An ensemble wavelet bootstrap machine learning approach to water demand forecasting: a case study in the city of Calgary, Canada

2015 ◽  
Vol 14 (2) ◽  
pp. 185-201 ◽  
Author(s):  
Mukesh K. Tiwari ◽  
Jan F. Adamowski
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Learning Approach ◽  
Water Demand Forecasting ◽  
Machine Learning Approach ◽  
The City

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

A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model

2019 ◽  
Vol 1 (1) ◽  
pp. 32-44
Author(s):  
Joseph Simonian ◽  
Chenwei Wu ◽  
Daniel Itano ◽  
Vyshaal Narayanam
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study

2019 ◽  
Vol 1284 ◽  
pp. 012004 ◽  
Author(s):  
Leandro L Lorente-Leyva ◽  
Jairo F Pavón-Valencia ◽  
Yakcleem Montero-Santos ◽  
Israel D Herrera-Granda ◽  
Erick P Herrera-Granda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Artificial Neural

scholarly journals Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona Case Study.

2014 ◽  
Vol 47 (3) ◽  
pp. 10457-10462 ◽  
Author(s):  
Ajay Kumar Sampathirao ◽  
Juan Manuel Grosso ◽  
Pantelis Sopasakis ◽  
Carlos Ocampo-Martinez ◽  
Alberto Bemporad ◽  
...  
Keyword(s):  
Drinking Water ◽  
Water Demand ◽  
Large Scale ◽  
Demand Forecasting ◽  
Optimal Operation ◽  
Water Networks ◽  
Water Demand Forecasting

scholarly journals Framework for propagating stress control message using heartbeat based IoT remote monitoring analytics

2020 ◽  
Vol 10 (5) ◽  
pp. 4615
Author(s):  
Eisha Akanksha
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Cost Effective ◽  
Learning Approach ◽  
Contextual Data ◽  
Aggregated Data ◽  
Machine Learning Approach ◽  
Close Relationship ◽  
Control Message

Abnormal level of stress is the root indicator factor to have significant impact over the health of heart and there is a close relationship between the stress levels with heart rate. Review of the existing literature showcase that there has been various work that has been carried out towards investigation of considering heart rate with an internet-of-things (IoT) system. Apart from this, existing system doesnt offer any instantaneous solution where certain intimation is offered in real-time to the user with wearables as a solution to control the stress condition. Therefore, the current paper introduces a novel framework where the sampled heart rates of the patients are captured by IoT deivices. The aggregated data are further forwarded to the cloud analytic system that uses correlation to extract the appropriate message. The system after being applied with teh machine learning approach could further extract the elite outcome followed by forwarding the contextual data to teh user. Using an analytical modelliig, the proposed system shows that it offers better accuracy and reduced processing time when compared with other machine learning approach and thereby it proves to be cost effective solution in IoT system over medical case study.

Sign in / Sign up

Export Citation Format

Share Document