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.

Climate change is affecting every aspect of the world including water resources and water scarcity. Drought is one of many big problems associated with climate change that could occur all over the world. Moreover, hydrological drought is one form of drought that relates to decreased river discharges, below-normal groundwater level, declining the area of wetlands and low water level in lakes or reservoirs. In this study, an assessment of hydrological drought in Gidra river is conducted to characterize dry and normal hydrological years according to Slovak Hydrometeorological Institute (SHMI) Methodology. Furthermore, making benefit of machine learning and artificial intelligence in this field is applicable now, as data of many types are being recorded every day. Deploying machine learning algorithms for the purpose of drought prediction is one way to regulate many operations of water management to prevent irrigation problems. By catching patterns through historical data and deploying machines to learn from those patterns, it is possible to use the values of daily average discharges for January, February, March, and April to correctly predict the hydrological situation in Gidra river whether it is dry or normal, knowing that normal situation refers to wet or normal hydrologically assessed years as the optimal goal in this study is drought assessment and prediction of Gidra river.

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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.

The article describes the process of redesigning the intake structure of the fish pass at the Žilina water structure in Slovakia. The existing intake structure does not meet the passability requirements for the target species of migratory fish. A design utilizing intake windows at various levels that cover fluctuations in the water level in the reservoir, which has been successfully used for other water structures, has been proposed. The new design was subjected to hydraulic calculations and simulations in the HECRAS 2D, 2D numerical model in order to achieve the required parameters such as the discharges, depths, and velocities within the limits for the specified fish zone.