Optimal Accuracy-Time Trade-off for Deep Learning Services in Edge Computing Systems

Hosseinzadeh, Minoo; Wachal, Andrew; Khamfroush, Hana; Lucani, Daniel E.

doi:10.1109/icc42927.2021.9500744

Cited by 8 publications

(1 citation statement)

References 12 publications

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“… Valavi et al (2021) reported an ensemble of tuned individual models as outperforming all other ML and regression based algorithms when benchmarking model performances on potential distribution of 225 different species; their results also show nonparametric technques outperforming traditional regression methods. Among SDM studies focused on testing and comparing different SDM methodologies, the study from Valavi et al (2021) is also one of the few reporting computation time for all the models: this is a metric seldomly reported, but relevant when considering the optimal trade-off between accuracy and time, a well-known issue in the ML field ( Hosseinzadeh et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning

Bonannella

Hengl²,

Heisig

et al. 2022

PeerJ

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This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species (Abies alba Mill., Castanea sativa Mill., Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H. Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestris L., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for a total of three million of points was used to train different algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 305 coarse and high resolution covariates representing spectral reflectance, different biophysical conditions and biotic competition was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to tune and train an ensemble model based on stacking with a logistic regressor as a meta-learner. An ensemble model was trained for each species: probability and model uncertainty maps of realized distribution were produced for each species using a time window of 4 years for a total of six distribution maps per species, while for potential distributions only one map per species was produced. Results of spatial cross validation show that the ensemble model consistently outperformed or performed as good as the best individual model in both potential and realized distribution tasks, with potential distribution models achieving higher predictive performances (TSS = 0.898, R2logloss = 0.857) than realized distribution ones on average (TSS = 0.874, R2logloss = 0.839). Ensemble models for Q. suber achieved the best performances in both potential (TSS = 0.968, R2logloss = 0.952) and realized (TSS = 0.959, R2logloss = 0.949) distribution, while P. sylvestris (TSS = 0.731, 0.785, R2logloss = 0.585, 0.670, respectively, for potential and realized distribution) and P. nigra (TSS = 0.658, 0.686, R2logloss = 0.623, 0.664) achieved the worst. Importance of predictor variables differed across species and models, with the green band for summer and the Normalized Difference Vegetation Index (NDVI) for fall for realized distribution and the diffuse irradiation and precipitation of the driest quarter (BIO17) being the most frequent and important for potential distribution. On average, fine-resolution models outperformed coarse resolution models (250 m) for realized distribution (TSS = +6.5%, R2logloss = +7.5%). The framework shows how combining continuous and consistent Earth Observation time series data with state of the art machine learning can be used to derive dynamic distribution maps. The produced predictions can be used to quantify temporal trends of potential forest degradation and species composition change.

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Section: Discussionmentioning

confidence: 99%