Predicting water demand: a review of the methods employed and future possibilities

2019 ◽  
Vol 19 (8) ◽  
pp. 2179-2198 ◽  
Author(s):  
Gustavo de Souza Groppo ◽  
Marcelo Azevedo Costa ◽  
Marcelo Libânio
Keyword(s):  
Artificial Intelligence ◽  
Water Supply ◽  
Water Demand ◽  
Supply And Demand ◽  
Demand Forecasting ◽  
System Management ◽  
Water Demand Forecasting ◽  
Combination Of Methods ◽  
Applications Of Machine Learning

Abstract The balance between water supply and demand requires efficient water supply system management techniques. This balance is achieved through operational actions, many of which require the application of forecasting concepts and tools. In this article, recent research on urban water demand forecasting employing artificial intelligence is reviewed, aiming to present the ‘state of the art’ on the subject and provide some guidance regarding methods and models to research and professional sanitation companies. The review covers the models developed using standard statistical techniques, such as linear regression or time-series analysis, or techniques based on Soft Computing. This review shows that the studies are, mostly, focused on the management of the operating systems. There is, therefore, room for long-term forecasts. It is worth noting that there is no global model that surpasses all the methods for all cases, it being necessary to study each region separately, evaluating the strengths of each model or the combination of methods. The use of statistical applications of Machine Learning and Artificial Intelligence methodologies has grown considerably in recent years. However, there is still room for improvement with regard to water demand forecasting.

scholarly journals Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Wong ◽  
Z. Q. Lin ◽  
L. Wang ◽  
A. G. Chung ◽  
B. Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Lung Disease ◽  
Disease Severity ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
X Rays ◽  
Computer Aided ◽  
Monte Carlo Cross Validation ◽  
Validation Experiments

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

scholarly journals Variable selection methods for water demand forecasting in Ethiopia: Case study Gondar town

2018 ◽  
Vol 4 (1) ◽  
pp. 1537067 ◽  
Author(s):  
Mohammed Gedefaw ◽  
Wang Hao ◽  
Yan Denghua ◽  
Abel Girma ◽  
Mustafa Ibrahim Khamis
Keyword(s):  
Variable Selection ◽  
Water Demand ◽  
Demand Forecasting ◽  
Selection Methods ◽  
Water Demand Forecasting ◽  
Gondar Town
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