Characteristics of river heatwaves in the Vistula River basin, Europe.

Rivers worldwide are warming due to the impact of climate change and human interventions. This study investigated river heatwaves in the Vistula River Basin, one of the largest river systems in Europe using long-term observed daily river water temperatures from the past 30 years (1991-2020). The results showed that river heatwaves are increased in frequency and intensity in the Vistula River Basin.

Streamflow trends and flood frequency analysis: a regional study of the UK.

In recent years, the escalating effects of climate change on surface water bodies have underscored the critical importance of analyzing streamflow trends for effective water resource planning and management. This study conducts a comprehensive regional investigation into the streamflow rate trends of 18 rivers across the United Kingdom (UK). An enhanced Mann-Kendall (MK) test was employed to meticulously analyze both rainfall and streamflow trends on monthly and annual scales.

Analysis, design, and maintenance of isolation valves in water distribution networks: State of the art review, insights from field experiences and future directions.

Isolation valves play a primary role in water distribution networks as their operation enables isolating the part of the network undergoing planned or extraordinary maintenance, in the context of rehabilitation or pipe break repairs, respectively. This paper presents a review of the current state of the art of isolation valves, with a focus on the problems of analysis, e.g.

An optimized NARX-based model for predicting thermal dynamics and heatwaves in rivers.

The thermal dynamics within river ecosystems represent critical areas of study due to their profound impact on overall aquatic health. With the rising prevalence of heatwaves in rivers, a consequence of climate change, it is imperative to deepen our understanding through comprehensive research efforts. Despite this urgency, there remains a noticeable dearth in studies aimed at refining modeling techniques to precisely characterize the duration and intensity of these events.

A novel optimized model based on NARX networks for predicting thermal anomalies in Polish lakes during heatwaves, with special reference to the 2018 heatwave.

In 2018, Europe experienced one of the most severe heatwaves ever recorded. This extreme event's impact on lake surface water temperature (LSWT) in Polish lakes has largely remained unknown. In this study, the impact of the 2018 European heatwave on LSWT in 24 Polish lakes was investigated based on a long-term observed dataset (1987-2020).

A stacked machine learning model for multi-step ahead prediction of lake surface water temperature.

Lake surface water temperature is one of the most important physical and ecological indices of lakes, which has frequently been used as the indicator to evaluate the impact of climate change on lakes. Knowing the dynamics of lake surface water temperature is thus of great significance. The past decades have witnessed the development of different modeling tools to forecast lake surface water temperature, yet, simple models with fewer input variables, while maintaining high forecasting accuracy are scarce.

Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm.

In recent years, the growing impact of climate change on surface water bodies has made the analysis and forecasting of streamflow rates essential for proper planning and management of water resources. This study proposes a novel ensemble (or hybrid) model, based on the combination of a Deep Learning algorithm, the Nonlinear AutoRegressive network with eXogenous inputs, and two Machine Learning algorithms, Multilayer Perceptron and Random Forest, for the short-term streamflow forecasting, considering precipitation as the only exogenous input and a forecast horizon up to 7 days. A large regional study was performed, considering 18 watercourses throughout the United Kingdom, characterized by different catchment areas and flow regimes.

A nonlinear autoregressive exogenous (NARX) model to predict nitrate concentration in rivers.

Forecasting nitrate concentration in rivers is essential for environmental protection and careful treatment of drinking water. This study shows that nonlinear autoregressive with exogenous inputs neural networks can provide accurate models to predict nitrate plus nitrite concentrations in waterways. The Susquehanna River and the Raccoon River, USA, were chosen as case studies.

Prediction of spring flows using nonlinear autoregressive exogenous (NARX) neural network models.

In the Mediterranean area, climate changes have led to long and frequent droughts with a drop in groundwater resources. An accurate prediction of the spring discharge is an essential task for the proper management of the groundwater resources and for the sustainable development of large areas of the Mediterranean basin. This study shows an unprecedented application of non-linear AutoRegressive with eXogenous inputs (NARX) neural networks to the prediction of spring flows.

Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network.

In the Mediterranean area, the high water demand frequently leads to an excessive exploitation of the water resource, which involves a qualitative degradation of the freshwaters stored in coastal karst aquifers, as a result of phenomena such as sea saltwater intrusion. In this study, the NARX network was used to predict the daily groundwater level fluctuation for 76 monitored wells located on the Apulian territory. A preliminary analysis on reference wells was performed in order to assess the impact on the groundwater level prediction of two input parameters, rainfall and evapotranspiration, and the sensitivity to changes of training algorithm and input time delay.