Evaluating landslide susceptibility and landscape changes due to road expansion using optimized machine learning
Author:
Funder
Deanship of Scientific Research, King Khalid University
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11069-024-06652-8.pdf
Reference94 articles.
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2. Abraham MT, Satyam N, Jain P, Pradhan B, Alamri A (2021) Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms. Geomatics Nat Hazards Risk 12(1):3381–3408
3. Adu-Boahen K, Addai MO, Hayford SC, Adjovu ET, Yeboah DO, Mensah P (2023) Human-environment nexus: evaluating the anthropo-geomorphology and urban expansion of the Weija Gbawe Municipality, Ghana. Discover Environ 1(1):21
4. Ahmed IA, Talukdar S, Shahfahad, Parvez A, Rihan M, Baig MRI, Rahman A (2022) Flood susceptibility modeling in the urban watershed of Guwahati using improved metaheuristic-based ensemble machine learning algorithms. Geocarto Int 37(26):12238–12266
5. Ahmed M, Sulaiman MH, Mohamad AJ, Rahman M (2024) Gooseneck barnacle optimization algorithm: a novel nature inspired optimization theory and application. Math Comput Simul 218:248–265
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