Search Results (5)

Accurate river hydraulic characterization is fundamental to assess flood risk, parametrize flood forecasting models, and develop river maintenance workflows. River hydraulic roughness and riverbed/floodplain geometry are the main factors controlling inundation extent and water levels. However, gauging stations providing hydrometric observations are declining worldwide, and they provide point measurements only. To describe hydraulic processes, spatially distributed data are required. In situ surveys are costly and time-consuming, and they are sometimes limited by local accessibility conditions. Satellite earth observation (EO) techniques can be used to measure spatially distributed hydrometric variables, reducing the time and cost of traditional surveys. Satellite EO provides high temporal and spatial frequency, but it can only measure large rivers (wider than ca. 50 m) and only provides water surface elevation (WSE), water surface slope (WSS), and surface water width data. UAS hydrometry can provide WSE, WSS, water surface velocity and riverbed geometry at a high spatial resolution, making it suitable for rivers of all sizes. The use of UAS hydrometry can enhance river management, with cost-effective surveys offering large coverage and high-resolution data, which are fundamental in flood risk assessment, especially in areas that difficult to access. In this study, we proposed a combination of UAS hydrometry techniques to fully characterize the hydraulic parameters of a river. The land elevation adjacent to the river channel was measured with LiDAR, the riverbed elevation was measured with a sonar payload, and the WSE was measured with a UAS radar altimetry payload. The survey provided 57 river cross-sections with riverbed elevation, and 8 km of WSE and land elevation and took around 2 days of survey work in the field. Simulated WSE values were compared to radar altimetry observations to fit hydraulic roughness, which cannot be directly observed. The riverbed elevation cross-sections have an average error of 32 cm relative to RTK GNSS ground-truth measurements. This error was a consequence of the dense vegetation on land that prevents the LiDAR signal from reaching the ground and underwater vegetation, which has an impact on the quality of the sonar measurements and could be mitigated by performing surveys during winter, when submerged vegetation is less prevalent. Despite the error of the riverbed elevation cross-sections, the hydraulic model gave good estimates of the WSE, with an RMSE below 3 cm. The estimated roughness is also in good agreement with the values measured at a gauging station, with a Gauckler–Manning–Strickler coefficient of M = 16–17 m^1/3/s. Hydraulic modeling results demonstrate that both bathymetry and roughness measurements are necessary to obtain a unique and robust hydraulic characterization of the river. Full article

Water is a critical resource globally, covering approximately 71% of the Earth’s surface. Employing analytical models to forecast water quality parameters based on historical data is a key strategy in the field of water quality monitoring and treatment. By using a forecasting model, potential changes in water quality can be understood over time. In this study, the gated recurrent unit (GRU) neural network was utilized to forecast dissolved oxygen levels following variational mode decomposition (VMD). The GRU neural network’s parameters were optimized using the grey wolf optimizer (GWO), leading to the development of a VMD–GWO–GRU model for forecasting water quality parameters. The results indicate that this model outperforms both the standalone GRU model and the GWO–GRU model in capturing key information related to water quality parameters. Additionally, it shows improved accuracy in forecasting medium to long-term water quality changes, resulting in reduced root mean square error (RMSE) and mean absolute percentage error (MAPE). The model demonstrates a significant improvement in the lag of forecasting water quality parameters, ultimately boosting forecasting accuracy. This approach can be applied effectively in both monitoring and forecasting water quality parameters, serving as a solid foundation for future water quality treatment strategies. Full article

The purpose of this study is to propose an appropriate model to predict chemical composition during water purification at the Regional Water Company (PDAM) Surabaya, in order to achieve proper drinking water standards. Drinking water treatment is very expensive, so the model serves as a basis for determining the composition of chemicals used in the water purification process at PDAM Surabaya. This study examines a model of the relationship between the level of clarity of drinking water and the composition of the chemicals used. The government can obtain important benefits from the forecasting model to formulate policies for the company. One of the objectives of developing the estimation method involved in this research is to efficiently determine the exact chemical composition resulting from the water purification process, which will inform the financing and control of water quality. We used a multivariable linear approach for some parametric components, a multivariable Fourier Series approach for some nonparametric components, and a multivariable Kernel approach for semiparametric regression. Using the penalized least square (PLS) approach, a mixed estimator of the Fourier and Kernel Series was obtained with semiparametric regression. The smoothing parameters were selected using a common cross-validation technique (GCV). The performance of this technique was evaluated using the Gaussian Kernel and Fourier Series with data trends in the drinking water clarity level obtained from PDAM Surabaya. The findings showed that this technique performed well, so we recommend that the government conduct an in-depth analysis to determine correct chemical composition so that the cost of water treatment can be minimized. Full article

In a context of accelerating deployment of distributed generation in power distribution grid, this work proposes an answer to an important and urgent need for better management tools in order to ‘intelligently’ operate these grids and maintain quality of service. To this aim, a model-based predictive control (MPC) strategy is proposed, allowing efficient re-routing of power flows using flexible assets, while respecting operational constraints as well as the voltage constraints prescribed by ENEDIS, the French distribution grid operator. The flexible assets used in the case study—a low-voltage power distribution grid in southern France—are a biogas plant and a water tower. Non-parametric machine-learning-based models, i.e., Gaussian process regression (GPR) models, are developed for intraday forecasting of global horizontal irradiance (GHI), grid load, and water demand, to better anticipate emerging constraints. The forecasts’ quality decreases as the forecast horizon grows longer, but quickly stabilizes around a constant error value. Then, the impact of forecasting errors on the performance of the control strategy is evaluated, revealing a resilient behaviour where little degradation is observed in terms of performance and computation cost. To enhance the strategy’s resilience and minimise voltage overflow, a worst-case scenario approach is proposed for the next time step and its contribution is examined. This is the main contribution of the paper. The purpose of the min–max problem added upstream of the main optimisation problem is to both anticipate and minimise the voltage overshooting resulting from forecasting errors. In this min–max problem, the feasible space defined by the confidence intervals of the forecasts is searched, in order to determine the worst-case scenario in terms of constraint violation, over the next time step. Then, such information is incorporated into the decision-making process of the main optimisation problem. Results show that these incidents are indeed reduced thanks to the min–max problem, both in terms of frequency of their occurrence and the total surface area of overshooting. Full article

Meteorological forcing is the primary driving force and primary source of errors for storm surge forecasting. The objective of this study was to learn how forecasted meteorological forcing influences storm surge generation and propagation during a hurricane so that storm surge models can be reliably used to forecast actual events. Hindcasts and forecasts of Hurricane Rita (2005) storm surge was used as a case study. Meteorological forcing or surface wind/pressure fields for Hurricane Rita were generated using both the Weather Research and Forecasting (WRF) full-scale forecasting model along with archived hurricane advisories ingested into a sophisticated parametric wind model, namely Generalized Asymmetric Holland Model (GAHM). These wind fields were used to forecast Rita storm surges. Observation based wind fields from the OceanWeather Inc. (OWI) Interactive Objective Kinematic Analysis (IOKA) model, and Best track wind data ingested into the GAHM model were used to generate wind fields for comparison purposes. These wind fields were all used to hindcast Rita storm surges with the ADvanced CIRCulation (ADCIRC) model coupled with the Simulating Waves Nearshore (SWAN) model in a tightly coupled storm surge-wave model referred to as ADCIRC+SWAN. The surge results were compared against a quality-controlled database of observed data to assess the performance of these wind fields on storm surge generation and propagation. The surge hindcast produced by the OWI wind field performed the best, although some high water mark (HWM) locations were overpredicted. Although somewhat underpredicted, the WRF wind fields forecasted wider surge extent and wetted most HWM locations. The hindcast using the Best track parameters in the GAHM and the forecast using forecast/advisories from the National Hurricane Center (NHC) in the GAHM produced strong and narrow wind fields causing localized high surges, which resulted in overprediction near landfall while many HWM locations away from wind bands remained dry. Full article