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Predictive models for forecasting hourly urban water demand
Manuel Herrera,
Luís Torgo
, Joaquín Izquierdo, Rafael Pérez-García
科研成果
:
期刊稿件
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文章
›
同行评审
360
引用 （Scopus）
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探究 'Predictive models for forecasting hourly urban water demand' 的科研主题。它们共同构成独一无二的指纹。
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Keyphrases
Predictive Models
100%
Urban Water Demand
100%
Urban Areas
66%
Time Series Data
66%
Water Consumption
66%
Water Supply Management
66%
Southeastern Spain
66%
Water Demand
33%
Random Forest
33%
Hydraulics
33%
Medium Size
33%
Artificial Neural Network
33%
Management Decisions
33%
Least Squares Support Vector Regression (LSSVR)
33%
Water Distribution Systems
33%
Water Network
33%
Operational Management
33%
Demand Profile
33%
Projection Pursuit Regression
33%
Experimental Methodology
33%
Multivariate Adaptive Regression Splines
33%
Nonlinear Time Series
33%
Network Projection
33%
Clean Water
33%
Future Water Demand
33%
Mathematics
Predictive Model
100%
Time Series Data
100%
Support Vector Machine
33%
Artificial Neural Network
33%
Nonlinear
33%
Simple Model
33%
Exploratory Data Analysis
33%
Linear Time Series
33%
Multivariate Adaptive Regression Spline
33%
Engineering
Data Series
100%
Linear Time
33%
Random Forest
33%
Clean Water
33%
Simple Model
33%
Hydraulics
33%
Artificial Neural Network
33%
Medium Size
33%
Demand Profile
33%
Key Importance
33%
Agricultural and Biological Sciences
Water Supply
100%
Water Consumption
66%
Neural Network
33%
Support Vector Machine
33%
Economics, Econometrics and Finance
Time Series
100%
Metropolitan Area
66%
Exploratory Data Analysis
33%
Chemical Engineering
Water Supply
100%
Neural Network
33%