Flood susceptibility mapping with ensemble machine learning: a case of Eastern Mediterranean basin, Türkiye

Özdemir, Hüseyin Baran; Koçyiğit, Müsteyde Baduna; Akay, Diyar

doi:10.1007/s00477-023-02507-z

Cited by 10 publications

(1 citation statement)

References 51 publications

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“…McNemar's test is a statistical test and is commonly used in order to compare two different classifiers concerning their performance and consistency. It is designed to examine whether the predictions produced by the models for a given test set are statistically different [83]. Hence, the null hypothesis of this test claims equal performance between the two classification algorithms.…”

Section: Statistical Assessmentmentioning

confidence: 99%

Agricultural Land Cover Mapping through Two Deep Learning Models in the Framework of EU’s CAP Activities Using Sentinel-2 Multitemporal Imagery

Papadopoulou,

Mallinis,

Siachalou

et al. 2023

Remote Sensing

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The images of the Sentinel-2 constellation can help the verification process of farmers’ declarations, providing, among other things, accurate spatial explicit maps of the agricultural land cover. The aim of the study is to design, develop, and evaluate two deep learning (DL) architectures tailored for agricultural land cover and crop type mapping. The focus is on a detailed class scheme encompassing fifteen distinct classes, utilizing Sentinel-2 imagery acquired on a monthly basis throughout the year. The study’s geographical scope covers a diverse rural area in North Greece, situated within southeast Europe. These architectures are a Temporal Convolutional Neural Network (CNN) and a combination of a Recurrent and a 2D Convolutional Neural Network (R-CNN), and their accuracy is compared to the well-established Random Forest (RF) machine learning algorithm. The comparative approach is not restricted to simply presenting the results given by classification metrics, but it also assesses the uncertainty of the classification results using an entropy measure and the spatial distribution of the classification errors. Furthermore, the issue of sampling strategy for the extraction of the training set is highlighted, targeting the efficient handling of both the imbalance of the dataset and the spectral variability of instances among classes. The two developed deep learning architectures performed equally well, presenting an overall accuracy of 90.13% (Temporal CNN) and 90.18% (R-CNN), higher than the 86.31% overall accuracy of the RF approach. Finally, the Temporal CNN method presented a lower entropy value (6.63%), compared both to R-CNN (7.76%) and RF (28.94%) methods, indicating that both DL approaches should be considered for developing operational EO processing workflows.

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Section: Statistical Assessmentmentioning

confidence: 99%