explosion damage to buildings from an economical point of view and accurate spatial information have not been actively investigated.…”

Section: Literature Reviewmentioning

confidence: 99%

Economic risk assessment of ammonium nitrate explosions at the Busan Port by determining the building damage using a 3D explosion simulation

Lee

Ham²,

Song³

et al. 2022

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Economic risk assessment of ammonium nitrate explosions at the Busan Port by determining the building damage using a 3D explosion simulation

Lee

Ham²,

Song³

et al. 2022

Heliyon

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“…The resulting CR was also 3%, validating the model as a good one for the ranking and weighting system. [28,33,[51][52][53][54][55][56]. It allows the input data to read, analyse, and train up to maximum accuracy compared to any traditional approaches.…”

Section: Pairwise Analysis Of Parametersmentioning

confidence: 99%

“…A contemporary study [28] on earthquake risk assessment has been carried in northeast India. The study focussed on distance from railway, railway density, distance from landuse, landuse density, distance from buildings, and building geospatial density layers.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Multicriteria Analysis for Earthquake Risk Assessment: Case Study of Fujairah City in the UAE

et al. 2021

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A clear understanding of the spatial distribution of earthquake events facilitates the prediction of seismicity and vulnerability among researchers in the social, physical, environmental, and demographic aspects. Generally, there are few studies on seismic risk assessment in United Arab Emirates (UAE) within the geographic information system (GIS) platform. Former researches and recent news events have demonstrated that the eastern part of the country experiences jolts of 3-5 magnitude, specifically near Fujairah city and surrounding towns. This study builds on previous research on the seismic hazard that extracted the eastern part of the UAE as the most hazard-prone zone. Therefore, this study develops an integrated analytical hierarchical process (AHP) and machine learning (ML) for risk mapping considering eight geospatial parameters—distance from shoreline, schools, hospitals, roads, residences, streams, confined area, and confined area slope. Experts’ opinions and literature reviews were the basis of the AHP ranking and weighting system. To validate the AHP system, support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were applied to the datasets. The datasets were split into 60 : 40 ratio for training and testing. Results show that SVM has the highest accuracy of 79.6% compared to DT and RF with a “predicted high” precision of 87.5% attained from the model. Risk maps from both AHP and ML approaches were developed and compared. Risk analysis was categorised into 5 classes “very high,” “high,” “moderate,” “low,” and “very low.” Both approaches modelled relatable spatial patterns as risk-prone zones. AHP approach concluded 3.6% as “very high” risk zone, whereas only 0.3% of total area was identified from ML. The total area for the “very high” (20 km2) and “high” (114 km2) risk was estimated from ML approach.

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“…He designs and implements a medical Q&A system based on deep learning. Jena et al [18] applies deep learning to seismic risk assessment, this study develops a convolutional neural network (CNN) model for earthquake probability assessment in NE India. Vasit et al [19] applies deep learning techniques to agriculture, to develop a raw imagery-based deep learning approach for feld-scale yield prediction.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Damage Assessment System for Large-Scale Surface Warships Based on Deep Learning

Duan

Yin

Wang

2022

Mathematical Problems in Engineering

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Artificial intelligence technology and image recognition technology are playing an increasingly important role in information warfare, while battlefield image recognition and information processing are at the heart of information processing in warfare. This research will use deep learning image recognition technology and QT development platform, combined with target damage tree analysis and Bayesian network inference method, to research and develop the design of large-scale surface warships damage assessment system. A large-scale surface warships damage assessment system was designed. The system can quickly identify the target large-scale surface warships type with an accuracy rate of over 91%. On this basis, damage assessment is carried out in terms of target vulnerability, combatant power analysis, and bullet-eye rendezvous. A new damage classification is established. The system can improve the efficiency of large-scale surface warships damage assessment, can be well combined with the front-line information collection pictures to assess, and overcome the traditional large-scale surface warships damage assessment and problems of slow and inaccurate manual processing of raw data. It provides a new way of thinking for large-scale surface warships damage assessment research.

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Earthquake risk assessment in NE India using deep learning and geospatial analysis

Cited by 45 publications

References 66 publications

Economic risk assessment of ammonium nitrate explosions at the Busan Port by determining the building damage using a 3D explosion simulation

Economic risk assessment of ammonium nitrate explosions at the Busan Port by determining the building damage using a 3D explosion simulation

Geospatial Multicriteria Analysis for Earthquake Risk Assessment: Case Study of Fujairah City in the UAE

Design and Implementation of a Damage Assessment System for Large-Scale Surface Warships Based on Deep Learning

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