Water demand forecasting: review of soft computing methods

Abstract

Demand forecasting plays a vital role in resource management for governments and private companies. Considering the scarcity of water and its inherent constraints, demand management and forecasting in this domain are critically important. Several soft computing techniques have been developed over the last few decades for water demand forecasting. This study focuses on soft computing methods of water consumption forecasting published between 2005 and 2015. These methods include artificial neural networks (ANNs), fuzzy and neuro-fuzzy models, support vector machines, metaheuristics, and system dynamics. Furthermore, it was discussed that while in short-term forecasting, ANNs have been superior in many cases, but it is still very difficult to pick a single method as the overall best. According to the literature, various methods and their hybrids are applied to water demand forecasting. However, it seems soft computing has a lot more to contribute to water demand forecasting. These contribution areas include, but are not limited, to various ANN architectures, unsupervised methods, deep learning, various metaheuristics, and ensemble methods. Moreover, it is found that soft computing methods are mainly used for short-term demand forecasting.

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

“…Recently, data-driven models such as artificial neural networks (ANN), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS) and genetic programming (GP), and time series methods such as autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) have been proved efficient in forecasting hydrologic time series (e.g., groundwater level, water demand and inflow) [5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Yoon et al [5] developed ANN and SVM models to forecast groundwater level fluctuations in a coastal aquifer.…”

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

“…This importance motivated the application of soft-computing techniques into urban water consumption forecasting methods to develop methods that are applicable in forecasting problems. Many techniques have been proposed to forecast water consumption under differing time scales; however, there has been limited investigation on performance comparisons between models to inform model selection under various conditions [5]. Moreover, non-stationary, non-linear, and inherent stochasticity of water consumption data makes forecasting problems more challenging in this field [83].…”

“…The reliability of water distribution systems may be improved through the accurate simulation of hydraulic conditions in pipeline systems based on future water consumption forecasting. In other words, water consumption forecasting provides public suppliers with the necessary future consumption information to ensure consumption needs can be met [4,5]. Water consumption forecasting is a dynamic process as predictions are essential for the optimum operation and sustainable growth and development of urban water supply [6].…”

Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

“…The objectives of this study are four-fold: (1) to investigate chaotic behavior of case data and finding the proper lag time; (2) to find the accuracy of the forecasting for one-day ahead lead time with various input combination, and (3) to study if phase space reconstruction (PSR) based on optimum embedding dimension would improve the accuracy of the models, and 4) application of wavelet decomposition by five different transform functions combined with all the mentioned models with and without PSR.…”

“…For using different standards to simulate hydraulic constitutions in pipeline systems (to improve the reliability of the system), it is necessary to have an accurate simulation of consumption value in a specific period. In other words, "The purpose of water demand forecast is to demonstrate futuristic information available for public water suppliers as they conduct their business" [1,2]. Short-term (e.g., less than a week), mid-term (e.g., weekly to monthly) and long-term (e.g., greater than monthly) period forecast demand values are critical for daily operations and future management of the system.…”