Drought is one of many critical problems that could arise as a result of climate change as it has an impact on many aspects of the world, including water resources and water scarcity. In this study, an assessment of hydrological drought in the Gidra River is carried out to characterize dry, normal, and wet hydrological situations by using the Slovak Hydrometeorological Institute (SHMI) methodology. The water bearing coefficient is used as the index of the hydrological drought. As machine and deep learning are increasingly being used in many areas of hydroinformatics, this study is utilized artificial neural networks (ANNs) and support vector machine (SVM) models to predict the hydrological drought in the Gidra River based on daily average discharges in January, February, March, and April of the corresponding year. The study utilized in total 58 years of daily average discharge values containing 35 normal and wet years and 23 dry years. The results of the study show high accuracy of 100% in predicting hydrological drought in the Gidra River. The early classification of the hydrological situation in the Gidra River shows the potential of integrating water management with the deep and machine learning models in terms of irrigation planning and mitigation of drought effects.

This study aims to develop an advanced deep learning model, Hydro-Informer, for accurate water level and flood predictions, emphasizing extreme event forecasting. Utilizing a comprehensive dataset from the Slovak Hydrometeorological Institute SHMI (2008–2020), which includes precipitation, water level, and discharge data, the model was trained using a ladder technique with a custom loss function to enhance focus on extreme values. The architecture integrates Recurrent and Convolutional Neural Networks (RNN, CNN), and Multi-Head Attention layers. Hydro-Informer achieved significant performance, with a Coefficient of Determination (R²) of 0.88, effectively predicting extreme water levels 12 h in advance in a river environment free from human regulation and structures. The model’s strong performance in identifying extreme events highlights its potential for enhancing flood management and disaster preparedness. By integrating with diverse data sources, the model can be used to develop a well-functioning warning system to mitigate flood impacts. This work proposes a novel architecture suitable for locations without water regulation structures.

The water sources located on the inland islands on the left side of the Danube River as well as water sources located on the right bank of the Danube River in the Pečniansky forest have an irreplaceable function in terms of supplying the population in Bratislava with drinking water. There are 80 wells in these mentioned areas operated by the Bratislava Water Company. In order to maintain the actual functions of the water resources, it is therefore necessary to assess the impact of the proposed construction of the multi-purposed structure ‘Hydraulic structure Bratislava — Pečniansky forest’ on these water sources.

The most appropriate method to protect settlements and economically important sites from flood hazard, is the implementation of flood protection measures in stream catchments and protected localities, which contribute to reduce the peak flow and distribution of the flood wave over a longer period of time. If such measures are not realistic or ineffective, it is necessary to focus on flood protection directly on the area of the protected side or its vicinity. Where the lag time between the flood threat detection and actual flood onset is short, one possible measure is to increase the capacity of the watercourse, very often in combination with other flood mitigation measures in the protected area. The engineering approach to flood protection is the subject of many scientific research studies. Permission for flood protection structures depends on their environmental impact assessment (EIA), according to Law no. 24/2002 Coll. on Environmental Impact Assessment in the Slovak Republic, annex no. 8 (list of activities subject to EIA). Based on the EIA, it is possible to select the best alternative of flood protection, i.e., the alternative with the lowest risk impact on the environment. This paper aims to analyse the flood protection measures along the Lukavica stream (central Slovakia), applying hydraulic models. The best alternative with the lowest impact on the environment, assessed using the risk analysis method, consists of detention reservoir construction. An effective combination of environmental impact assessment and hydraulic modelling contribute to the selection of an effective flood protection measure in the territory.

Presented paper deals with the hydraulic assessment of groundwater flow in the area affected by the realization of the hydraulic gate on the Klátov branch and in the adjacent territory of a dike, which is located on the right-side of Little Danube. This hydraulic assessment is part of the project of the Slovak Water Management Enterprise, which also aims to increase the height and seal the dike on the right-side of the Little Danube. Generally, the project is divided into three phases (Phase I, II and III) to implement different technical measures to protect the area from flooding. The assumption for the execution of the technical measures of the mentioned three project phases is a continuous flood protection of part of the Žitný ostrov area around the Little Danube and the Klátov River branch in the reach from Kolárovo to Jahodná town. Therefore, a 3D mathematical model was created to simulate groundwater flow by changing boundary conditions of surface water flow during flood periods.