scholarly journals Optimized Deep Learning Framework for Water Distribution Data-Driven Modeling

2017 ◽  
Vol 186 ◽  
pp. 261-268 ◽  
Author(s):  
Zheng Yi Wu ◽  
Atiqur Rahman
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 137656-137667 ◽  
Author(s):  
Bilal Hussain ◽  
Qinghe Du ◽  
Sihai Zhang ◽  
Ali Imran ◽  
Muhammad Ali Imran

2009 ◽  
Vol 11 (1) ◽  
pp. 1-17 ◽  
Author(s):  
M. Tabesh ◽  
J. Soltani ◽  
R. Farmani ◽  
D. Savic

In this paper two models are presented based on Data-Driven Modeling (DDM) techniques (Artificial Neural Network and neuro-fuzzy systems) for more comprehensive and more accurate prediction of the pipe failure rate and an improved assessment of the reliability of pipes. Furthermore, a multivariate regression approach has been developed to enable comparison with the DDM-based methods. Unlike the existing simple regression models for prediction of pipe failure rates in which only few factors of diameter, age and length of pipes are considered, in this paper other parameters such as pressure and pipe depth, are also included. Furthermore, an investigation is carried out on most commonly used mechanical reliability relationships and the results of incorporation of the proposed pipe failure models in the reliability index are compared. The proposed models are applied to a real case study involving a large water distribution network in Iran and the results of model predictions are compared with measured pipe failure data. Compared with the results of neuro-fuzzy and multivariate regression models, the outcomes of the artificial neural network model are more realistic and accurate in the prediction of pipe failure rates and evaluation of mechanical reliability in water distribution networks.


Author(s):  
Alexander Rodríguez ◽  
Anika Tabassum ◽  
Jiaming Cui ◽  
Jiajia Xie ◽  
Javen Ho ◽  
...  

AbstractHow do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DeepCovid, an operational deep learning framework designed for real-time COVID-19 forecasting. Deep-Covid works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The deployed framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.


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