GIS

Purpose. This study investigates the seismicity of North Rhine-Westphalia in western Germany, focusing on the tectonically active Lower Rhine Embayment and the surrounding Rhenish lignite mining district. It aims to characterize spatial, temporal, and magnitude-related patterns of seismicity and to assess the influence of both natural and anthropogenic factors. Methodology. A catalog of 626 earthquake events recorded between 1970 and 2026 was obtained from an open-access database, including information on earthquake magnitude, focal depth, geographic location, and occurrence time.

Purpose. The study aims to substantiate the effectiveness of integrating Earth remote sensing (ERS) and geographic information systems (GIS) for implementing the principles of precision agriculture. The main objective is to analyze the potential of multispectral satellite data for assessing the spatial heterogeneity of agroecosystems, identifying vegetation stress conditions, and supporting decision-making in agricultural production management.

This study extends previous research on Remaining Useful Life (RUL) prediction for agricultural vehicles by utilizing an enriched dataset to overcome earlier limitations in forecasting RUL for electric and hydraulic system components. Influential features have been identified through Pearson correlation and Random Forest feature importance analysis. Discrete Wavelet Transform (DWT) has been applied to extract additional approximation and detail coefficients, enhancing the feature set.

This study investigates soil erosion and land degradation in the Soubella sub-watershed of the Hodna region, Algeria, an area shaped by dryland climatic conditions ranging from semi-arid to arid. To evaluate erosion risk, the MEDALUS (Mediterranean Desertification and Land Use) model was combined with GIS-based spatial analysis. Soil erosion represents a critical environmental challenge in water-limited regions, where harsh climate and human pressures intensify land degradation.

In the dynamic landscape of modern agriculture, ensuring the reliability and efficiency of machinery is a critical challenge. This article proposes an innovative platform for monitoring and detecting failures in agricultural machinery, harnessing the power of Internet of Things (IoT) technology and cloud computing. The system in AWS cloud receives data from vehicles in real-time and can predict potential failures in engine, transmission, electric and hydraulic systems using machine learning algorithm LSTM.

Timely detection and prevention of agriculture vehicles malfunctions are key approaches to reducing maintenance costs, as well as updating and replacing equipment, and reducing the cost of growing agricultural crops. In this article an approach for Remaining Useful Life (RUL) prediction that utilizes a combination of telemetry, maintenance, and geospatial data (such as weather and terrain information) as input to a Long Short- Term Memory (LSTM) algorithm has been considered.

Проаналізовано цифрові карти лісів Львівської області (Україна) та Підкарпатського воєводства (Польща). Наведено порівняльний аналіз лісівничо-таксаційних показників лісів Львівської області (Україна) та Підкарпатського воєводства (Польща), які використовуються для обчислення емісій та поглинань парникових газів.

The digital maps of forest Lviv region (Ukraine) and the Subcarpathian Voivodeship (Poland) was anylised. Comparative analysis of forest taxation data in those study areas, which were used to calculate the emissions and absorption of greenhouse gases, was done.

Розглянуто геоінформаційну систему (ГІС), побудовану за результатами інвентаризації та моніторингових досліджень природного заповідника «Розточчя». До складу ГІС «Розточчя» введено квартально видільну сітку, породний склад насаджень, постійні пробні площі з таксаційними характеристиками, моніторингові маршрути і пункти. Описана ГІС забезпечує можливість використання, збереження, редагування, аналізу та відображення географічних даних «Розточчя».