Data-driven flow forecasting models, such as Artificial Neural Networks (ANNs), are increasingly used for operational flood warning systems. In this research, we systematically evaluate different machine learning techniques (random forest and decision tree) compare them with classical methods of the NAM rainfall run-off model Vésubie River, Nice, France. The modeled network is trained tested using discharge, precipitation, temperature, evapotranspiration data about four years (2011–2014). A...

River flow prediction is a pivotal task in the field of water resource management during era rapid climate change. The highly dynamic and evolving nature climatic variables, e.g., precipitation, has significant impact on temporal distribution river discharge recent days, making forecasting even more complicated for diversified water-related issues, flood irrigation planning. In order to predict discharge, various physics-based numerical models are used using numerous hydrologic parameters....

The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With models, the choice a spatio-temporal discretization scheme for computational domain strenuous task that requires extensive calibration potentially lab-based experimentation. GSI has high temporal dynamics due to natural, anthropogenic, climatic processes are not well represented by traditional which calibrated...

Distribution of the water flow path and residence time (HRT) in hyporheic zone is a pivotal aspect anatomizing transport environmental contaminants metabolic rates at groundwater surface interface fluvial habitats. Due to high variability material distribution composition streambed subsurface media, pragmatic model setup laboratory strenuous. Moreover, investigation an individual streamline cannot be efficiently executed experiments. However, automated generation paths, i.e., streamlines...

The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation's pattern use renewable production is crucial developing strategic plans. No previous study has been performed explore dynamics power consumption with change on country-wide scale. In contrast, number deep learning algorithms demonstrated acceptable...

Abstract Accurately delineating both pluvial and fluvial flood risk is critical to protecting vulnerable populations in urban environments. Although there are currently models frameworks estimate stormwater runoff predict flooding, often minimal observations validate results due the quick retreat of floodwaters from affected areas. In this research, we compare contrast different methodologies for capturing extent order highlight challenges inherent current methods flooding delineation. This...

Residence time of water flow is an important factor in subsurface media to determine the fate environmental toxins and metabolic rates ecotone between surface stream groundwater. Both numerical lab-based experimentation can be used estimate residence time. However, due high variability material composition media, a pragmatic model set up laboratory trace particles strenuous. Nevertheless, selection inclusion input parameters, execution simulation, generation results as well post-processing...

River flow prediction is a pivotal task in the field of water resource management during era rapid climate change. The highly dynamic and evolving nature climatic variables e.g., precipitation has significant impact on temporal distribution river discharge recent days making forecasting even more complicated for diversified water-related issues flood irrigation planning. To predict discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive...

As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myriad of interacting variables. Numerous hydraulic models available for analyzing flooding; however, meeting demand high spatial extension and finer discretization solving physics-based numerical equations computationally expensive. Computational efforts increase drastically with in model dimension resolution, preventing current solutions from...

Abstract Manning's roughness coefficient, n , is used to describe channel roughness, and a widely sought‐after key parameter for estimating predicting flood propagation. Due its control of flow velocity shear stress, critical modeling timing floods pollutants, aquatic ecosystem health, infrastructural safety, so on. While alternative formulations exist, open‐channel typically regarded as temporally constant, determined from lookup tables or calibration, spatiotemporal variability was never...

<p>Distribution of the hyporheic streamlines and residence time (HRT) is a crucial factor under streambed to understand transport phenomena environmental toxins, sediment metabolic rates in fluvial ecology as well hydrological water budget. To quantify HRT, both laboratory numerical approach could serve discerning tools. However, due high heterogeneity natural topography, an efficient model setup can prove be pragmatic comparison tedious experiments for tracing streamlines....

River renaturation can be an effective management method for restoring a floodplain’s natural capacity and minimizing the effects during high flow periods. A 1D-2D Hydrologic Engineering Center–River Analysis System (HEC-RAS) model, in which flood plain was considered as 2D main channel 1D, used to simulate flooding restored reach of Spree River, Germany. When computing this finite volume difference approximations using Preissmann approach are 1D models, respectively. To comprehend...

Groundwater (GW) flooding mechanisms differ from river both spatially and temporally, preventative methods against groundwater must take this into account. Although caused by water rise occurs seldom, it can occasionally become severe last for a long time if the is significantly flooded. In southwest portion of research domain, Friedrichshafen with few urban communities, level table was discovered to be roughly 1 m below surface. It that settlement area only has one-story buildings. study...

The alteration of natural land cover to impervious surfaces during development increases stormwater runoff. Stormwater Control Measures (SCMs) are used manage water quantity and enhance quality by restoring the hydrologic cycle altered development. Often, SCMs have an outflow pipe handle overflows or release detained when infiltration is not possible. Traditionally, these static controls (e.g. a small orifice restrict volume outflow), however, systems can be improved instituting real-time...

River renaturation can be an effective management method for restoring the floodplain's natural capacity and minimizing effects during high flow periods. A 1D-2D HEC-RAS model, in which flood plain was considered as 2D main channel 1D, used to simulate flooding restored reach of Spree River. When computing this finite volume difference approximations using Preissmann approach are 1D models, respectively. To comprehend sensitivity parameters several scenarios were simulated different time...

The dynamics of suspended sediment involves inherent non-linearity and complexity as a result the presence both spatial variability basin characteristics temporal climatic patterns. As this complexity, conventional rating curve (SRC) other empirical methods produce inaccurate predictions. Deep neural networks (DNNs) have emerged one advanced modeling techniques capable addressing in hydrological processes over last few decades. DNN algorithms are used to perform predictive analysis...

The dynamics of suspended sediment involves inherent non-linearity and complexity as a result the presence both spatial variability basin characteristics temporal climatic patterns. As this complexity, conventional rating curve (SRC) other empirical methods produce inaccurate predictions. Deep neural networks (DNNs) have emerged one advanced modeling techniques capable addressing in hydrological processes over last few decades. DNN algorithms are used to perform predictive analysis...

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Abstract As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myriad of interacting variables. Numerous hydraulic models available for analyzing flooding; however, meeting demand high spatial extension and finer discretization solving physics-based numerical equations computationally expensive. Computational efforts increase drastically with in model dimension resolution, preventing current solutions...