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Geomorphological evidence inconvenient for the antecedent rivers of the Arun and Tista across the Himalayan range

Journal of Mountain Science 2024年第9期21卷 3001-3017页

作者： NAKATA Takashi KUMAHARA Yasuhiro naik sambit prasanajit SAKAI Harutaka Graduate School of Humanities and Social Sciences Hiroshima UniversityHigashi-Hiroshima 739-8522Japan Active Fault and Earthquake Hazard Mitigation Research Center Pukyong National UniversityBusan 48513South Korea Department of Petroleum Engineering and Earth Sciences University of Petroleum and Energy StudiesDehradun 248007India Graduate School of Science Kyoto UniversityKyoto 606-8502Japan

The Arun and Tista Rivers,which flow across the Himalayas,are commonly known as antecedent valleys that overcame the rapid uplift of the Higher Himalayan *** clarify whether the idea of antecedent rivers is acceptable,we investigated the geomorphology of the Himalayas between eastern Nepal and Bhutan *** southern part of Tibetan Plateau,extending across the Himalayas as tectonically un-deformed glaciated terrain named as'Tibetan Corridor,'does not suggest the regional uplift of the Higher *** 8,000-m class mountains of Everest,Makalu,and Kanchenjunga are isolated residual peaks on the glaciated terrain composed of mountain peaks of 4,000–6,000 m *** Tibetan glaciers commonly beheaded by Himalayan glaciers along the great watershed of the Himalayas suggest the expansion of Himalayan river drainage by *** the narrow upstream regions of the Arun and Tista Rivers with less precipitation behind the range,it is hard to collect enough water for the power of down-cutting their channels against the uplifting *** fission track ages of the Higher Himalayan Crystalline Nappe suggest that the Himalayas attained their present altitude by 11–10 Ma,and the Arun and Tista Rivers formed deep gorges across the Himalayas by headward erosion.

关键词： Himalaya Antecedent river Geomorphology AW3D30 DSM Glacier piracy

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

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Geoscience Frontiers 2021年第3期12卷 541-556页

作者： Ratiranjan Jena Biswajeet Pradhan sambit prasanajit naik Abdullah M.Alamri The Center for Advanced Modeling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneyNSW 2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209Neungdong-roGwangin-guSeoul 05006Republic of Korea Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKMBangiSelangorMalaysia Active Fault and Earthquake Hazard Mitigation Research Institute Pukyong National UniversityBusan 48513South Korea Department of Geology&Geophysics College of ScienceKing Saud UniversityRiyadh 11451Saudi Arabia

Earthquake prediction is currently the most crucial task required for the probability,hazard,risk mapping,and mitigation *** prediction attracts the researchers'attention from both academia and ***,the risk assessment approaches have used various traditional and machine learning ***,deep learning techniques have been rarely tested for earthquake probability ***,this study develops a convolutional neural network(CNN)model for earthquake probability assessment in NE *** conducts vulnerability using analytical hierarchy process(AHP),Venn's intersection theory for hazard,and integrated model for risk mapping.A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine *** classification results and intensity variation were then used for probability and hazard mapping,***,earthquake risk map was produced by multiplying hazard,vulnerability,and coping *** vulnerability was prepared by using six vulnerable factors,and the coping capacity was estimated by using the number of hospitals and associated variables,including budget available for disaster *** CNN model for a probability distribution is a robust technique that provides good *** show that CNN is superior to the other algorithms,which completed the classification prediction task with an accuracy of 0.94,precision of 0.98,recall of 0.85,and F1 score of *** indicators were used for probability mapping,and the total area of hazard(21,412.94 km^(2)),vulnerability(480.98 km^(2)),and risk(34,586.10 km^(2))was estimated.

关键词： Earthquake Convolutional neural network Geospatial information systems Hazard Vulnerability Risk North-East India

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