Bogdan Popa

Characterizing acoustic Willis materials offer unique opportunities in medical diagnostics, structural health monitoring, and nondestructive evaluation as they better model the complex materials with various levels of asymmetry involved in these applications. However, current estimation methods are insufficient for fully characterizing these materials due to the limited knowledge of the complex wave-matter interactions in Willis media. We introduce an iterative convolutional neural network (CNN) training scheme that robustly maps the complete set of material and geometric parameters of Willis samples to their scattered sound fields. The synthetically-trained CNN estimates all unknown parameters from measured acoustic scattering data from which statistical confidence bounds are derived. The process is repeated iteratively by searching in the newly derived and progressively smaller parameter space until the desired precision is achieved. This non-uniform and adaptive sampling strategy results in more accurate and efficient characterization for advanced health monitoring and related applications.

Within the framework of the iAMP-Hydro EU project, two dedicated work packages address the challenges of inflow and power forecasting for both run-of-river hydropower plants and large hydropower reservoirs. These tasks aim to improve short-term and medium-term operational planning by integrating advanced data-driven techniques into hydrological forecasting chains. Five initial validation sites, three located in Spain and two in Greece, were analyzed using a suite of statistical and machine learning techniques, including ARIMA, LSTM, and Random Forest (RF) models implemented in MATLAB. The first results demonstrated satisfactory forecasting performance for regulated basins, particularly for the fourth and fifth reservoirs of the five-reservoir cascade system along the Aliakmon River in Greece. When extending the workflow to natural inflows and incorporating the first reservoir of the cascade as a new validation site, the modelling framework revealed a significant improvement in simulated inflows, emphasizing the importance of upstream regulation effects in data-driven forecasting. To further strengthen the robustness and reliability of the methodology, a sixth validation site, corresponding to the upstream (first) reservoir, was subsequently integrated into the project s evaluation structure. This paper presents the overall methodological framework, data preprocessing strategies, selected modelling tools, and preliminary forecasting results for the three Greek validation sites: Asomata, Agia Varvara, and Ilarion on the Aliakmon River. The study highlights the potential of statistical and machine learning approaches to support hydropower optimization within interconnected and regulated river systems.

Accurate forecasting of reservoir inflows based on historical data is essential for effective water resources planning and management. Water flow forecasting presents a major challenge due to the nonlinear, non-stationary, stochastic, and highly noisy nature of water flows in rivers and inflows into reservoirs. These complex characteristics hinder the modeling process and limit the effectiveness of conventional prediction methods, making it difficult to achieve high accuracy results. This study explores the use of the Elephant Herding Optimization (EHO) algorithm for hyperparameter tuning in three established forecasting models: Autoregressive Integrated Moving Average (ARIMA), Gated Recurrent Unit (GRU), and Random Forest (RF). The proposed framework enables efficient exploration of the parameter space and adaptive learning of inflow patterns, aiming to reduce overfitting and improve predictive accuracy. All models are trained using historical inflow data and evaluated for one-day-ahead forecasts over a 365-day period in an open-loop configuration. By combining data-driven methods with meta-heuristic optimization, this work contributes to the development of robust forecasting tools for water resources management, enhancing the resilience and sustainability of hydropower system operation.

This study presents a multicriteria evaluation for the integration and development of the Limberg Pumped Storage Hydropower Plant (PSHP) in Austria. The research aims to identify the most sustainable and efficient solution to enhance energy storage capacity and operational flexibility within the national energy system. Three development scenarios were analyzed using a Multicriteria Decision Analysis (MCDA) based on the Analytic Network Process (ANP) implemented in Super Decisions (SD) software. Scenario 1 involves the rehabilitation of Limberg II and the enlargement of the lower reservoir by raising the dam crest. Scenario 2 proposes the construction of a new hydropower unit, Limberg III. Scenario 3 combines both strategies, involving the construction of Limberg III together with the dam crest elevation to increase storage capacity and system efficiency. Scenario 3 stands out as the most viable and resilient option from both technical and strategic perspectives. Its mixed development nature, integrating new infrastructure with modernization of existing facilities, ensures not only the highest analytical score obtained through the ANP-based evaluation but also the stability of this position under varying decision parameters.

This paper applies an integrated Geographic Information Systems (GIS) – Multi-Criteria Decision Analysis (MCDA) framework to the site selection of pumped-storage hydropower plants (PSHP). After outlining the role of PSHP in ensuring energy flexibility and system stability, the study introduces the main principles of multicriteria analysis, including methods, steps and criteria relevant to an alternative for the selected site. To illustrate the method, Tarnița– Lăpuștești PSHP, a proposed pumped-storage project on the Someșul Cald River in Cluj County, Romania, is analyzed by combining GIS-based spatial analysis with engineering assessments applied as a MCDA. The results confirm the validity of previous decisions while providing a transparent and replicable framework for objectively comparing alternative sites. The study highlights that GIS–MCDA offers a robust decision-support tool for the planning and development of future pumped-storage hydropower facilities.

This paper investigates the integration of hydropower and solar energy within the Lower Olt River cascade as a pathway toward sustainable energy generation in Romania. The study focuses on the conceptual design of future hybrid power plants consisting of existing hydropower facilities where floating photovoltaic panels are proposed to be installed on the reservoir’s surfaces. An estimation of electricity production from both sources was performed, followed by the formulation of a trading strategy for the July–September 2025 period. The paper also explores the interaction between tactical and strategic management in hydropower operation and planning, describing how forecasting and decision-making processes are structured within the institutional framework. Finally, results for the selected hydropower plants demonstrate the positive influence of floating photovoltaic deployment on company performance, the national energy mix, and the overall sustainability of energy generation in Romania.

In the hydropower sector, accurate estimation of short-term reservoir inflows is an essential element to ensure efficient and safe management of water resources. Short-term forecasting supports the optimization of energy production, prevention of uncontrolled water discharges, planning of equipment maintenance, and adaption of operational strategies. In the absence of data on topography, vegetation, and basin characteristics (required in distributed or semi-distributed models), data-driven approaches can serve as effective alternatives for inflow prediction. This study proposes a novel hybrid approach that reverses the conventional LSTM (Long Short-Term Memory)—ARIMA (Autoregressive Integrated Moving Average) sequence: LSTM is first used to capture nonlinear hydrological patterns, followed by ARIMA to model residual linear trends.The model was calibrated using daily inflow data in the Izvorul Muntelui–Bicaz reservoir in Romania from 2012 to 2020, tested for prediction on the day ahead in a repetitive loop of 365 days corresponding to 2021 and further evaluated through multiple seven-day forecasts randomly selected to cover all 12 months of 2021. For the tested period, the proposed model significantly outperforms the standalone LSTM, increasing the R2 from 0.93 to 0.96 and reducing RMSE from 9.74 m3/s to 6.94 m3/s for one-day-ahead forecasting. For multistep forecasting (84 values, randomly selected, 7 per month), the model improves R2 from 0.75 to 0.89 and lowers RMSE from 18.56 m3/s to 12.74 m3/s. Thus, the hybrid model offers notable improvements in multi-step forecasting by capturing both seasonal patterns and nonlinear variations in hydrological data. The approach offers a replicable data-driven solution for inflow prediction in reservoirs with limited physical data.

Floating photovoltaic (FPV) systems are a new green technology emerging lately, having the indisputable advantage of not covering agricultural land but instead the surface of lakes or reservoirs. Being a new technology, even though the number of studies is significant, reliable results remain limited. This paper presents the possible influence of an FPV farm installed on the surface of a reservoir in Romania in four scenarios of the surface being covered with photovoltaic panels. The changes in the water mass under the FPV panels were determined using mathematical modelling as a tool. For this purpose, a water quality model was implemented for Mihăilești Reservoir, Romania, and the variations in the temperature, the phytoplankton biomass, and the total phosphorus and nitrogen were computed. Also, by installing FPV panels, it was estimated that a volume of water of between 1.75 and 7.43 million m3/year can be saved, and the greenhouse gas emission reduction associated with the proposed solutions will vary between 15,415 and 66,066 tCO2e/year; these results are in agreement with those reported in other scientifical studies. The overall conclusion is that the effect of an FPV farm on the reservoir’s surface is beneficial for the water quality in the reservoir.

Discharge is one of the most important parameters that characterize the operation of a hydroelectric power plant. Traditional methodologies determine the discharge of a high-head hydropower plant by the Gibson method, by exploring the velocity field through a penstock section (current meter method). Nevertheless, it is much more comfortable and advantageous to measure the discharge in a downstream section, at the immediately following hydropower plant, but in this case, it is necessary to know all the phenomena that occur in the tailrace channel between the two hydropower plants and to establish the measurement conditions so that the discharge estimation be within the limits imposed by the standards in force. This paper aims to determine the turbine discharge at Retezat HPP by measuring the discharge to the turbine from the HA1 in Clopotiva HPP.

In this paper, the forecast of the daily inflow water volumes in the Asomata reservoir, on the Aliakmon River, Greece, based on long short-term memory was realized. A MATLAB program was developed for one day ahead prediction; the model was calibrated for the period 2011-2021 based on the historical values and used in a repetitive loop for 365 days corresponding to the testing year 2022. An improvement in the forecasting was observed when the daily index associated with the forecasted variable was used as input. This improvement was much more important than when the associated precipitation values were used in the absence of the day index. A possible explanation would be the operating schedule of the plants upstream, Asomata reservoir being the fourth in a five reservoirs cascade. More than that, the upstream hydropower plant, Sfikia, is a pumped storage plant, Asomata being lower reservoir for this one. The results are in good agreement with the measurements, confirming the fact that long-short term memory networks are widely used in the hydrological forecasting with good results.

This study investigates the application of the Hydrologic Modeling System (HEC-HMS) for simulating inflows to the Ilarion Reservoir, the first reservoir in the cascade of five along the Aliakmon River in northern Greece. The model is calibrated using observed meteorological and hydrological data to assess its performance and reliability in reproducing observed flows. Model accuracy is evaluated through statistical indicators such as the Nash-Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS). The results demonstrate a satisfactory agreement between simulated and observed discharge, highlighting the model’s potential as a reliable tool for hydrological forecasting and water resource management in the region.

Water resources planning and management, involves as one of the most important aspects, forecasting the inflows in the reservoirs with the highest possible accuracy. The aim of this paper is to forecast the inflow for the next 1, 2, 3, 5, 365 days, based on historical recorded data. Two different methods were applied: one belongs to the classical time series methods, autoregressive integrated moving average (ARIMA), and the second one is part of artificial intelligence, Artificial Neural Network (ANN), both methods having toolboxes and predefined functions developed in MATLAB and being suitable for forecasting non-stationary time series. Thus, the capabilities of seasonality components integrated in ARIMA method and the ability of ANN to give good results were explored using MATLAB. Inflow values recorded at a large reservoir in Romania were used for calibrating the model. Coefficient of determination, root mean square error (RMSE) and Nash Sutcliffe efficiency have been calculated to estimate the performance of the models. ARIMA, with the seasonal component removed, is well-suited to short-term forecasts. By increasing the forecast time horizon, the accuracy of the predicted values decreases. ARIMA can be used to forecast the inflows for one year prediction horizon but the accuracy is low. For one step ahead forecasting, both methods provide results in good agreement with the original data.

Introduction While being a complex concept, religion can shape the way people in general, and students in particular, behave and make decisions in different types of contexts. In this regard, our paper aimed to assess the way religiosity influences the school climate and the social behavior of students from confessional and non-confessional Romanian high schools in order to raise awareness regarding the importance of religion in students’ education. Methods We used a quantitative method and we applied a questionnaire to 353 students from confessional and non-confessional high schools in Timișoara, Romania. Results and discussion The results of our study show positive correlations between religiosity and school climate, revealing that students from confessional schools have stronger feelings of belonging and better relationships with their teachers.

The main drawbacks of conventional acoustic imaging systems are low resolution images and high power consumption. This presentation will outline a new imaging paradigm that addresses these limitations. In this approach the environment is probed with an omnidirectional acoustic source and the back-scattered sound captured on an acoustic aperture is converted into a coherent optical field. The latter is focused into an image with off-the-shelf commercial cameras. The acoustic-to-light conversion is performed by a metasurface consisting of independent unit cells requiring no electronic synchronization thus facilitating scalability of the metasurface to large numbers of unit cells. The main advantages of this method are low power requirements and the ability to provide much higher resolution images than possible with conventional methods especially when low frequency sound is used to probe the environment.

In this paper, attention is drawn to the deterioration of Romanian surface water ecosystems due to eutrophication, an important environmental issue both at national and international levels. An inventory of existing studies dealing with the issue of the eutrophication of lakes and reservoirs in Romania is made, aiming to identify the main problems Romania is facing in monitoring, classifying, and managing eutrophic ecosystems. On Web of Science, the keyword “Eutrophication”, with “Romania” as country/region, leads to 50 publications, which are analyzed in this review. The number of articles found does not reflect the real environmental issue represented by eutrophic lakes and reservoirs in Romania. At a national level, only 126 lakes and reservoirs have been monitored and assessed between 2018 and 2020, in terms of ecological status/ecological potential. Thus, at a global evaluation, 77% of natural lakes and 33% of artificial ones do not reach the quality objectives. The results of this study showed that the frequency of measurements taken by water quality indicators is not the strongest point of measurement campaigns, as it is not sufficient for the diagnosis of eutrophic lakes, and supplementary measures must be undertaken to better understand and mitigate this phenomenon.

Although the important positive effects that lakes have ondevelopment at the regional level and beyond, they also have a negativeimpact related to the large amounts of water that they can consume byevaporation. This paper quantifies the effects that one of the largestartificial lakes in Romania (with complex use) has on the environment byestimating the blue water footprint and the carbon footprint. Thus, ananalysis is made of the evolution of the blue water footprint and carbonintensity (calculated for a 100-year life cycle) for 16 years and Pearsoncorrelation coefficients for these indicators are investigated. During the2008-2023 study period, the mean water footprint for the Vidraruhydropower plant was 5.07 m 3 /GJ and the carbon intensity varies between7.1 to 5.24 gCO 2 /kWh, with a polynomial trend. Those results are in goodagreement compared with the literature presenting results related to largereservoirs.

Evaporation has a major significance in the water balance of a reservoir. Usually, recorded data for the evaporation on the free surface of a reservoir are not available. There are numerous empirical relationships for the assessment of the evaporation that can be implemented into the water balance equation. In this paper, for Vidraru, one of the largest reservoirs in Romania, the Hargreaves method is used to estimate the evaporation values that are compared with recorded data obtained from Meteoblue archive. Recorded precipitation and evaporation data are used in mathematical model for water balance to find the answer to the question: can the evaporation and the directly water surface rainfall be neglected in the monthly/annual water balance of a reservoir? Daily meteorological values for the minimum and maximum temperature, evaporation and precipitation measured in the Vidraru reservoir area are used in this work. The main conclusion of the paper is that although in the summer months, on the surface of the lake, the amount of water lost through the evaporation is greater than the amount of water from the precipitations, on the time horizon of one year, the two components of the water balance of the reservoir have close values. Thus, for an accurate application of the water balance equation for a reservoir, the two variables, evaporation and precipitation, can be both considered or both neglected.

Because of an increased care for the environment, many related EU directives were issued on this subject. The Water Framework Directive is one of that and since it was published dramatically influenced the development of new SHPPs and the operation of the existing ones. The longitudinal connectivity of rivers and the available flow downstream SHPP weirs, due to the ecological flow and changes in the way to calculate it are reasons for operational and sometime even constructive changes of the SHPPs. This paper presents some methods for assessing the energy generation of a small hydropower plant considering the ecological flow. For that purpose, 3 different scenarios were considered to determine the ecological flow, and 4 methods were used to obtain the annual energy production: the classical method, a so-called simplified method and software packages dedicated to SHPPs, CASiMiR Hydropower and SMART Mini-Idro. The obtained results indicated that the simplified method and CASiMiR Hydropower performed very well, obtaining relative errors below 1%. The assessment of losses in energy production of a SHPP due to ecological flow for Romanian legislation case is also presented for a case study.

Hydropower represents one of the oldest and most developed technologies for electricity generation. Combined with more and more harsh environmental … societal restrictions, this led to a lack of available sites for developing new hydropower plants. At the same time, water streams are usually equipped with different hydrotechnical works for preventing flooding or assuring/supplying water to various beneficiaries. This is why in the latest years there is a new trend related to empowering existing hydrotechnical structures. This paper presents an assessment of the theoretical hydropower potential for 7 existing water barriers in Arges-Vedea hydrographic basin, Romania. Based on historical river discharge data and forecasted data for two climate change scenarios, RCP 4.5 and RCP 8.5, the gain in clean energy is presented and compared with the results for the reference period. The results show that while the cost of empowering the considered sites is around 760,000 Euro, a total energy generation of over 600 MWh/year can be obtained.

Today’s demand for reliable, affordable, and clean energy production is greater than ever. As most suitable sites for developing major hydropower facilities are already equipped or face strong opposition due to environmental restrictions, a new concept was lately defined: Hidden Hydro. This concept refers to the untapped, latent energy present within hydraulic systems that is often overlooked or underutilized. In this context, the present paper aims to emphasize the possibilities of empowering existing dams which are not used for generating electricity and Facau reservoir, located in Arges river catchment, Romania, is considered as the case study.

The development of floating photovoltaic systems in Romania can help to the share of more than 30.7 % of the total energy produced from renewable energy sources set for the year 2030. Floating photovoltaic plants ensure the achievement of overall energy targets without affecting biodiversity and/or agricultural crops. This study evaluates the insolation and the possibility of installing this technology on Lake Făcău, Romania, to benefit from the generation of electricity but also better water quality and reduction of carbon dioxide emissions $\left(\mathrm{CO}_{2}\right)$. The obtained results are promising as Floating photovoltaic plants covering around 0.22 % of the lake surface, provide 26.76 $M W h /$ year and the estimated reduction of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emission is 7.52 $\mathbf{t C O}_{2 \mathrm{eq}} /$ year.

In this paper, the efficiency of a metaheuristic optimization algorithm, Elephant Herding Optimization (EHO), is tested for the operation of a Hydro-Power Plant (HPP). The optimization problem is to impose the realization of the monthly energy generation planned for one year of a HPP and to compare the obtained results with the exact numerical solution (obtained using the fsolve built-in function of MATLAB). As in most algorithms that are inspired by nature, EHO manages through simple operations of sorting, keeping the most suitable solution, removing the worst solution and repopulating with modified solutions, to iteratively lead to optimal solutions for engineering problems. With a reduced calculation effort and with the right choice of the coefficients involved in the algorithm, results were obtained with good accuracy, so EHO can be used in more complex optimization problems, which cannot be analytically solved.

Abstract Large hydropower developments are not considered to be environmentally friendly due to large landscape flooded by their reservoirs. There is a certain carbon footprint related to this, in the phase of construction and of the operation of the reservoirs. This paper presents GHG emissions related to an iconic reservoir from Romania, Vidraru, and demonstrate that values are close to those determined around the world and that comparing with other conventional power plants producing electricity is the best environmental option. Furthermore, must be considered the other water uses related to reservoirs as flood mitigation, water supply for population, irrigation, and industry, where reservoirs cannot be replaced by anything else.

The paper presents the carbon footprint of the 10 reservoirs on Colentina river in Bucharest. There were presented entry data and hypotheses used by G-res tool who was applied for determining GHG emissions of these reservoirs. Therefore, we now have a good picture about their contribution to the overall GHG emissions in Bucharest.