Groundwater spring potential prediction using a deep-learning algorithm-Reference-Cited by-同舟云学术

Groundwater spring potential prediction using a deep-learning algorithm

Published:2023-03-16 Issue: Volume: Page:
ISSN:1895-7455
Container-title:Acta Geophysica
language:en
Short-container-title:Acta Geophys.

Author:

Moughani Solmaz Khazaei,Osmani Abdolbaset,Nohani Ebrahim,Khoshtinat Saeed,Jalilian Tahere,Askari Zahra,Heddam Salim,Tiefenbacher John P.,Hatamiafkoueieh Javad^ORCID

Funder

RUDN University Strategic Academic Leadership Program

Publisher

Springer Science and Business Media LLC

Subject

Geophysics

Link

https://link.springer.com/content/pdf/10.1007/s11600-023-01053-0.pdf

Reference95 articles.

1. Abedini M, Ghasemian B, Shirzadi A, Bui DT (2019) A comparative study of support vector machine and logistic model tree classifiers for shallow landslide susceptibility modeling. Environ Earth Sci 78(18):1–15. https://doi.org/10.1007/s12665-019-8562-z

2. Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9(1):93–106

3. Althuwaynee OF et al (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA 114:21–36

4. Assadollahi S et al (2009) From lateral flow devices to a novel nano-color microfluidic assay. Sensors 9(8):6084–6100

5. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31

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