RESUMEN
Water quality extremes, which water quality models often struggle to predict, are a grave concern to water supply facilities. Most existing water quality models use mean error functions to maximize the predictability of water quality mean value. This paper describes a composite quantile regression neural network (CQRNN) model, which simultaneously estimates non-crossing regression quantiles by minimizing the composite quantile regression error function. This method can improve the prediction of extremes. This paper evaluates the performance of CQRNN for predicting extreme values of turbidity and total organic carbon (TOC) and compares with quantile regression (QR), linear regression (LR), and k-nearest neighbors (KNN) in an application to the Hetch Hetchy Regional Water System, which is the primary water supply for San Francisco, CA. CQRNN is superior to QR, LR, and KNN for predicting the mean trend and extremes of turbidity and TOC, especially for the non-Gaussian turbidity data. The performance of CQRNN is the most stable relative to other methods over different training sample sizes.
Asunto(s)
Monitoreo del Ambiente , Calidad del Agua , Redes Neurales de la Computación , Modelos Lineales , Abastecimiento de AguaRESUMEN
Acoustic Emission (AE) is revealed to be highly adapted to monitor materials and structures in materials research and for site monitoring. AE-features can be either analyzed by means of physical considerations (geophysics/seismology) or through their time/frequency waveform characteristics. However, the multitude of definitions related to the different parameters as well as the processing methods makes it necessary to develop a comparative analysis in the case of a heterogeneous material such as civil engineering concrete. This paper aimed to study the micro-cracking behavior of steel fiber-reinforced reinforced concrete T-beams subjected to mechanical tests. For this purpose, four-points bending tests, carried out at different displacement velocities, were performed in the presence of an acoustic emission sensors network. Besides, a comparison between the sensitivity to damage of three definitions corresponding to the b-value parameter was performed and completed by the evolution of the RA-value and average frequency (AF) as a function of loading time. This work also discussed the use of the support-vector machine (SVM) approach to define different damage zones in the load-displacement curve. This work shows the limits of this approach and proposes the use of an unsupervised learning approach to cluster AE data according to physical and time/frequency parameters. The paper ends with a conclusion on the advantages and limitations of the different methods and parameters used in connection with the micro/macro tensile and shear mechanisms involved in concrete cracking for the purpose of in situ monitoring of concrete structures.
RESUMEN
Variable energy sources such as solar and runoff sources are intermittent in time and space, following their driving hydro-meteorological processes. Recent research has shown that in mountainous areas the combination of solar and hydropower has large potential (termed complementarity) to cover the temporal variability of the energy load and, by this mean, to facilitate integration of renewables into the electricity network. Climate change is causing widespread glacier retreat, and much attention is devoted to negative impacts such as diminishing water resources and shifts in runoff seasonality. However, the effects of glacier shrinkage on complementarity between hydropower and solar energy sources have been disregarded so far. This research aims at filling this gap. Data from the Eastern Italian Alps are used for the analysis. The Decision Scaling approach is used to analyze the electric energy system sensitivity and vulnerability to change in precipitation, temperature and glacier coverage. With this method, the electric energy system is first subject to a scenario-independent climate stress test, while projections from Regional Climate Models (RCMs) are then used to infer the likelihood of the future climate states and subsequently changes in complementarity of energy production. Results show that glacier shrinkage and increasing temperatures induced by climate change lead to a marked shift of seasonal hydropower production. As a consequence, the complementarity between hydropower and solar photovoltaic increases in a marked way in the basin with the largest original glacier coverage. Changes in complementarity are less significant in larger basins characterized by less glacier contribution.