This paper analyses the problem of predicting students’ academic performance, a subject that is increasingly investigated within Educational Data Mining literature. For better understanding educational related phenomena, there continuous interest in applying supervised and unsupervised learning methods for obtaining additional insights into process. The if student will pass or fail at certain discipline based on grades received during semester difficult one, highly dependent various...

With the recent increase in occurrence of severe weather phenomena, development accurate nowcasting is paramount importance. Among computational methods that are used to predict evolution weather, deep learning techniques offer a particularly appealing solution due their capability for patterns from large amounts data and fast inference times. In this paper, we propose convolutional network forecasting based on radar product prediction. Our model (NeXtNow) adapts ResNeXt architecture has...

Short-term quantitative precipitation forecast is a challenging topic in meteorology, as the number of severe meteorological phenomena increasing most regions world. Weather radar data utmost importance to meteorologists for issuing short-term weather and warnings phenomena. We are proposing AutoNowP, binary classification model intended nowcasting based on reflectivity prediction. Specifically, AutoNowP uses two convolutional autoencoders, being trained collected both stratiform convective...

Predicting weather, and particularly severe is an important challenge both for meteorological machine learning researchers. The complexity difficulty of the problem mainly due to chaotic character atmosphere implicit large set information (radar, satellite or ground observations) which have be analyzed by meteorologists. Thus, understanding relationships between various parameters extracted from radar observations may useful providing additional comprehension about weather development would...

We studied the feasibility of recognizing individual right whales (Eubalaena glacialis) using convolutional neural networks. Prior studies have shown that CNNs can be used in wide range classification and categorization tasks such as automated human face recognition. To test applicability deep learning to whale recognition we developed several models based on best practices from literature. Here, describe performance models. conclude machine is feasible comment difficulty problem

One of the hottest topics in today’s meteorological research is Weather nowcasting, which weather forecast for a short time period such as one to six hours. Radar an important data source used by operational meteorologists issuing nowcasting warnings. With main goal helping analysing radar warnings, we propose NowDeepN, supervised learning based regression model uses ensemble deep artificial neural networks predicting values products at certain moment. The predicted NowDeepN may be...

ABSTRACT Ground penetrating radar has been used extensively in near‐surface studies to detect underground objects and features typically located within a few metres beneath the surface. In urban areas, ground is widely study buried utilities such as pipes cables. A more recent unconventional application of detection tree roots, which can interact negatively with human infrastructure number ways. However, geophysical roots proven quite challenging site‐specific. Most (even coarse roots) have...

Flash floods are a major weather-related risk, as they cause more than 5000 fatalities annually, according to the World Meteorological Organization. Quantitative Precipitation Estimation is method used approximate rainfall over locations where direct field observations not available. It represents one of most valuable information employed by meteorologists and hydrologists for issuing early warnings concerning flash floods. The current study in line with efforts improve radar-based estimates...

Weather nowcasting which is the analysis and shortterm weather forecast a topic of major interest both for meteorological machine learning researchers. The problem complex one due to large volume data (such as radar, satellite or other ground observations) has be analyzed by meteorologists issuing warnings. In addition, climate changes are chaotic models complex. main goal paper better understand relationships between products extracted from radar observations in severe normal conditions....

The problem of forecasting severe weather events is one the most challenging topics in meteorology, as phenomena are becoming more and frequent many regions world. short term analysis forecast called nowcasting has a major role prevention risks management crisis situations. Radar data essential materials used by meteorologists for making short-term issuing early warnings weather. We proposing convolutional neural network model XNow prediction radar products' values that would be useful...

The paper approaches the topic of nowcasting, one hottest topics in meteorology which deals with problem short-term forecasting severe weather phenomena. Various types meteorological data, including radar measurements, satellite data and stations' observations are currently used for events. Radar is important sources by meteorologists nowcasting providing alerts We proposing a new one-class classifier, named RadRAR (Radar products' values prediction using Relational Association Rules)...

A complex system for zonal earthquake prediction, warning, and local assessment of seismic events has been designed, performed, implemented, experimented/validated. The was designed to ensure simultaneously: the reception warning signals following earthquakes with epicentre on a radius 1000 km; acquisition precursor data possible prediction in perimeter targeted locality and/or improvement database field Earth physics purchased processed centrally at national dispatcher; intensity movements,...

One of the most challenging topics within meteorological domain is weather forecasting, with a particular focus on predicting severe weather. Weather forecasting for short-time periods (0 to 6 hours) called nowcasting and major interest both researchers operational meteorologists. For meteorologists, task issuing warnings not an easy one as it highly dependent their experience in analysing correlating various observations. A study using self-organizing maps analyzing radar data conducted...

Summary The aim of this study is to conduct new experiments with higher precision and building complementary equipment in which we can detect the plant roots extent more accuracy, their behaviour natural environments by carrying tests both laboratory agricultural fields. We've been able create ERT (electrical resistivity tomography) electrode arrays classical experimental types, using flexible created Matlab programming. developed test settings for order minimize human errors come up high...

Summary Trees play an important part in urban areas, but sometimes, tree roots can interact destructively with the surrounding infrastructure. This interaction be studied through geophysical methods. Here, we use Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT) to study this interaction. We split surveys into two categories: carried out on natural soils, where both ERT GPR were applied, man-made surfaces, only was out. also introduce a custom-made equipment which...

Summary Urban areas are not usually favourable sites for geophysical surveys. However, GPR has shown promise in different environments, particularly the mapping of coarse roots. Given its functionality on multiple urban surfaces, speed data acquisition, and overall suitability tree root detection, we find that most scenarios, is well-suited method detection. a panacea, though. It fundamental limitations when it comes to detection , given variability environmental parameters areas, as well...

Summary The use of two geophysical methods is presented, the electrical resistivity tomography and GPR. Integrating these in agricultural environments posses significant advantages water fertilizer consumption representing a very fast useful tool area precision agriculture as well. results are promising, achieving level by implementing various IoT (internet things) devices adaptations method data processing.