There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning signals of stress build-up deep in the Earth’s crust (presumably associated with earthquake events). Here, we propose implementing a machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes and manifested as disturbances in the TEC data. After filtering and screening our data for solar or geomagnetic influences at different time scales, our results indicate that for large earthquakes (>Mw 6), true negative predictions can be achieved with 85.7% accuracy, and true positive predictions with an accuracy of 80%. We tested our method with different skill scores, such as accuracy (0.83), precision (0.85), recall (0.8), the Heidke skill score (0.66), and true skill statistics (0.66).

Predicting where and when space weather events such as solar flares and X-rays bursts are likely to occur in a specific area of interest constitutes a significant challenge in space weather research. Space weather scientists are therefore gradually exploring multivariate data analysis techniques from the fields of data mining or machine learning in order to approximate future occurrences of space weather events from past distribution patterns. As solar flares emit extreme ultraviolet (EUV) and X-ray radiation which leads to ionization effect in different layers of the ionosphere, most recent works related to solar flare predictions using machine learning (ML) techniques, focused on x-ray time series predictions. Here, we suggest using Support Vector Machine (SVM) for classifying sub-daily and diurnal TEC spatial changes prior to solar flare events, in order to assess the possibility of predicting B, C, M and X-class solar flare events. This is done as opposed to predicting Total Electron Content (TEC) time series using ML techniques. The predictions are estimated up to three days before each tested class events, along with different skill scores such as precision, recall, HSS, accuracy and TSS. The results indicate that the suggested approach has the ability to predict solar flare events of X and M-class 24 hours prior to their occurrence with 91% and 76% HSS skill scores, respectively, which improves over most recent related works. However, for the small-size C and B-class flares, the suggested approach does not succeed in producing similar promising results.

Grapevine (Vitis vinifera L.) currently includes thousands of cultivars. Discrimination between these varieties, historically done by ampelography, is done in recent decades mostly by genetic analysis. However, when aiming to identify archaeobotanical remains, which are mostly charred with extremely low genomic preservation, the application of the genomic approach is rarely successful. As a result, variety-level identification of most grape remains is currently prevented. Because grape pips are highly polymorphic, several attempts were made to utilize their morphological diversity as a classification tool, mostly using 2D image analysis technics. Here, we present a highly accurate varietal classification tool using an innovative and accessible 3D seed scanning approach. The suggested classification methodology is machine-learning-based, applied with the Iterative Closest Point (ICP) registration algorithm and the Linear Discriminant Analysis (LDA) technique. This methodology achieved classification results of 91% to 93% accuracy in average when trained by fresh or charred seeds to test fresh or charred seeds, respectively. We show that when classifying 8 groups, enhanced accuracy levels can be achieved using a "tournament" approach. Future development of this new methodology can lead to an effective seed classification tool, significantly improving the fields of archaeobotany, as well as general taxonomy.

A flash flood is a rapid and intense response of a drainage area to heavy rainfall events. In the arid and semiarid parts of the Eastern Mediterranean (EM) region, the spatiotemporal distribution of rainfall is the most important factor for flash flood generation. A possible precursor to heavy rainfall events is the rise in tropospheric water vapor amount, which can be remotely sensed using ground-based global navigation satellite system (GNSS) stations. Here, we use the precipitable water vapor (PWV) derived from nine GNSS ground-based stations in the arid part of the EM region in order to predict flash floods. Our approach includes using three types of machine learning (ML) models in a binary classification task, which predicts whether a flash flood will occur given 24 h of PWV data. We train our models with 107 unique flash flood events and vigorously test them using a nested cross-validation technique. The results indicate a good agreement between all three types of models and across various score metrics. In addition, the models are further improved by adding more features such as surface pressure measurements. Finally, a feature importance analysis shows that the most important features are the PWV values from 2 to 6 h prior to a flash flood. These promising results indicate that it is possible to augment the current flash flood warning systems with a near real-time GNSS ground-based data-driven approach as demonstrated in this work.

Space weather phenomena such as solar flares have a massive destructive power when they reach a certain magnitude. Here, we explore the deep-learning approach in order to build a solar flare-forecasting model, while examining its limitations and feature-extraction ability based on the available Geostationary Operational Environmental Satellite (GOES) X-ray time-series data. We present a multilayer 1D convolutional neural network to forecast the solar flare event probability occurrence of M- and X-class flares at 1, 3, 6, 12, 24, 48, 72, and 96 hr time frames. The forecasting models were trained and evaluated in two different scenarios: (1) random selection and (2) chronological selection, which were compared afterward in terms of common score metrics. Additionally, we also compared our results to state-of-the-art flare-forecasting models. The results indicates that (1) when X-ray time-series data are used alone, the suggested model achieves higher score results for X-class flares and similar scores for M-class as in previous studies. (2) The two different scenarios obtain opposite results for the X- and M-class flares. (3) The suggested model combined with solely X-ray time-series fails to distinguish between M- and X-class magnitude solar flare events. Furthermore, based on the suggested method, the achieved scores, obtained solely from X-ray time-series measurements, indicate that substantial information regarding the solar activity and physical processes are encapsulated in the data, and augmenting additional data sets, both spatial and temporal, may lead to better predictions, while gaining a comprehensive physical interpretation regarding solar activity. All source codes are available at https://github.com/vladlanda.

Interferometric synthetic aperture radar (InSAR) technology provides a valuable tool for obtaining Earth surface deformation and topography at high spatial resolution for crustal deformation studies. Similar to global positioning system (GPS), InSAR measurements are affected by the Earth's ionospheric and tropospheric layers as the electromagnetic signals significantly refract while propagating through the different layers. While GPS signals propagating through the neutral atmosphere are affected primarily by the distribution, pressure and temperature of atmospheric gases, including water vapour, the propagation through the ionosphere is mainly affected by the number of free electrons along the signal path. Here, we present the use of dense regional GPS networks for extracting tropospheric zenith delays and ionospheric total electron content (TEC) maps in order to reduce the noise levels in InSAR images. The results show significant reduction in the root mean square (RMS) values when simultaneously combining the two corrections, both at short time periods where no surface deformation is expected, and at longer periods, where imaging of localized subsidence and fault creep is enhanced.