257 research outputs found
Machine learning for uncertainty estimation in fusing precipitation observations from satellites and ground-based gauges
To form precipitation datasets that are accurate and, at the same time, have
high spatial densities, data from satellites and gauges are often merged in the
literature. However, uncertainty estimates for the data acquired in this manner
are scarcely provided, although the importance of uncertainty quantification in
predictive modelling is widely recognized. Furthermore, the benefits that
machine learning can bring to the task of providing such estimates have not
been broadly realized and properly explored through benchmark experiments. The
present study aims at filling in this specific gap by conducting the first
benchmark tests on the topic. On a large dataset that comprises 15-year-long
monthly data spanning across the contiguous United States, we extensively
compared six learners that are, by their construction, appropriate for
predictive uncertainty quantification. These are the quantile regression (QR),
quantile regression forests (QRF), generalized random forests (GRF), gradient
boosting machines (GBM), light gradient boosting machines (LightGBM) and
quantile regression neural networks (QRNN). The comparison referred to the
competence of the learners in issuing predictive quantiles at nine levels that
facilitate a good approximation of the entire predictive probability
distribution, and was primarily based on the quantile and continuous ranked
probability skill scores. Three types of predictor variables (i.e., satellite
precipitation variables, distances between a point of interest and satellite
grid points, and elevation at a point of interest) were used in the comparison
and were additionally compared with each other. This additional comparison was
based on the explainable machine learning concept of feature importance. The
results suggest that the order from the best to the worst of the learners for
the task investigated is the following: LightGBM, QRF, GRF, GBM, QRNN and QR..
Ensemble learning for blending gridded satellite and gauge-measured precipitation data
Regression algorithms are regularly used for improving the accuracy of
satellite precipitation products. In this context, ground-based measurements
are the dependent variable and the satellite data are the predictor variables,
together with topography factors. Alongside this, it is increasingly recognised
in many fields that combinations of algorithms through ensemble learning can
lead to substantial predictive performance improvements. Still, a sufficient
number of ensemble learners for improving the accuracy of satellite
precipitation products and their large-scale comparison are currently missing
from the literature. In this work, we fill this specific gap by proposing 11
new ensemble learners in the field and by extensively comparing them for the
entire contiguous United States and for a 15-year period. We use monthly data
from the PERSIANN (Precipitation Estimation from Remotely Sensed Information
using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE
Retrievals for GPM) gridded datasets. We also use gauge-measured precipitation
data from the Global Historical Climatology Network monthly database, version 2
(GHCNm). The ensemble learners combine the predictions by six regression
algorithms (base learners), namely the multivariate adaptive regression splines
(MARS), multivariate adaptive polynomial splines (poly-MARS), random forests
(RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and
Bayesian regularized neural networks (BRNN), and each of them is based on a
different combiner. The combiners include the equal-weight combiner, the median
combiner, two best learners and seven variants of a sophisticated stacking
method. The latter stacks a regression algorithm on the top of the base
learners to combine their independent predictions...Comment: arXiv admin note: text overlap with arXiv:2301.0125
Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques
Diabetic foot ulcers (DFUs) constitute a serious complication for people with
diabetes. The care of DFU patients can be substantially improved through
self-management, in order to achieve early-diagnosis, ulcer prevention, and
complications management in existing ulcers. In this paper, we investigate two
categories of image-to-image translation techniques (ItITT), which will support
decision making and monitoring of diabetic foot ulcers: noise reduction and
super-resolution. In the former case, we investigated the capabilities on noise
removal, for convolutional neural network stacked-autoencoders (CNN-SAE).
CNN-SAE was tested on RGB images, induced with Gaussian noise. The latter
scenario involves the deployment of four deep learning super-resolution models.
The performance of all models, for both scenarios, was evaluated in terms of
execution time and perceived quality. Results indicate that applied techniques
consist a viable and easy to implement alternative that should be used by any
system designed for DFU monitoring
Tensor-based Nonlinear Classifier for High-Order Data Analysis
In this paper we propose a tensor-based nonlinear model for high-order data
classification. The advantages of the proposed scheme are that (i) it
significantly reduces the number of weight parameters, and hence of required
training samples, and (ii) it retains the spatial structure of the input
samples. The proposed model, called \textit{Rank}-1 FNN, is based on a
modification of a feedforward neural network (FNN), such that its weights
satisfy the {\it rank}-1 canonical decomposition. We also introduce a new
learning algorithm to train the model, and we evaluate the \textit{Rank}-1 FNN
on third-order hyperspectral data. Experimental results and comparisons
indicate that the proposed model outperforms state of the art classification
methods, including deep learning based ones, especially in cases with small
numbers of available training samples.Comment: To appear in IEEE ICASSP 2018. arXiv admin note: text overlap with
arXiv:1709.0816
Automatic 3D modeling and reconstruction of cultural heritage sites from Twitter images
This paper presents an approach for leveraging the abundance of images posted on social media like Twitter for large scale 3D reconstruction of cultural heritage landmarks. Twitter allows users to post short messages, including photos, describing a plethora of activities or events, e.g., tweets are used by travelers on vacation, capturing images from various cultural heritage assets. As such, a great number of images are available online, able to drive a successful 3D reconstruction process. However, reconstruction of any asset, based on images mined from Twitter, presents several challenges. There are three main steps that have to be considered: (i) tweets’ content identification, (ii) image retrieval and filtering, and (iii) 3D reconstruction. The proposed approach first extracts key events from unstructured tweet messages and then identifies cultural activities and landmarks. The second stage is the application of a content-based filtering method so that only a small but representative portion of cultural images are selected to support fast 3D reconstruction. The proposed methods are experimentally evaluated using real-world data and comparisons verify the effectiveness of the proposed scheme.peer-reviewe
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