Synergetic use of geospatial and machine learning techniques in modelling landslide susceptibility in parts of Shimla to Kinnaur National Highway, Himachal Pradesh-Reference-Cited by-同舟云学术

Synergetic use of geospatial and machine learning techniques in modelling landslide susceptibility in parts of Shimla to Kinnaur National Highway, Himachal Pradesh

Published:2024-04-18 Issue:3 Volume:10 Page:4163-4183
ISSN:2363-6203
Container-title:Modeling Earth Systems and Environment
language:en
Short-container-title:Model. Earth Syst. Environ.

Author:

Das Rahul^ORCID,Chattoraj Shovan Lal,Singh Mohit,Bisht Ashish

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40808-024-01993-6.pdf

Reference79 articles.

1. Achu AL, Aju CD, Di Napoli M, Prakash P, Gopinath G, Shaji E, Chandra V (2023) Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis. Geosci Front 14(6):101657. https://doi.org/10.1016/j.gsf.2023.101657

2. Ahmadlou M, Al-Fugara A, Al-Shabeeb AR, Arora A, Al-Adamat R, Pham QB, Al-Ansari N, Linh NTT, Sajedi H (2021) Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks. J Flood Risk Manag 14(1):e12683. https://doi.org/10.1111/JFR3.12683

3. Akcay S, Kundegorski ME, Willcocks CG, Breckon TP (2018) Using deep convolutional neural network architectures for object classification and detection within x-ray baggage security imagery. IEEE Trans Inf Forensics Secur 13(9):2203–2215. https://doi.org/10.1109/TIFS.2018.2812196

4. Alcántara-Ayala I, Garnica-Peña RJ (2023) In: Alcántara-Ayala I, Arbanas Ž, Cuomo S, Huntley D, Konagai K, Arbanas SM, Mikoš M, Sassa K, Sassa S, Tang H, Tiwari B (eds) Landslide warning systems in high-income countries: past accomplishments and expected endeavours BT - Progress in landslide research and technology, 2(1):147–157. Springer Nature, Switzerland. https://doi.org/10.1007/978-3-031-39012-8_5

5. Amatya P, Kirschbaum D, Stanley T, Tanyas H (2021) Landslide mapping using object-based image analysis and open source tools. Eng Geol 282:106000. https://doi.org/10.1016/J.ENGGEO.2021.106000

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