A Novel Dual-Scale Deep Belief Network Method for Daily Urban Water Demand Forecasting

Abstract

Water demand forecasting applies data supports for the scheduling and decision-making of urban water supply systems. In this study, a new dual-scale deep belief network (DSDBN) approach for daily urban water demand forecasting was proposed. Original daily water demand time series was decomposed into several intrinsic mode functions (IMFs) and one residue component with ensemble empirical mode decomposition (EEMD) technique. Stochastic and deterministic terms were reconstructed through analyzing the frequency characteristics of IMFs and residue using generalized Fourier transform. The deep belief network (DBN) model was used for prediction using the two feature terms. The outputs of the double DBNs are summed as the final forecasting results. Historical daily water demand datasets from an urban waterworks in Zhuzhou, China, were investigated by the proposed DSDBN model. The mean absolute percentage error (MAPE), normalized root-mean-square error (NRMSE), correlation coefficient (CC) and determination coefficient (DC) were used as evaluation criteria. The results were compared with the autoregressive integrated moving average (ARIMA) model, feed forward neural network (FFNN) model, support vector regression (SVR) model, EEMD and their combinations, and single DBN model. The results obtained in the test period indicate that the proposed model has the smallest MAPE and NRMSE values of 1.291099 and 0.016625, respectively, and the largest CC and DC values of 0.976528 and 0.953512, respectively. Therefore, the proposed DSDBN method is a useful tool for daily urban water demand forecasting and outperforms other models in common use.

Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors

“…Recent methods make use of neural networks (NN) or other supervised machine learning methods, or hybrid methods that combine NN with univariate/regression forecasting models (Babel and Shinde 2011;Bai et al 2014;Xu et al 2018;Pacchin et al 2019). Similar to exogenous ARMA models, these methods are capable of incorporating exogenous data and boast reliable forecasts, but require extensive historical data for training and are accompanied by large forecast uncertainties, which cannot always be quantified (Hutton and Kapelan 2015b;Anele et al 2017).…”

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

“…In recent years, the data-driven models, such as regression (Vonk et al 2019;Pesantez et al 2020), artificial neural network (ANN) (Peng et al 2020;Zubaidi et al 2020;Salloom et al 2021), time series (Xu et al 2018;Tripathi et al 2019;Smolak et al 2020) and deep learning (Guo et al 2018;Nasser et al 2020;Du et al 2021) models, have been widely used in water demand prediction. This is because the data-driven models will not be affected by the external physical environment.…”

Nonlinear Dynamic Modeling of Urban Water Consumption Using Chaotic Approach (Case Study: City of Kelowna)

“…to ascertain the relationship between the input and output variables. The physical-based and black box models include conventional regression models [9], artificial neural networks (ANN) [14,20,30,31], feedforward neural networks (FNN) [22,32], general regression neural networks (GRNNs) [33,34], deep belief neural network (DBNN) [35], support vector machines (SVMs) [16,18,[36][37][38], gene expression programming (GEP) [39,40], adaptive neural fuzzy inference system (ANFIS) [41], Fourier analysis [7], hybrid models (e.g., combined wavelet) [23,42,43], fuzzy regression [44], fuzzy cognitive map learning method [45], epidemiology-based forecasting framework [46], temporal disaggregation [47], harmonic analysis [48], and wavelet de-noising [49].…”