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Journal: Land, 2024
Volume: 13
Number: 322
322
Article:
Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping
Authors:
by
Elham Hosseinzadeh, Sara Anamaghi, Massoud Behboudian and Zahra Kalantari
Link:
https://www.mdpi.com/2073-445X/13/3/322
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Cite
Hosseinzadeh, E.; Anamaghi, S.; Behboudian, M.; Kalantari, Z. Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping. Land 2024, 13, 322. https://doi.org/10.3390/land13030322
Hosseinzadeh E, Anamaghi S, Behboudian M, Kalantari Z. Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping. Land. 2024; 13(3):322. https://doi.org/10.3390/land13030322
Chicago/Turabian StyleHosseinzadeh, Elham, Sara Anamaghi, Massoud Behboudian, and Zahra Kalantari. 2024. "Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping" Land 13, no. 3: 322. https://doi.org/10.3390/land13030322
APA StyleHosseinzadeh, E., Anamaghi, S., Behboudian, M., & Kalantari, Z. (2024). Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping. Land, 13(3), 322. https://doi.org/10.3390/land13030322