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Hydrological data provide valuable information for the decision-making process in water resources management, where long and complete time series are always desired. However, it is common to deal with missing data when working on streamflow time series. Rainfall-streamflow modeling is an alternative to overcome such a difficulty. In this paper, self-organizing maps (SOM) were developed to simulate monthly inflows to a reservoir based on satellite-estimated gridded precipitation time series. Three different calibration datasets from Três Marias Reservoir, composed of inflows (targets) and 91 TRMM-estimated rainfall data (inputs), from 1998 to 2019, were used. The results showed that the inflow data homogeneity pattern influenced the rainfall-streamflow modeling. The models generally showed superior performance during the calibration phase, whereas the outcomes varied depending on the data homogeneity pattern and the chosen SOM structure in the testing phase. Regardless of the input data homogeneity, the SOM networks showed excellent results for the rainfall-runoff modeling, presenting Nash–Sutcliffe coefficients greater than 0.90.
Graphical Abstract 相似文献Sustainable management of water supply systems is a major challenge within the framework of the water-energy nexus. The main strategies to improve the operation of these systems are related to increasing the hydraulic and energy efficiency of pumping systems. In this context, this work presents a new artificial neural network (ANN) controller to improve the operation of water distribution systems (WDSs) that includes in its algorithm the specific energy consumption (SEC) as a decision parameter. Therefore, pressure control at the measuring points is also based on the energy efficiency of the pumps. The technique was applied to control the pressures in an experimental setup that emulates a WDS with two consumption zones with different topographies. For this purpose, the controller acted on a conventional pump, a booster pump and a control valve. To analyze the performance under the controller action, tests were performed emulating water-demand scenarios, introducing perturbations and changing the pressure setpoints. The real-time control performance was proven based on the dynamic performance, steady-state performance and SEC. The experimental results showed that the proposed controller kept the pressures close to the setpoints and provided a reduction in the SEC between 15.1% and 17.8%, compared with the uncontrolled system, and an economy that varied from 2.5% to 8.1% compared with the performance of the ANN based only on pressure control.
相似文献This research aims to illustrate the potential use of concepts, techniques, and mining process tools to improve the systematic review process. Thus, a review was performed on two online databases (Scopus and ISI Web of Science) from 2012 to 2019. A total of 9649 studies were identified, which were analyzed using probabilistic topic modeling procedures within a machine learning approach. The Latent Dirichlet Allocation method, chosen for modeling, required the following stages: 1) data cleansing, and 2) data modeling into topics for coherence and perplexity analysis. All research was conducted according to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses in a fully computerized way. The computational literature review is an integral part of a broader literature review process. The results presented met three criteria: (1) literature review for a research area, (2) analysis and classification of journals, and (3) analysis and classification of academic and individual research teams. The contribution of the article is to demonstrate how the publication network is formed in this particular field of research, and how the content of abstracts can be automatically analyzed to provide a set of research topics for quick understanding and application in future projects.
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