scholarly journals A hybrid model based on stacking and multi-correction mechanisms for urban water demand prediction

2020 ◽  
Vol 53 (2) ◽  
pp. 16685-16690
Author(s):  
Yang Lan ◽  
Jingcheng Wang ◽  
Miaoshun Bai ◽  
Ibrahim Brahmia ◽  
Haotian Xu ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Water Demand ◽  
Urban Water ◽  
Demand Prediction ◽  
Urban Water Demand ◽  
Model Based ◽  
Water Demand Prediction

scholarly journals Short-Term Urban Water Demand Prediction Considering Weather Factors

2018 ◽  
Vol 32 (14) ◽  
pp. 4527-4542 ◽  
Author(s):  
Salah L. Zubaidi ◽  
Sadik K. Gharghan ◽  
Jayne Dooley ◽  
Rafid M. Alkhaddar ◽  
Mawada Abdellatif
Keyword(s):  
Water Demand ◽  
Urban Water ◽  
Short Term ◽  
Weather Factors ◽  
Demand Prediction ◽  
Urban Water Demand ◽  
Water Demand Prediction

scholarly journals Water demand forecasting using extreme learning machines

2016 ◽  
Vol 28 (1) ◽  
pp. 37-52 ◽  
Author(s):  
Mukesh Tiwari ◽  
Jan Adamowski ◽  
Kazimierz Adamowski
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Demand Model ◽  
Demand Prediction ◽  
Learning Machines ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Learning Machine ◽  
Similar Accuracy

AbstractThe capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELMW, ELMB), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANNW, ANNB). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELMBand ANNBmodels provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANNWand ELMWmodels provided greater accuracy, with the ELMWmodel outperforming the ANNWmodel. Significant improvement in peak urban water demand prediction was only achieved with the ELMWmodel. The superiority of the ELMWmodel over both the ANNWor ANNBmodels demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

Application of Urban Water Demand Prediction Model by Using Particle Swarm Algorithm Based on Simulated Annealing

2012 ◽  
Vol 155-156 ◽  
pp. 102-106
Author(s):  
Bo Sun ◽  
Jian Cang Xie ◽  
Ni Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Water Demand ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Urban Water ◽  
Swarm Optimization ◽  
Demand Prediction ◽  
Water Demand Prediction

Water demand prediction is a complicated multifactor, multi-level non-linear system influenced by the urban population, industrial and economic level. The results of the prediction accuracy have a greater uncertainty and ambiguity. As a new cluster intelligent evolutionary algorithms, particle swarm optimization (PSO) is easy to understand, easy to implement ,and it is very suitable for non-linear model parameters fitting problems. At the same time, we will introduce the simulated annealing mechanism into particle swarm optimization algorithm, constructed the optimization algorithm of simulated annealing particle swarm (SA-PSO). In the paper, the optimization algorithm of simulated annealing particle swarm (SA-PSO) is applied to the field of water demand prediction. Example show that compared with the particle swarm algorithm, simulated annealing particle swarm optimization achieves a high prediction accuracy for urban water demand prediction, and it is strong applicability in the water demand forecast.

Research on Urban Water Demand Prediction

2012 ◽  
Vol 594-597 ◽  
pp. 2037-2040
Author(s):  
Kun Yang Wan
Keyword(s):  
Prediction Model ◽  
Bp Neural Network ◽  
Water Demand ◽  
Prediction Method ◽  
Urban Water ◽  
Sequence Prediction ◽  
Demand Prediction ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Water Demand Prediction

Water demand prediction adopts combined prediction method based on BP neural network prediction model, grey G (1,1) prediction model, time sequence prediction model (second multinomial exponential smoothing model) and single linear regression model (Cubics Ratio model). Empirical results show that combined prediction method makes comprehensive use of information of every separate prediction model, and thus enhances prediction accuracy.

The Application of System Dynamics Model of City Water Demand Forecasting

2014 ◽  
Vol 535 ◽  
pp. 440-445
Author(s):  
Sheng Na Yang ◽  
Hua Guo ◽  
Yan Li ◽  
Jun Liang Liu
Keyword(s):  
System Dynamics ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Demand Prediction ◽  
The Future ◽  
Water Demand Prediction ◽  
City Water

With the continuous development of city, urban water consumption is continuously increasing. But the earth's fresh water resources are limited. If you want to meet the urban development increases the amount of water, you must forecast for urban water demand in the future, water demand prediction is also should be formed. This research mainly adopts the method of system dynamics to predict the water demand of city over the next decade. Forecast of water demand to get accurate effective demand in the future by establishing a system dynamic model, which accuracy should tested and verified.

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